Unmanned operation equipment control method, system and device applied to a farm

Abstract

The application provides a kind of unmanned operation equipment control method, system and equipment applied to farm, which comprises the following steps: determining a target navigation module according to the current environmental information of unmanned operation equipment, the current state and priority level of each sensor in multi-source navigation system; the multi-source navigation system comprises differential positioning sensor, geomagnetic sensor, ultrasonic sensor and inertial unit sensor; determining the current work area of unmanned operation equipment according to the current environmental information, and obtaining a target obstacle avoidance component; determining the target running mode of unmanned operation equipment according to the obstacle perception information collected by the target obstacle avoidance component in real time and the current work area; and controlling the unmanned operation equipment to perform a moving operation action in the farm according to the target navigation module and the target running mode. The application realizes dynamic and adaptive obstacle avoidance and navigation for unmanned operation equipment in different working environments, thereby improving the running continuity and operation efficiency of unmanned operation equipment.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving control technology, and in particular to a control method, system and equipment for unmanned operation equipment applied to a farm. Background Technology

[0002] With the advancement of smart ranch construction, utilizing unmanned operating equipment (UAVs) to perform high-frequency operations such as feed delivery and bulk material clearing has become a key means to improve ranch operational efficiency. The complex dynamic conditions of ranches place more stringent demands on the autonomous movement stability and safe passage capabilities of UAVs in multi-dimensional scenarios, aiming to reduce labor costs through efficient and stable automated operations and promote the development of precision livestock management.

[0003] Currently, the main control method adopted is based on a single navigation source combined with fixed obstacle avoidance logic. The specific implementation plan is usually as follows: satellite positioning technology is used for long-distance tracking, and sensors are used to detect the physical distance of obstacles; once an obstacle is detected within a preset distance, the unmanned operating equipment is controlled to stop, and the operation resumes after the obstacle leaves the detection range.

[0004] However, under the complex dynamic conditions of the ranch, this method has limited environmental adaptation boundaries between different work areas, making it difficult to take into account the passage logic under different work conditions. It is prone to poor navigation stability and high degree of obstacle avoidance redundancy. The work process is frequently disturbed in the dynamic environment and causes unexpected stagnation. As a result, the control accuracy and stability of the unmanned operation equipment are poor, which affects the operation continuity of the unmanned operation equipment and the efficiency of the ranch's automated operation. Summary of the Invention

[0005] This invention provides a control method, system, and equipment for unmanned operating equipment applied to farms, which solves the defects of poor control accuracy and stability of unmanned operating equipment in the prior art, and improves the control accuracy and stability of unmanned operating equipment under complex dynamic working conditions in farms, thereby improving the operation continuity and operational efficiency of unmanned operating equipment.

[0006] This invention provides a control method for unmanned operating equipment applied in a farm, comprising: Based on the current environmental information of the unmanned operating equipment, the current status and priority level of each sensor in the multi-source navigation system, the target navigation module is determined in the multi-source navigation system; the multi-source navigation system includes differential positioning sensors, geomagnetic sensors, ultrasonic sensors and inertial unit sensors; The current operating area of ​​the unmanned equipment is determined based on the current environmental information, and a target obstacle avoidance component matching the current operating area is obtained. Based on the obstacle perception information collected in real time by the target obstacle avoidance component and the current working area, the target operating mode of the unmanned operation equipment is determined; the target operating mode includes deceleration mode, stopping and avoiding mode, detour and avoiding mode, turning around and moving mode, reversing and moving mode, or normal passage mode; Based on the target navigation module and the target operation mode, the unmanned operation equipment is controlled to perform mobile operation actions within the farm.

[0007] According to the present invention, a control method for unmanned operating equipment applied in a farm is provided, wherein the priority level of the geomagnetic sensor is higher than that of the ultrasonic sensor, and the priority level of the ultrasonic sensor is higher than that of the differential positioning sensor. The step of determining the target navigation module in the multi-source navigation system based on the current environmental information of the unmanned operating equipment, the current status and priority level of each sensor in the multi-source navigation system, includes: Based on the current environmental information, the system monitors changes in the current work area. When a change in the current work area is detected, the system controls the target navigation module to perform real-time switching between the differential positioning sensor, the geomagnetic sensor, the ultrasonic sensor, and the inertial unit sensor, based on the current state and the priority level. The switching operation includes: If the unmanned operating equipment is detected to have entered a transition area and the current state of the geomagnetic sensor is valid, the target navigation module will be switched to the geomagnetic sensor; wherein, the transition area is an intermediate connecting area with ground magnetic strips for the unmanned operating equipment to move from one area to another. If the unmanned equipment is detected to have entered the straight-line area inside the enclosure and the ultrasonic sensor is in an effective state, the target navigation module will be switched to the ultrasonic sensor. If the unmanned operating equipment is detected to have entered the reversing area within the enclosure and the inertial unit sensor is in an effective state, the target navigation module is switched to the inertial unit sensor. If the unmanned equipment is detected to have entered the area outside the enclosure and the differential positioning sensor is in an effective state, the target navigation module will be switched to the differential positioning sensor.

[0008] According to the present invention, a control method for unmanned operating equipment applied in a farm is provided, the method further comprising: If the unmanned equipment is detected to have entered the transition area and the current state of the geomagnetic sensor is in a failed state, the sensor with the highest priority among the other sensors whose current state is valid will be identified as the target navigation module; the other sensors are the sensors in the multi-source navigation system other than the geomagnetic sensor.

[0009] According to the present invention, a control method for unmanned operating equipment applied in a farm includes, wherein acquiring a target obstacle avoidance component matching the current operating area includes: If the current working area is determined to be a straight-line area within the enclosure, the first obstacle avoidance component is identified as the target obstacle avoidance component; the first obstacle avoidance component includes a visual sensor and a non-optical image sensing sensor; the non-optical image sensing sensor includes at least one of a lidar sensor, a collision sensor, and a photoelectric sensor; If the current working area is determined to be a transition area or a reversing area within the enclosure, the second obstacle avoidance component is identified as the target obstacle avoidance component; the second obstacle avoidance component includes at least one of the collision sensor, the photoelectric sensor, and the lidar sensor; If the current working area is determined to be outside the enclosure, the third obstacle avoidance component is identified as the target obstacle avoidance component; the third obstacle avoidance component includes the lidar sensor.

[0010] According to a control method for unmanned operating equipment applied in a farm provided by the present invention, the step of determining the target operating mode of the unmanned operating equipment based on obstacle perception information collected in real time by the target obstacle avoidance component and the current operating area includes: If the current working area is determined to be the straight-line area, the presence of dynamically moving obstacles within the first preset range of the unmanned operating equipment is determined based on the obstacle perception information collected in real time by the non-optical image perception sensor. In the presence of the dynamically moving obstacle, the target operating mode is determined as the parking avoidance mode; If, in the absence of the dynamically moving obstacle, the obstacle perception information collected in real time by the visual sensor indicates that a biometric target exists within the second preset range of the unmanned operating equipment, then the target operation mode is determined to be the deceleration passage mode; if the obstacle perception information indicates that the biometric target does not exist within the second preset range, then the target operation mode is determined to be the normal passage mode.

[0011] According to the present invention, a control method for unmanned operating equipment applied in a farm is provided, the method further comprising: When the current working area is determined to be the straight-ahead area, the visual sensor is used to collect image data of the road surface in front of the unmanned working equipment, and the image data is segmented to obtain ground area features; The tilt angle of the ground edge is detected based on the ground area features. When the tilt angle is detected to be greater than a preset angle threshold, it is determined that the unmanned operation equipment has entered the reversing area within the enclosure, and the vehicle mobility performance parameters of the unmanned operation equipment are obtained. If the vehicle mobility performance parameters indicate that the unmanned operating equipment has the conditions to complete the reversal within the reversal area, then the target navigation module is switched to the inertial unit sensor, and the target operating mode is determined to be the turning movement mode, so as to control the unmanned operating equipment to perform the turning action. If the vehicle mobility performance parameters indicate that the unmanned operating equipment does not have the conditions to complete a U-turn in the reversing area, the target operating mode is determined as the reversing movement mode, so as to control the unmanned operating equipment to perform the reversing action along the original route.

[0012] According to the present invention, a control method for unmanned operation equipment applied to a farm is provided. The ultrasonic sensor includes a first ultrasonic sensor, a second ultrasonic sensor and a third ultrasonic sensor. The first ultrasonic sensor, the second ultrasonic sensor and the third ultrasonic sensor are arranged sequentially at intervals along the travel direction of the unmanned operation equipment, and the second ultrasonic sensor is located between the first ultrasonic sensor and the third ultrasonic sensor. The step of controlling the unmanned operation equipment to perform mobile operation actions within the farm according to the target navigation module and the target operation mode includes: When the target navigation module is the ultrasonic sensor, the first distance data between the unmanned operating equipment and the limit bar measured by the first ultrasonic sensor, the third distance data between the unmanned operating equipment and the limit bar measured by the third ultrasonic sensor, and the second distance data between the unmanned operating equipment and the limit bar measured by the second ultrasonic sensor are respectively acquired. Calculate the average value between the first distance data and the third distance data; If the difference between the average value and the second distance data is less than a preset deviation value, the correction control amount of the movement offset angle of the unmanned operation equipment is determined based on the distance difference between the first distance data and the third distance data, and the preset reference distance data, and the correction control amount of the movement speed of the unmanned operation equipment is determined based on the distance difference between the second distance data and the preset reference distance data. The motor speed of the unmanned operating equipment is adjusted according to the correction control amount of the moving offset angle and the correction control amount of the moving speed. Based on the adjusted motor speed and the target operating mode, the unmanned operating equipment is controlled to perform mobile operations within the farm.

[0013] According to a control method for unmanned operating equipment applied in a farm provided by the present invention, the step of controlling the unmanned operating equipment to perform mobile operation actions within the farm based on the target navigation module and the target operating mode includes: When the target navigation module is the geomagnetic sensor, the position of the ground magnetic stripe is detected by the geomagnetic sensor; The yaw angle of the unmanned operating equipment relative to the ground magnetic strip is calculated based on the position of the ground magnetic strip; The motor speed of the unmanned operating equipment is adjusted according to the yaw angle; Based on the adjusted motor speed and the target operating mode, the unmanned operating equipment is controlled to perform moving operations along the ground magnetic strip.

[0014] According to a control method for unmanned operating equipment applied in a farm provided by the present invention, the step of controlling the unmanned operating equipment to perform mobile operation actions within the farm based on the target navigation module and the target operating mode includes: When the target navigation module is the differential positioning sensor, the target trajectory point to be tracked is determined from a pre-constructed navigation movement point set; the navigation movement point set contains multiple trajectory points arranged according to the operation sequence of the unmanned operation equipment. Compare the current positioning coordinates of the unmanned operating equipment with the coordinates of the target trajectory point; The driving direction of the unmanned operation equipment is adjusted based on the comparison results; Based on the adjusted driving direction and the target operating mode, the unmanned operation equipment is controlled to perform mobile operation actions at each trajectory point in the navigation movement point set.

[0015] The present invention also provides a control system for unmanned operation equipment applied in a farm, comprising: The first decision-making unit is used to determine the target navigation module in the multi-source navigation system based on the current environmental information of the unmanned operating equipment, the current status and priority level of each sensor in the multi-source navigation system; the multi-source navigation system includes differential positioning sensors, geomagnetic sensors, ultrasonic sensors and inertial unit sensors; The second decision-making unit is used to determine the current operating area of ​​the unmanned operation equipment based on the current environmental information, and to obtain a target obstacle avoidance component that matches the current operating area. The third decision-making unit is used to determine the target operation mode of the unmanned operation equipment based on the obstacle perception information collected in real time by the target obstacle avoidance component and the current operation area; the target operation mode includes deceleration mode, stopping and avoiding mode, detour and avoiding mode, turning around and moving mode, reversing and moving mode, or normal passage mode. The control unit is used to control the unmanned operation equipment to perform mobile operation actions within the farm according to the target navigation module and the target operation mode.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the unmanned operation equipment control method applied to a farm as described above.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the unmanned operation equipment control method applied to a farm as described above.

[0018] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the control method for unmanned operating equipment applied to a farm as described above.

[0019] The present invention provides a control method, system, and equipment for unmanned operation equipment applied to farms. By dynamically selecting the navigation source in a multi-source navigation system based on the current environmental characteristics, navigation module status, and priority of the unmanned operation equipment, and matching specific obstacle avoidance components and operating modes with the characteristics of the operating area, the unmanned operation equipment can dynamically and adaptively avoid obstacles and navigate for different operating environments. This solves the problems of single navigation source being susceptible to environmental interference and high redundancy of fixed obstacle avoidance logic, realizes stable movement and safe passage of unmanned operation equipment under complex dynamic working conditions, ensures the continuity of farm operations, and significantly improves the efficiency of automated operations. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is one of the flowcharts illustrating the control method for unmanned operating equipment applied to farms provided by the present invention.

[0022] Figure 2 This is a structural schematic diagram of the material pushing robot provided by the present invention.

[0023] Figure 3 This is a schematic diagram of the material spreading vehicle provided by the present invention.

[0024] Figure 4 This is a schematic diagram illustrating the functional implementation of different navigation modules in different scenarios within the unmanned operation equipment provided by this invention.

[0025] Figure 5 This is one of the schematic diagrams of the mobile operation layout of the breeding farm provided by the present invention.

[0026] Figure 6 This is a schematic diagram illustrating the functional implementation of different obstacle avoidance modules in different scenarios within the unmanned operation equipment provided by this invention.

[0027] Figure 7 This is the second flowchart of the control method for unmanned operating equipment applied to farms provided by the present invention.

[0028] Figure 8 This is a schematic diagram of the target detection results provided by the present invention.

[0029] Figure 9 This is the second schematic diagram of the mobile operation layout of the breeding farm provided by the present invention.

[0030] Figure 10 This is the third schematic diagram of the mobile operation layout of the breeding farm provided by the present invention.

[0031] Figure 11 This is a schematic diagram of the structure of the unmanned operation equipment control system for farms provided by the present invention.

[0032] Figure 12 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0034] In recent years, with the rapid development of the livestock industry, small-scale, free-range, and labor-intensive farming models have been gradually replaced by intelligent, intensive, and precision farming models. Driven by the development of artificial intelligence, semi-automatic and automated equipment combined with intelligent equipment based on computer information technology are increasingly entering ranches. Modern ranch operations encompass multiple stages, including grazing, feeding, harvesting, and cleaning, relying on the coordinated operation of various machines. Currently, ranches still primarily rely on manual driving, which suffers from low efficiency, high costs, and significant risks in harsh environments. There is an urgent need to develop unmanned technology, the core of which lies in achieving autonomous navigation and obstacle avoidance capabilities in complex dynamic scenarios.

[0035] However, the pasture environment features unstructured terrain, such as feeding passages and ramps, as well as dynamic obstacles, such as moving herds of livestock and groups of work vehicles. It also presents complex dynamic conditions, such as signal interference, such as GPS signal attenuation inside the livestock shed. These conditions place stringent requirements on the autonomous movement stability of unmanned equipment and its safe passage capability in multi-dimensional scenarios.

[0036] Currently, the industry mainly adopts a control method based on a single navigation source combined with fixed obstacle avoidance logic. For example, satellite positioning technology or real-time kinematic (RTK) technology is used for long-distance tracking, or ultrasonic navigation technology is used for parameter-following navigation, or geomagnetic navigation technology is used for geomagnetic signal tracking navigation, or inertial measurement units are used for navigation, and sensors are used to detect the physical distance of obstacles. Once an obstacle is detected, a stop action is executed.

[0037] This traditional method has significant limitations: First, the environmental adaptability of positioning and guidance is limited across different work areas. For example, while RTK or GPS-based sensor-based navigation technologies have a wide range of applications, they are prone to signal loss due to obstructions within livestock sheds. Geomagnetic navigation, although stable, requires construction and the magnetic strips are easily damaged. Ultrasonic navigation relies on specific reference markers. LiDAR mapping navigation is unsuitable for mapping and navigation in large open areas, resulting in excessive deviations. Inertial Measurement Unit (IMU) navigation is unsuitable for long-distance operations. Second, obstacle avoidance redundancy is high; a single obstacle-detection-stop logic cannot accommodate different traffic flow logics under various working conditions, easily leading to frequent interference and unexpected stoppages in dynamic environments. These shortcomings result in poor control accuracy and stability of unmanned equipment, severely impacting operational continuity and the efficiency of automated farm operations.

[0038] Figure 1 This is one of the flowcharts illustrating the control method for unmanned operating equipment applied in livestock farms provided by the present invention. For example... Figure 1As shown, in order to achieve unmanned and precise navigation and obstacle avoidance in ranches and improve breeding efficiency and economic benefits, this application provides a control method for unmanned operation equipment applied to ranches. This method integrates a multi-source navigation system that combines differential positioning sensors, geomagnetic sensors, ultrasonic sensors, and inertial unit sensors. It designs multi-segment autonomous navigation for the complex environment of ranches, and can switch navigation modes in real time according to changes in the work area. It also matches obstacle avoidance components in different areas to use the models in the obstacle avoidance components in different areas to predict obstacle movement and optimize navigation strategies, thereby realizing multi-segment, multi-machine universal navigation and obstacle avoidance technology for ranches.

[0039] It should be noted that the control method provided in this application embodiment can be applied to various mobile unmanned operation equipment, including but not limited to feeding equipment, receiving equipment, pushing equipment, inspection equipment or disinfection equipment in the breeding process. The execution subject of the method can be an unmanned operation equipment control system applied to the breeding farm, which is integrated into the unmanned operation equipment that needs to be dynamically controlled. The unmanned operation equipment control system can be the controller in the unmanned operation equipment.

[0040] Figure 2 This is a schematic diagram of the pusher robot provided by the present invention; Figure 3 This is a schematic diagram of the material spreading vehicle provided by the present invention.

[0041] like Figure 2 and Figure 3 As shown, the unmanned operation equipment of this application is equipped with a combined control device, which includes multiple modules, including but not limited to a collision sensor 1, a photoelectric sensor 2, a lidar sensor 3, a differential positioning sensor 4, a vision sensor 5, an ultrasonic adjustment device 6, an ultrasonic sensor 7, a controller 8, an inertial unit sensor 9, and a geomagnetic sensor. The specific installation positions of each module in the device can be set according to different vehicle models and actual needs. For example, the geomagnetic sensor can be installed on the bottom of the unmanned operation equipment. This embodiment does not make specific limitations in this regard.

[0042] Based on the above system architecture, this application achieves full-scene autonomous navigation and intelligent obstacle avoidance of unmanned operating equipment in the complex environment of a farm by deeply integrating real-time optimization of multi-source navigation modules and regional obstacle avoidance strategies.

[0043] The following will elaborate on the method flow of this application based on the specific structure of the unmanned operation equipment. For example... Figure 1 As shown, the method includes: Step 110: Based on the current environmental information of the unmanned operating equipment, the current status and priority level of each sensor in the multi-source navigation system, determine the target navigation module in the multi-source navigation system; the multi-source navigation system includes differential positioning sensors, geomagnetic sensors, ultrasonic sensors and inertial unit sensors.

[0044] The unmanned operating equipment referred to here is the driverless mobile device currently required for navigation and obstacle avoidance control within the farm, such as feed spreading equipment, feed collecting equipment, feed pushing equipment, inspection equipment, or disinfection equipment. This embodiment does not specifically limit this. Current environmental information refers to the spatial characteristics of the surrounding environment perceived in real time by the unmanned operating equipment through its onboard sensors. This includes information such as RTK signal strength, whether ground magnetic strips are detected, and whether it is within the enclosure's zoning passage. Current state characterizes whether each sensor in the multi-source navigation system is within its usable or effective signal range. For example, a differential positioning sensor may fail when entering a livestock shed due to severe satellite signal attenuation, but remain effective in open outdoor areas. Priority levels are navigation execution sequences pre-set according to the reliability requirements of different operating scenarios in the farm. For example, the priority of a geomagnetic sensor may be set higher than that of an ultrasonic sensor, or vice versa. This embodiment does not specifically limit this.

[0045] Optionally, when navigation and obstacle avoidance control of unmanned operating equipment are required, the surrounding spatial features can be perceived in real time by various sensors carried on the unmanned operating equipment to obtain current environmental information, and the current status of each sensor in the multi-source navigation system can be perceived in real time, as well as the priority level pre-set for each sensor in the multi-source navigation system can be obtained.

[0046] Subsequently, based on the current environmental information, the current status and priority level of each sensor in the multi-source navigation system, a target navigation module adapted to the current navigation environment is determined in the multi-source navigation system. By comprehensively considering environmental characteristics, module real-time effectiveness and priority level, the target navigation module most suitable for the current environment can be automatically selected from multiple navigation sources.

[0047] It should be noted that when selecting a target navigation module, one can first determine all sensors in a valid state within the multi-source navigation system based on the current state of each sensor. Then, according to the current environmental information and priority level, an appropriate sensor can be adaptively selected as the target navigation module from among all valid sensors. Alternatively, one can first select the sensor that best matches the current environmental information. If the current state of the best-matched sensor is valid, it is prioritized as the target navigation module. If the current state of the best-matched sensor is invalid, a sensor in a valid state is selected from other sensors in the multi-source navigation system according to priority level, and so on. This embodiment does not specifically limit this approach.

[0048] Step 120: Determine the current operating area of ​​the unmanned operation equipment based on the current environmental information, and obtain the target obstacle avoidance component that matches the current operating area.

[0049] The current operating area refers to the geographical range with specific spatial characteristics or functional attributes in which the unmanned equipment is located during the execution of its tasks. In this embodiment, the current operating area may include a straight-line area within the enclosure, a reversing area within the enclosure, a transition area, and an area outside the enclosure. The transition area is an intermediate connecting area equipped with ground magnetic strips, used for the unmanned equipment to move from one area to another, such as an intermediate connecting area between the area outside and inside the enclosure, or between the charging station and the area outside the enclosure. This embodiment does not specifically limit this. The target obstacle avoidance component refers to a set of sensors or a combination of sensors used to perceive obstacles within a specific operating area. The target obstacle avoidance component may include, but is not limited to, one or more of the following: visual sensors, lidar sensors, collision sensors, and photoelectric sensors; the lidar sensor may be a multi-line lidar (Light Detection and Ranging, LiDAR), and the visual sensor may be a red-green-blue-depth (RGB-D) camera.

[0050] Optionally, the current area attribute of the unmanned equipment, i.e. the current working area, can be determined by analyzing the current environmental information of the unmanned equipment.

[0051] Subsequently, based on the identified current area attributes, a pre-stored obstacle avoidance component configuration scheme matching that area is obtained from a local database or cloud server, thus obtaining the target obstacle avoidance component. In one possible implementation, different types of obstacle avoidance modules can be activated in different areas. For example, in the straight-moving area within the enclosure, a combination of visual sensors, LiDAR sensors, collision sensors, and photoelectric sensors is activated as the target obstacle avoidance component; in the area outside the enclosure, LiDAR is activated as the target obstacle avoidance component; and in transition areas or reversing areas within the enclosure, a combination of collision sensors, photoelectric sensors, and LiDAR sensors is activated as the target obstacle avoidance component. In another possible implementation, all obstacle avoidance modules can be kept active, but depending on the operating area, obstacle perception data collected by specific obstacle avoidance modules is defined as the basis for obstacle avoidance decisions, ensuring that the unmanned equipment can obtain the most suitable perception capabilities in different areas.

[0052] Step 130: Based on the obstacle perception information collected in real time by the target obstacle avoidance component and the current working area, determine the target operating mode of the unmanned operation equipment; the target operating mode includes deceleration mode, parking avoidance mode, detour avoidance mode, U-turn movement mode, reversing movement mode, or normal passage mode.

[0053] The obstacle perception information here refers to the data collected by the target obstacle avoidance component that reflects the state of obstacles around the unmanned operating equipment. This information can specifically include the type of obstacle, such as a cow's head that is grazing, a moving work vehicle, a pedestrian, an abnormal road surface, or a fixed fence, etc. This information can also include the real-time distance, azimuth, speed, and trend of the obstacle relative to the unmanned operating equipment.

[0054] The target operation mode refers to the specific driving strategy adopted by unmanned operating equipment to deal with obstacles, aiming to ensure the safety of pedestrians, machines, and livestock. The target operation mode includes: deceleration mode (passing cautiously at a low speed), stopping and avoiding mode (coming to a complete stop), detour and avoiding mode (planning a new path to bypass the obstacle), turning around mode (turning in place or with a small radius), reversing mode (moving backward along the original path), or normal operation mode (driving according to the original preset parameters).

[0055] Optionally, after acquiring the obstacle perception information and the current working area collected in real time by the target obstacle avoidance component, the obstacle perception information can be combined with the constraints of the current working area to determine the most suitable operating mode for the unmanned equipment, thus obtaining the target operating mode. For example, when the unmanned equipment is in a relatively wide area outside the enclosure, and the obstacle perception information indicates the presence of a fixed or dynamic obstacle in front, the target operating mode is determined to be a detour and avoidance mode; while when the unmanned equipment is in a narrow straight-through area inside the enclosure, and a cow's head is detected protruding from the barrier to the side, the target operating mode is determined to be a deceleration and passage mode. This multi-mode dynamic switching mechanism can effectively avoid frequent unexpected stops caused by a single obstacle avoidance logic in traditional control schemes, greatly enhancing the passage flexibility of the unmanned equipment in complex dynamic environments.

[0056] Step 140: Based on the target navigation module and the target operation mode, control the unmanned operation equipment to perform mobile operation actions within the farm.

[0057] Optionally, after obtaining the target navigation module and the target operating mode, the path guidance data provided by the target navigation module can be fused with the driving constraints set by the target operating mode to transform them into drive commands for the underlying actuators. For example, the deviation of the unmanned equipment from the operating trajectory perceived by the target navigation module in real time, such as yaw angle and lateral displacement, can be obtained through the target navigation module. Based on the speed limit determined by the target operating mode and the deviation, the target speed of the left and right drive motors of the unmanned equipment can be calculated to generate corresponding drive commands. This embodiment does not specifically limit this.

[0058] Subsequently, drive commands are sent to the drive circuit of the unmanned equipment to drive the motor and rotate the wheels, thereby controlling the equipment to perform specific movement operations. These movement operations include, but are not limited to, spreading, pushing, collecting, and inspection. Thus, by decoupling and re-integrating navigation and positioning with dynamic obstacle avoidance decision-making in real time, high-precision tracking and intelligent obstacle avoidance of the unmanned equipment are achieved across the entire farm environment.

[0059] The method provided in this embodiment dynamically selects the navigation source in a multi-source navigation system based on the current environmental characteristics, navigation module status, and priority of the unmanned operating equipment. It also matches specific obstacle avoidance components and operating modes with the characteristics of the operating area. This solves the problems of single navigation sources being susceptible to environmental interference and high redundancy of fixed obstacle avoidance logic. It enables unmanned operating equipment to move stably and pass safely under complex dynamic working conditions, ensuring the continuity of farm operations and significantly improving the efficiency of automated operations.

[0060] Figure 4 This is a schematic diagram illustrating the functional implementation of different navigation modules in different scenarios within the unmanned operation equipment provided by this invention; for example... Figure 4 As shown, in this embodiment, the geomagnetic sensor is set to the highest priority, the ultrasonic sensor is set to the second highest priority, and the differential positioning sensor (such as an RTK sensor) is set to the third highest priority. It should be noted that in practical applications, inertial unit sensors (i.e., IMU sensors) are typically used as an auxiliary or supplementary tool for ultrasonic navigation under specific conditions and do not participate independently in the global priority ranking. Furthermore, the preferred target navigation module differs in different areas. For example, in transition areas, the geomagnetic sensor is preferred; in straight-line areas, the ultrasonic sensor is preferred; in reversing areas, the inertial unit sensor is preferred; and in areas outside the enclosure, the differential positioning sensor is preferred.

[0061] Accordingly, step 110 specifically includes: Based on the current environmental information, the system monitors changes in the current work area. When a change is detected, the system controls the target navigation module to perform real-time switching between the differential positioning sensor, the geomagnetic sensor, the ultrasonic sensor, and the inertial unit sensor, based on the current state and the priority level.

[0062] Specifically, during the movement of the unmanned operating equipment, environmental characteristics are continuously collected through a sensor array to monitor whether the equipment's current operating area has changed from one specific scenario to another. For example, when the equipment moves from outside the enclosure to inside, or from a straight path inside the enclosure to uneven ground such as a ramp, a change of area is determined. Subsequently, the controller, based on the effectiveness status and priority level of each sensor in the multi-source navigation system, controls the target navigation module to perform real-time switching to ensure the continuity of navigation accuracy.

[0063] The switching operations specifically include the following four typical scenarios: In scenario one, if the unmanned operating equipment is detected to have entered the transition area and the current state of the geomagnetic sensor is valid, the target navigation module will be switched to the geomagnetic sensor.

[0064] The transition zone refers to an intermediate connecting area equipped with ground magnetic strips, used for unmanned equipment to move from one area to another. Examples include unmanned equipment departing from the charging station to the pasture open area (i.e., the area outside the pen), entering the pen area from the pasture open area, returning to the charging station from the pen area, or entering the pasture open area from the pen area. If the unmanned equipment is detected entering the transition zone and the geomagnetic sensor on the bottom of the equipment detects the ground magnetic strips, it is determined that the geomagnetic sensor is effective. To avoid signal instability caused by obstruction from the pen roof, the target navigation module is switched to the geomagnetic sensor first, thus switching the unmanned equipment's navigation mode to geomagnetic navigation, and accurately guiding the unmanned equipment through the transition zone.

[0065] Scenario 2: When the unmanned operating equipment is detected to have entered the straight-line area within the enclosure, and the ultrasonic sensor is in an effective state, the target navigation module is switched to the ultrasonic sensor.

[0066] Optionally, when the unmanned equipment completely enters the straight-through area such as the feeding passage inside the enclosure from the area outside the enclosure, and the ultrasonic sensor is in an effective state, although there may still be a weak differential positioning signal, since the priority of the ultrasonic sensor is higher than that of the differential positioning sensor, the target navigation module can be automatically switched to the ultrasonic sensor to switch the navigation mode of the unmanned equipment to ultrasonic navigation, thereby accurately guiding the unmanned equipment through the straight-through area.

[0067] Scenario 3: When the unmanned operating equipment is detected to have entered the reversing area within the enclosure, and the inertial unit sensor is in an active state, the target navigation module is switched to the inertial unit sensor.

[0068] Optionally, when the unmanned operating equipment moves within the enclosure, a visual sensor (such as an RGB-D camera) segments the ground image. When it detects that it is about to reach an area within the enclosure unsuitable for ultrasonic tracking (such as a ramp or an area requiring a U-turn), it is determined that the unmanned operating equipment has entered a reversing zone. At this point, the target navigation module can be switched to an inertial unit sensor (also known as an inertial measurement unit) to acquire real-time pose and attitude angle data. For operating robots capable of turning around, inertial guidance is used to change the direction of the vehicle. For operating robots that cannot turn around, inertial guidance is used to assist in reversing along the original path. Once the current operating area is detected to have returned to a flat surface, i.e., after entering the straight-ahead area within the enclosure, the target navigation module is switched back to the ultrasonic sensor to continue ultrasonic navigation of the unmanned operating equipment.

[0069] Scenario 4: When the unmanned operating equipment is detected to have entered the area outside the enclosure and the differential positioning sensor is in an effective state, the target navigation module is switched to the differential positioning sensor.

[0070] Optionally, when the unmanned equipment leaves the pen area and enters the pasture open space (i.e., the area outside the pen), ultrasonic navigation fails due to the loss of reference markers such as limit barriers. At this time, if the RTK signal recovers and is valid, the system switches the target navigation module to a differential positioning sensor (i.e., an RTK sensor) to control the unmanned equipment to perform long-distance tracking operations based on a pre-marked set of navigation movement points, i.e., a preset coordinate and attitude sequence.

[0071] The method provided in this embodiment will be described in detail below with specific examples.

[0072] Figure 5 This is one of the schematic diagrams of the mobile operation layout of the breeding farm provided by the present invention; for example... Figure 5As shown, when the unmanned operation equipment (also known as a robot) starts working, it collects current environmental information through sensors configured on the equipment to determine its current operating area. If the current operating area is a transition zone between the charging station (also known as a charging pile) and the area outside the enclosure, the target navigation module is switched to a geomagnetic sensor to control the unmanned operation equipment to navigate from the charging station into the pasture open area (i.e., the area outside the enclosure) using geomagnetic navigation. Once the environmental information indicates that the unmanned operation equipment has entered the pasture open area, since the current operating area has switched to the area outside the enclosure, when the RTK signal is valid, the target navigation module is switched to a differential positioning sensor to control the unmanned operation equipment to navigate using RTK. The track navigation system moves across open areas of the pasture. When the current environmental information determines that the unmanned equipment is moving from the open area to the transition zone between the pasture and the entrance of the pen, and the geomagnetic sensor detects the signal of the ground magnetic strip, the target navigation module is switched to the geomagnetic sensor to control the unmanned equipment to enter the pen area using geomagnetic navigation, since the current work area has switched to the transition zone. When the current environmental information determines that the unmanned equipment has entered the pen area, the RTK signal may still exist, but since its priority is not as high as that of the ultrasonic sensor, the target navigation module will be switched to the ultrasonic sensor to control the unmanned equipment to move using ultrasonic navigation. During movement, the RGB-D camera detects livestock features (such as cow heads) and segments the ground image. When it detects that it is about to enter an area unsuitable for movement within the pen, it determines that the unmanned equipment is entering a reversing area within the pen. At this point, the target navigation module switches to an inertial unit sensor to control the unmanned equipment to move using IMU navigation. After detecting a flat surface, it determines that the unmanned equipment is re-entering a straight-line area within the pen, and then switches the target navigation module back to an ultrasonic sensor to control the unmanned equipment to continue moving using ultrasonic navigation. When about to leave the pen area, if a signal from a ground magnetic strip is detected, it determines that the unmanned equipment is entering a transition area between the pen area and the area outside the pen. At this point, the target navigation module switches to a geomagnetic sensor to control the unmanned equipment to move using geomagnetic navigation. After determining that the unmanned equipment has entered an open area of ​​the pasture based on the current environmental information, the target navigation module switches to a differential positioning sensor to control the unmanned equipment to move using RTK line-following navigation. After completing all tasks, it returns to the charging station using geomagnetic navigation.

[0073] The method provided in this embodiment achieves a smooth transition of unmanned equipment at different work boundaries by setting a strict priority system and combining the dynamic characteristics of the work area to perform real-time switching of navigation sources. It solves the problem of positioning loss in areas with signal obstruction and significantly improves the navigation stability and environmental adaptability of unmanned equipment in the entire ranch scenario.

[0074] In some embodiments, the method further includes: If the unmanned equipment is detected to have entered the transition area and the current state of the geomagnetic sensor is in a failed state, the navigation module with the highest priority among the other navigation modules that are currently in a valid state will be identified as the target navigation module; the other navigation modules are the navigation modules in the multi-source navigation system other than the geomagnetic sensor.

[0075] Optionally, this embodiment provides a fault-tolerant degradation mechanism when the core navigation source fails in the transition region, so as to improve the survivability of the system.

[0076] Specifically, when unmanned equipment moves to a transitional area equipped with ground magnetic strips, such as the entrance to an enclosure or the connection point to a charging station, geomagnetic navigation should be prioritized under normal circumstances. However, if the geomagnetic sensor becomes ineffective due to missing or buried ground magnetic strips, hardware failure of the geomagnetic sensor, or excessive environmental electromagnetic interference, a fallback processing logic will be automatically initiated. The system performs real-time retrieval of other navigation modules in the multi-source navigation system besides the geomagnetic sensor, namely differential positioning sensors, ultrasonic sensors, and inertial unit sensors. It determines which modules are currently active and, among these active modules, performs the preferred operation based on a preset priority level. For example, if the geomagnetic signal is missing when the unmanned equipment enters the magnetic strip area at the entrance of the enclosure, but the ultrasonic sensor is active, the system will adaptively identify the ultrasonic sensor as the target navigation module because its priority is higher than that of the differential positioning sensor.

[0077] In such extreme circumstances, if all other retrieved navigation modules are also malfunctioning, or if unexpected situations such as location loss, excessive navigation deviation, or obstacles being too close occur after switching, an immediate warning will be sent to the farm management personnel, remotely transmitting the current real-time status and specific problems of the unmanned equipment. Human intervention is required at this point. Only after the management personnel guide the unmanned equipment to the effective area or resolve the sensor malfunction can the unmanned equipment resume navigation, thus ensuring the absolute safety of people, machines, and livestock within the farm.

[0078] Similarly, if the current state of the preferred navigation module corresponding to other work areas is invalid, the preferred operation can be performed as the target navigation module among other navigation modules that are currently valid, according to the priority level. For details, please refer to the degradation processing logic of the transition area, which will not be elaborated here.

[0079] The method provided in this embodiment effectively compensates for the control blind spots of a single navigation source under extreme abnormal conditions by introducing a priority-based navigation degradation mechanism. It realizes redundant navigation protection for unmanned equipment at key operation nodes, greatly reduces the risk of unexpected operation interruption caused by partial sensor failure, further enhances the control stability and operational robustness of the entire farm's unmanned driving system, and ensures a high degree of continuity of automated farm operations.

[0080] In some embodiments, step 120 specifically includes: If the current working area is determined to be a straight-line area within the enclosure, the first obstacle avoidance component is identified as the target obstacle avoidance component; the first obstacle avoidance component includes a visual sensor and a non-optical image sensing sensor; the non-optical image sensing sensor includes at least one of a lidar sensor, a collision sensor, and a photoelectric sensor; If the current working area is determined to be a transition area or a reversing area within the enclosure, the second obstacle avoidance component is identified as the target obstacle avoidance component; the second obstacle avoidance component includes at least one of the collision sensor, the photoelectric sensor, and the lidar sensor; If the current working area is determined to be outside the enclosure, the third obstacle avoidance component is identified as the target obstacle avoidance component; the third obstacle avoidance component includes the lidar sensor.

[0081] Figure 6 This is a schematic diagram illustrating the functional implementation of different obstacle avoidance modules in the unmanned operation equipment provided by this invention under different scenarios. For example... Figure 6 As shown, when the current working area is determined to be a straight-line area within the pen, the first obstacle avoidance component is identified as the target obstacle avoidance component. This first obstacle avoidance component includes a visual sensor (such as an RGB-D camera) and at least one of a lidar sensor (such as LiDAR), a collision sensor, and a photoelectric sensor. Specifically, due to the complex features within the pen, such as livestock grazing, human movement, and narrow passageways, a combined approach incorporating visual sensors, lidar sensors, collision sensors, and photoelectric sensors can achieve comprehensive perception of livestock biological characteristics and subtle physical obstacles.

[0082] If the current work area is determined to be a transition zone or a reversing zone within the enclosure, the second obstacle avoidance component is designated as the target obstacle avoidance component. This second obstacle avoidance component includes at least one of a collision sensor, a photoelectric sensor, and a lidar sensor. Since transition zones, such as enclosure entrances or charging station connection sections, typically have relatively fixed paths and primarily contain physical structural obstacles, the system, by acquiring a sensor combination focused on close-range physical detection, can ensure the safe passage of unmanned equipment through narrow sections.

[0083] If the current operating area is determined to be outside the enclosure, the third obstacle avoidance component is identified as the target obstacle avoidance component. The third obstacle avoidance component includes a lidar sensor. In open pasture areas, obstacles are typically sparsely distributed and far apart. By acquiring lidar sensors with long detection ranges as the core obstacle avoidance sensing source, the system can effectively identify pedestrians or vehicles in the distance.

[0084] The method provided in this embodiment achieves optimal configuration of sensing resources by matching differentiated obstacle avoidance components to work areas with different risk levels. This ensures both precise obstacle avoidance in complex indoor environments and large-scale early warning in outdoor environments, significantly improving the environmental adaptability of obstacle avoidance.

[0085] Based on the above embodiments, this embodiment further refines how to dynamically adjust the driving strategy of the unmanned operation equipment according to the specific target type sensed when traveling in a straight area within the enclosure. Step 130 specifically includes: If the current working area is determined to be the straight-line area, the presence of dynamically moving obstacles within the first preset range of the unmanned operating equipment is determined based on the obstacle perception information collected in real time by the non-optical image perception sensor. In the presence of the dynamically moving obstacle, the target operating mode is determined as the parking avoidance mode; If, in the absence of the dynamically moving obstacle, the obstacle perception information collected in real time by the visual sensor indicates that a biometric target exists within the second preset range of the unmanned operating equipment, then the target operation mode is determined to be the deceleration passage mode; if the obstacle perception information indicates that the biometric target does not exist within the second preset range, then the target operation mode is determined to be the normal passage mode.

[0086] Figure 7 This is the second flowchart of the control method for unmanned operation equipment applied to a farm provided by the present invention; Figure 8 This is a schematic diagram of the target detection results provided by the present invention; Figure 9 This is the second schematic diagram of the mobile operation layout of the breeding farm provided by the present invention.

[0087] like Figure 7As shown, when the current work area is determined to be a straight-line area within the enclosure, and the ultrasonic waves are effective (i.e., the ultrasonic sensor is in an effective state), the system first determines whether there are dynamically moving obstacles (hereinafter referred to as dynamic obstacles) within a first preset range of the unmanned operating equipment based on obstacle perception information collected in real time by the non-optical image perception sensor. For example, the lidar sensor in the first obstacle avoidance component is used to scan in real time within the first preset range centered on the unmanned operating equipment to see if there are dynamically moving obstacles such as pedestrians or other unmanned operating equipment (such as feeding vehicles). Here, the first preset range is the detection range that triggers the non-optical image perception sensor to perform parking trigger condition detection, such as the area directly in front of the unmanned operating equipment at a first preset distance.

[0088] In the presence of dynamically moving obstacles, to avoid the risk of collision, the current operating mode (i.e., the target operating mode) of the unmanned operating equipment is set to stop and avoidance mode, and it enters a waiting state until the obstacle moves beyond the safety threshold range.

[0089] In the absence of dynamically moving obstacles, if the presence of a biometric target is determined within the second preset range of the unmanned equipment based on obstacle perception information collected in real time by the visual sensor, the target operation mode is set to a deceleration passage mode. Specifically, a visual sensor (such as an RGB-D camera) is used to collect color image data within the second preset range centered on the unmanned equipment, and the algorithm built into the visual sensor is used to detect biometric targets (such as a cow's head) in real time. The specific detection results are as follows: Figure 8 The red detection box is shown. The second preset range is the detection range that triggers the visual sensor to detect the deceleration operation trigger condition. For example, it can be the limit bar area in front of the unmanned operating equipment at a second preset distance. The first and second preset distances can be set according to actual needs, such as the second preset distance being greater than the first preset distance. This embodiment does not specifically limit this.

[0090] When a biometric target such as a cow's head is detected within the second preset range, the distance between the biometric target and the robot is obtained, and a movement warning is sent to the unmanned operating equipment in advance. This prompts the unmanned operating equipment to reduce its speed, allowing it to move at low speed and ensuring the safety of the biometric target. If the obstacle perception information determines that no biometric target exists within the second preset range, indicating that the current path is safe to pass, the target operation mode is set to normal passage mode, and the unmanned operating equipment is controlled to move at full speed, thus ensuring the operational efficiency of the unmanned operating equipment.

[0091] This embodiment of the solution achieves a more intelligent logical decision-making process than simply stopping upon encountering an obstacle by classifying and identifying obstacles as dynamic and biological targets. While ensuring the safety of humans, machines, and animals, it minimizes unnecessary stops during operations and improves overall operational efficiency.

[0092] In addition, such as Figure 7 As shown, this embodiment also provides a dynamic obstacle avoidance strategy for transition areas. Specifically, when the current work area is determined to be a transition area based on the current environmental information, and the geomagnetic sensor is effective (i.e., the geomagnetic field is effective), the laser radar sensor in the second obstacle avoidance component can be used to scan in real time whether there are moving obstacles such as pedestrians or other unmanned equipment (e.g., feeding vehicles) within a first preset range centered on the unmanned work equipment. If a moving obstacle is present, to avoid collision risks, the current operating mode (i.e., the target operating mode) of the unmanned work equipment is set to a stop-and-avoidance mode, and it enters a waiting state until the obstacle moves beyond the safety threshold range, at which point it switches to normal passage mode to continue performing the geomagnetic navigation task.

[0093] In addition, such as Figure 7 As shown, this embodiment also provides a dynamic obstacle avoidance strategy for areas outside the enclosure. Specifically, when the current work area is determined to be outside the enclosure based on the current environmental information, and the differential positioning sensor is effective (i.e., RKT is effective), the laser radar sensor in the third obstacle avoidance component can be used to scan in real time whether there are moving pedestrians or other unmanned equipment (such as feeding vehicles) or other dynamically moving obstacles within a first preset range centered on the unmanned work equipment. In the presence of dynamically moving obstacles, to avoid the risk of collision, the current operating mode (i.e., the target operating mode) of the unmanned work equipment is set to detour avoidance mode, and it enters a waiting state until it moves beyond the safety threshold range of the obstacle, then switches to normal passage mode to continue performing the tracking task; or, when the surrounding space is insufficient to support detour, the unmanned work equipment will enter a waiting state until the dynamically moving obstacle is detected to have moved beyond the preset safety threshold range, then returns to normal passage mode to continue performing the tracking task.

[0094] This embodiment achieves enhanced logical judgment and continuous operation capabilities for unmanned equipment by refining the classification of dynamic obstacles in different areas, while ensuring safety.

[0095] Based on the above embodiments, this embodiment also provides an intelligent reversing and adaptive operation scheme based on visual feedback. Accordingly, the method further includes: When the current working area is determined to be the straight-ahead area, the visual sensor is used to collect image data of the road surface in front of the unmanned working equipment, and the image data is segmented to obtain ground area features; The tilt angle of the ground edge is detected based on the ground area features. When the tilt angle is detected to be greater than a preset angle threshold, it is determined that the unmanned operation equipment has entered the reversing area within the enclosure, and the vehicle mobility performance parameters of the unmanned operation equipment are obtained. If the vehicle mobility performance parameters indicate that the unmanned operating equipment has the conditions to complete the reversal within the reversal area, then the target navigation module is switched to the inertial unit sensor, and the target operating mode is determined to be the turning movement mode, so as to control the unmanned operating equipment to perform the turning action. If the vehicle mobility performance parameters indicate that the unmanned operating equipment does not have the conditions to complete a U-turn in the reversing area, the target operating mode is determined as the reversing movement mode, so as to control the unmanned operating equipment to perform the reversing action along the original route.

[0096] like Figure 7 As shown, when it is determined that the unmanned operating equipment is moving in a straight area within the enclosure, the visual sensors (such as RGB-D cameras) in the first obstacle avoidance component can be used to collect image data of the road surface in front of the unmanned operating equipment. Artificial intelligence models, such as semantic segmentation algorithms like Mask Region-based Convolutional Neural Network (Mask-R-CNN), can be applied to segment the image data into regions, so as to accurately identify and divide the ground area from the complex background, thereby extracting the ground area features for subsequent calculations.

[0097] Subsequently, the tilt angle of the ground edge is detected by analyzing the geometric information representing the road surface edge in the ground area features. This angle reflects the flatness of the ground under the chassis and whether it is about to reach the physical boundary.

[0098] For example, it can be achieved by extracting such as Figure 8 The geometric information of the left side (denoted as L) and right side (denoted as R) of the ground monitoring rectangle shown in the green box is used to dynamically detect the tilt angle of the ground edge by calculating the coordinates of the endpoints of these two sides.

[0099] Then, it is determined whether the tilt angle is greater than a preset angle threshold. If it is not greater, monitoring continues until a tilt angle greater than the preset angle threshold is detected. For example, when the angle indicator shows a significant change in the current road surface slope or the end of the enclosure is reached, it is determined that the unmanned operating equipment has entered a reversing area from a straight-ahead area, meaning that the current position is not suitable for continuing ultrasonic tracking. Specifically, as shown in the figure... Figure 9 As shown. At this time, the vehicle mobility performance parameters of the unmanned operation equipment are actively obtained from the local configuration information of the unmanned operation equipment. These vehicle mobility performance parameters refer to technical indicators that characterize the mobility of the unmanned operation equipment in confined environments, such as the minimum turning radius of the equipment, the external dimensions of the vehicle body, and the steering control capability of the drive motor.

[0100] If the acquired parameters indicate that the current unmanned operating equipment is small in size and capable of turning in place (such as a material pusher robot), then the conditions for performing a U-turn within the current reversing area are met. In this case, the target navigation module is switched from ultrasonic navigation to inertial unit sensor, and the target operating mode is set to U-turn movement mode. This controls the unmanned operating equipment to perform the task of turning around in inertial navigation mode. Once the turning around is completed and the visual sensor detects a flat road surface again, the unmanned operating equipment is switched back to ultrasonic navigation to continue moving. If the turning around is not completed, the process returns to the step of acquiring the vehicle mobility performance parameters of the unmanned operating equipment, iteratively switching the target operating mode until the turning around is completed and the visual sensor detects a flat road surface again. Then, the unmanned operating equipment is switched back to ultrasonic navigation to continue moving.

[0101] If the acquired parameters indicate that the current unmanned operating equipment is a large, bulky machine (such as a material spreader), and its physical size and turning radius limit its ability to turn around at the end of a narrow pen passage, then the target operating mode is set to reverse movement mode. Based on the current pose record, the unmanned operating equipment is controlled to reverse along the original route using reverse navigation mode, and the reverse marker of the unmanned operating equipment is set to 1. During the reversing process, the surrounding environment is continuously monitored until the unmanned operating equipment returns to the preset connection position or the effective RTK area. Then, the vehicle's direction is adjusted, the reverse marker of the unmanned operating equipment is set to 0, and subsequent tasks are executed.

[0102] The method provided in this embodiment achieves advanced perception of unstructured terrain changes in special working conditions such as narrow passages and slopes by deeply coupling visual terrain feedback with a multi-source navigation module. Combined with the differences in physical performance of unmanned equipment of different specifications, it provides differentiated reversal decisions, effectively solving the navigation interruption problem caused by terrain limitations in complex ranch environments for large and small unmanned equipment. This greatly improves the degree of autonomy in reversal and escape scenarios, ensuring the high flexibility and safety of mobile operations in farms.

[0103] Figure 10 This is the third schematic diagram of the mobile operation layout of the breeding farm provided by the present invention; as shown. Figure 10 As shown, in some embodiments, ultrasonic sensors serve as the primary means of navigation within the enclosure. These sensors can be a three-in-one ultrasonic module, consisting of a first, second, and third ultrasonic sensor. These sensors are arranged sequentially at intervals along the direction of travel of the unmanned equipment. The second ultrasonic sensor is located between the first and third ultrasonic sensors. In an optional embodiment, the first ultrasonic sensor is arranged as the front sensor, the third ultrasonic sensor as the rear sensor, and the second ultrasonic sensor as the middle sensor.

[0104] Accordingly, step 140 specifically includes: When the target navigation module is the ultrasonic sensor, the first distance data between the unmanned operating equipment and the limit bar measured by the first ultrasonic sensor, the third distance data between the unmanned operating equipment and the limit bar measured by the third ultrasonic sensor, and the second distance data between the unmanned operating equipment and the limit bar measured by the second ultrasonic sensor are respectively acquired. Calculate the average value between the first distance data and the third distance data; If the difference between the average value and the second distance data is less than a preset deviation value, the correction control amount of the movement offset angle of the unmanned operation equipment is determined based on the distance difference between the first distance data and the third distance data, and the preset reference distance data, and the correction control amount of the movement speed of the unmanned operation equipment is determined based on the distance difference between the second distance data and the preset reference distance data. The motor speed of the unmanned operating equipment is adjusted according to the correction control amount of the moving offset angle and the correction control amount of the moving speed. Based on the adjusted motor speed and the target operating mode, the unmanned operating equipment is controlled to perform mobile operations within the farm.

[0105] Optionally, when the target navigation module is an ultrasonic sensor, the first distance data between the unmanned operating equipment and the limit bar, measured by the first ultrasonic sensor, is acquired (denoted as...). The third distance data between the unmanned operating equipment and the limit bar, measured by the third ultrasonic sensor (denoted as...). ), and the second distance data between the unmanned operating equipment and the limit bar measured by the second ultrasonic sensor (denoted as ...). When the unmanned equipment moves within the enclosure, it is necessary to read the physical distance between the machine body and the horizontal bar on the limit bar in real time based on the installation position of these three ultrasonic sensors.

[0106] Then, the average of the first distance data and the third distance data is calculated (denoted as ). The specific calculation formula can be expressed as: ; Then, calculate the difference between the average value and the second distance data.

[0107] Due to the complex environment of farms, the restraining fences inside the pens are usually equipped with movable livestock clamps (such as cattle neck clamps), which can cause local distance deviations. In addition, some farm roads (such as sheep farm roads) may have depressions or uneven restraining fences. To avoid excessive deviations in navigation due to local bumps or depressions, the following logic can be executed to adjust the motor speed of the unmanned operation equipment: When the difference is less than the preset deviation threshold (denoted as...) In the case of a situation where the current limit bar reference surface is flat, navigation is considered to be in normal condition. At this time, based on the distance difference between the first distance data and the third distance data, and using preset benchmark distance data, the correction control amount of the unmanned operation equipment's movement offset angle is determined through geometric relationships (such as trigonometric function relationships, etc., specifically determined according to the actual motion model). This allows the unmanned operation equipment's movement direction, i.e., whether to turn left or right, to be adjusted according to the positive or negative value and magnitude of the correction control amount of the movement offset angle.

[0108] Furthermore, the distance difference between the second distance data and the preset reference distance data is calculated to determine the correction control amount of the unmanned operation equipment's moving speed based on the distance difference. This allows for a larger adjustment of the vehicle speed when the distance difference is too large, and a smaller adjustment when the distance difference is too small, in order to maintain a relatively stable driving speed.

[0109] Subsequently, the motor speed of the unmanned operating equipment is dynamically adjusted based on the correction control amount of the movement offset angle and the correction control amount of the movement speed. For example, by adjusting the speed difference between the left and right drive motors, the unmanned operating equipment maintains a preset parallel distance from the limit bar for navigation movement.

[0110] Finally, based on the adjusted motor speed and target operating mode, the unmanned equipment is controlled to perform smooth movement operations within the farm.

[0111] In another possible implementation, if the detected difference is greater than or equal to a preset deviation threshold, it indicates that there is unexpected severe deformation or obstacle interference in the current lateral environment, making it impossible to provide a reliable tracking reference. In this case, navigation will immediately stop and a warning will be issued, requiring manual intervention to ensure safety.

[0112] The method provided in this embodiment uses a three-point ultrasonic ranging verification mechanism to compare the average values ​​of the front and rear sensors with the data from the middle sensor. This effectively filters out random interference caused by factors such as cattle neck clamps, local deformation of the restraint fence, and road surface depressions. It not only solves the problem that a single ultrasonic sensor is easily affected by local working conditions, leading to navigation instability, but also significantly improves the tracking accuracy and anti-interference ability of unmanned operating equipment in unstructured pen environments, ensuring the accurate execution of tasks such as feeding and pushing feed.

[0113] In some embodiments, step 140 further includes: When the target navigation module is the geomagnetic sensor, the position of the ground magnetic stripe is detected by the geomagnetic sensor; The yaw angle of the unmanned operating equipment relative to the ground magnetic strip is calculated based on the position of the ground magnetic strip; The motor speed of the unmanned operating equipment is adjusted according to the yaw angle; Based on the adjusted motor speed and the target operating mode, the unmanned operating equipment is controlled to perform moving operations along the ground magnetic strip.

[0114] In this embodiment, the geomagnetic sensor, as a stable and reliable navigation method, is mainly used in transitional areas, such as when entering or leaving a charging station or the entrance to a enclosure, where satellite signals are easily blocked.

[0115] Optionally, when the target navigation module is a geomagnetic sensor, since the geomagnetic sensor of the unmanned operation equipment mainly relies on magnetic strips laid on the ground and stations next to the magnetic strips for movement and positioning, the position of the ground magnetic strips can be obtained in real time by sensing the magnetic field signal generated by the ground magnetic strips through a geomagnetic sensor (also called a geomagnetic sensor) arranged on the bottom of the vehicle body.

[0116] The offset distance and direction of the magnetic strip centerline relative to the central axis of the unmanned operation equipment can be determined based on the position of the magnetic strip on the ground. Then, the current yaw angle of the unmanned operation equipment can be obtained through geometric calculation using the offset distance and offset direction.

[0117] Then, the motor speed of the unmanned operating equipment is adjusted according to the yaw angle. In one optional implementation, a preset proportional-integral-derivative (PID) algorithm is applied, using the yaw angle as feedback input, to calculate the speed compensation of the left and right drive motors in real time, thereby adjusting the speed of the two motors in a timely manner and eliminating heading deviation.

[0118] Finally, based on the adjusted motor speed and target operating mode, the unmanned equipment is controlled to perform moving operations along the ground magnetic strip.

[0119] The method provided in this embodiment achieves precise guidance of unmanned operating equipment in the transition area through the physical coupling of the geomagnetic sensor and the ground magnetic strip, and through real-time monitoring of the ground magnetic strip signal and dynamic compensation of the motor speed, thus ensuring the smooth connection of the farm operation process.

[0120] In some embodiments, step 140 further includes: When the target navigation module is the differential positioning sensor, the target trajectory point to be tracked is determined from a pre-constructed navigation movement point set; the navigation movement point set contains multiple trajectory points arranged according to the operation sequence of the unmanned operation equipment. Compare the current positioning coordinates of the unmanned operating equipment with the coordinates of the target trajectory point; The driving direction of the unmanned operation equipment is adjusted based on the comparison results; Based on the adjusted driving direction and the target operating mode, the unmanned operation equipment is controlled to perform mobile operation actions at each trajectory point in the navigation movement point set.

[0121] In this embodiment, the differential positioning sensor can be an RTK (Real-Time Kinematic) sensor to achieve centimeter-level high-precision positioning.

[0122] like Figure 10 As shown, when the target navigation module is a differential positioning sensor, the path data required for the operation needs to be pre-constructed. Specifically, before executing the automated operation task, the operator needs to mark the trajectory of the unmanned operation equipment. During the marking process, the operator manually drives or guides the unmanned operation equipment along the preset path, and collects and stores the GPS position and attitude data of each location point in real time into the unmanned operation equipment, thereby constructing a navigation movement point set containing several point sets. This navigation movement point set contains multiple trajectory points arranged according to the operation sequence of the unmanned operation equipment, and each trajectory point has clear coordinate information and heading angle information.

[0123] Subsequently, during the operation, when the RTK signal is valid, the target trajectory point to be tracked is determined from the navigation movement point set. Typically, based on the current operation progress and position of the unmanned equipment, the next trajectory point closest to it and located ahead of the travel direction is selected from the ordered point set as the current target trajectory point to be tracked.

[0124] Subsequently, the current positioning coordinates of the unmanned operation equipment fed back by the differential positioning sensor are acquired in real time. The current positioning coordinates of the unmanned operation equipment are compared with the coordinates of the target trajectory point to obtain the deviation value between the current positioning coordinates of the unmanned operation equipment and the coordinates of the target trajectory point, such as lateral deviation and heading deviation.

[0125] Subsequently, the travel direction of the unmanned operating equipment is adjusted based on the comparison results. In one optional implementation, a steering command is generated based on the calculated deviation value using a preset path tracking algorithm (such as a pure tracking algorithm or a proportional-integral-derivative control algorithm). This allows for real-time correction of the travel direction by adjusting the steering angle of the drive mechanism or adjusting the speed difference between the left and right drive motors.

[0126] Finally, based on the adjusted driving direction and target operating mode, the unmanned operation equipment is controlled to sequentially perform mobile operation actions at each trajectory point in the navigation movement point set.

[0127] The method provided in this embodiment, by introducing a pre-marked path point set and combining it with high-precision differential positioning feedback, enables unmanned operating equipment to perform autonomous and precise tracking operations in large-scale, open pasture scenarios, significantly improving the path-keeping capability and operational automation efficiency of unmanned operating equipment during long-distance movement.

[0128] In some embodiments, the inertial unit sensor, i.e., the inertial measurement unit, is mainly used to provide accurate attitude maintenance and short-distance position estimation in the reversing area within the enclosure, ensuring that the unmanned operating equipment can still perform complex actions when external reference markers are lost. Accordingly, when the target navigation module is an inertial unit sensor, step 140 further includes: First, the pose information before navigation begins is acquired. Specifically, when it is determined that the unmanned operating equipment has entered the reversing area and switched to inertial navigation, the attitude angle, velocity, and coordinate data at the moment of switching are recorded as the starting reference for subsequent calculations.

[0129] Subsequently, the inertial unit sensor is used to collect the pose information after navigation in real time. Specifically, during the movement of the unmanned equipment, high-frequency angular velocity and linear acceleration information are obtained through the accelerometer and gyroscope inside the inertial unit sensor to obtain the pose information after navigation.

[0130] Then, the pose information after navigation is integrated with the pose information before navigation begins by taking the time derivative, and the pose change result is obtained. In specific implementation, the current yaw angle, pitch angle, and displacement change of the unmanned operating equipment relative to the initial reference are calculated in real time by performing time-dimensional integration on the collected angular velocity and acceleration data.

[0131] Next, based on the pose change results obtained from the integral calculation, the motor speed of the unmanned operation equipment is calculated. Specifically, based on the target pose that the unmanned operation equipment needs to achieve, the target speeds that the left and right drive motors need to achieve are calculated to compensate for the accumulated attitude deviation.

[0132] Finally, based on the calculated motor speed and target operating mode, the unmanned equipment is controlled to perform mobile operations within the farm. For example, in the turning-around mode, the left and right motors are controlled to rotate in opposite directions, using inertial guidance to achieve precise on-the-spot turning of the vehicle.

[0133] The method provided in this embodiment realizes the autonomous movement capability of unmanned operating equipment in the absence of external guidance signals, such as magnetic strips, visual references, and satellite signals, through real-time integral calculation and closed-loop control of attitude data. It effectively solves the guidance problem of unmanned operating equipment in special areas such as slope reversal and narrow road U-turn, and significantly improves the continuity and environmental adaptability of the navigation system.

[0134] In summary, the control method for unmanned operating equipment in farms provided in this application has the following specific functions: First, it features multiple navigation methods and rapid switching. This means that it can achieve a universal navigation technology for various unmanned operating equipment in different ranch environments that present challenges such as unstructured terrain, such as feeding passages or ramps, dynamic obstacles, such as moving herds or groups of work vehicles, and signal interference, such as GPS signal attenuation in livestock sheds. It combines the characteristics of multi-source navigation technologies such as RTK navigation, ultrasonic navigation, geomagnetic navigation, and inertial navigation, and achieves rapid switching of navigation modes through control strategies.

[0135] Secondly, it features obstacle avoidance and dynamic adjustment, meaning it has the ability to monitor and adjust in real time. In addition to using lidar to avoid obstacles in all scenarios, it can also flexibly adjust its speed in the pen according to the actual feeding situation of cattle and sheep. Through this dynamic adjustment mechanism, the safe movement of unmanned equipment can be ensured.

[0136] Third, slope detection and intelligent U-turn; that is, the ground is detected by intelligent algorithms, and the slope of the left and right sides is calculated. Once the detection setting is met, it is determined that the U-turn condition is met, and then the intelligent U-turn action is executed, which significantly improves the adaptability of navigation in the complex environment of the ranch.

[0137] Fourth, stable closed-loop feedback, that is, a closed-loop feedback control architecture of real-time perception, dynamic decision-making, execution feedback and deviation correction is constructed. It has strong ability to suppress interference and robustness, and can adapt to all aspects of cattle and sheep navigation, ensuring the stable and reliable operation of unmanned operation equipment.

[0138] Based on the implementation of the above technical solution, the advantages brought by this application are mainly reflected in: Advantage 1: Significantly improves the efficiency of aquaculture operations. The unmanned driving control strategy proposed in this application supports various types of robots to perform multi-mode navigation tasks, making aquaculture management in line with the modern trend of intelligent and intensive development, and effectively improving the overall operational efficiency of the ranch.

[0139] Advantage 2: Ensuring stable and reliable operation. Through closed-loop control and multi-source redundant navigation, the production process can still be maintained even in the face of harsh environments or partial sensor failures, reducing the risk of unexpected shutdowns.

[0140] Advantage 3: Ensuring a high level of safety during mobile operations. Utilizing multiple sensors for real-time, multi-dimensional monitoring of the surrounding environment and intelligently adjusting speed based on data extraction results, the system meets the stringent obstacle avoidance requirements throughout the entire operation, protecting the safety of livestock, workers, and unmanned equipment within the farm.

[0141] Fourthly, it significantly reduces the cost of manual intervention and management. The highly automated operation mode reduces reliance on manual driving, thereby lowering labor costs. At the same time, precise logic control reduces human error and improves the accuracy of ranch management.

[0142] Based on the above-described control method for unmanned equipment used in livestock farms, this application also provides a control system for unmanned equipment used in livestock farms. This control system can be referred to in correspondence with the control method described above.

[0143] Figure 11 This is a schematic diagram of the structure of the unmanned operation equipment control system for farms provided by the present invention; as shown. Figure 11 As shown, the system includes: The first decision unit 1110 is used to determine the target navigation module in the multi-source navigation system based on the current environmental information of the unmanned operation equipment, the current status and priority level of each sensor in the multi-source navigation system; the multi-source navigation system includes differential positioning sensors, geomagnetic sensors, ultrasonic sensors and inertial unit sensors; The second decision-making unit 1120 is used to determine the current operating area of ​​the unmanned operation equipment based on the current environmental information, and to obtain a target obstacle avoidance component that matches the current operating area; The third decision unit 1130 is used to determine the target operation mode of the unmanned operation equipment based on the obstacle perception information collected in real time by the target obstacle avoidance component and the current operation area; the target operation mode includes deceleration mode, parking avoidance mode, detour avoidance mode, U-turn movement mode, reversing movement mode, or normal passage mode. The control unit 1140 is used to control the unmanned operation equipment to perform mobile operation actions within the farm according to the target navigation module and the target operation mode.

[0144] The system provided in this embodiment dynamically selects the best navigation source in a multi-source navigation system based on the current environmental characteristics, navigation module status, and priority of the unmanned operating equipment. It also matches specific obstacle avoidance components and operating modes with the characteristics of the operating area. This solves the problems of single navigation sources being susceptible to environmental interference and high redundancy of fixed obstacle avoidance logic. It enables unmanned operating equipment to move stably and pass safely under complex dynamic working conditions, ensuring the continuity of farm operations and significantly improving the efficiency of automated operations.

[0145] The system provided by this invention is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0146] Figure 12 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 12 As shown, the electronic device may include: a processor 1210, a communications interface 1220, a memory 1230, and a communication bus 1240, wherein the processor 1210, the communications interface 1220, and the memory 1230 communicate with each other through the communication bus 1240. The processor 1210 can call logic instructions in the memory 1230 to execute a control method for unmanned operating equipment applied to a farm. This method includes: determining a target navigation module in the multi-source navigation system based on the current environmental information of the unmanned operating equipment, the current status and priority level of each sensor in the multi-source navigation system; the multi-source navigation system includes differential positioning sensors, geomagnetic sensors, ultrasonic sensors, and inertial unit sensors; determining the current operating area of ​​the unmanned operating equipment based on the current environmental information, and acquiring a target obstacle avoidance component matching the current operating area; determining a target operating mode of the unmanned operating equipment based on obstacle perception information collected in real time by the target obstacle avoidance component and the current operating area; the target operating mode includes a deceleration mode, a stopping and yielding mode, a detour and yielding mode, a U-turn mode, a reversing mode, or a normal operating mode; and controlling the unmanned operating equipment to perform mobile operation actions within the farm based on the target navigation module and the target operating mode.

[0147] Furthermore, the logical instructions in the aforementioned memory 1230 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0148] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the unmanned operation equipment control method for aquaculture farms provided by the above methods. The method includes: determining a target navigation module in the multi-source navigation system based on the current environmental information of the unmanned operation equipment, the current status and priority level of each sensor in the multi-source navigation system; the multi-source navigation system includes a differential positioning sensor, a geomagnetic sensor, an ultrasonic sensor, and an inertial unit sensor; determining the current operating area of ​​the unmanned operation equipment based on the current environmental information, and acquiring a target obstacle avoidance component matching the current operating area; determining a target operating mode of the unmanned operation equipment based on the obstacle perception information collected in real time by the target obstacle avoidance component and the current operating area; the target operating mode includes a deceleration mode, a stop and avoidance mode, a detour and avoidance mode, a U-turn mode, a reversing mode, or a normal passage mode; and controlling the unmanned operation equipment to perform mobile operation actions within the aquaculture farm based on the target navigation module and the target operating mode.

[0149] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program is implemented to perform the control method for unmanned operating equipment applied to a farm provided by the methods described above. The method includes: determining a target navigation module in the multi-source navigation system based on the current environmental information of the unmanned operating equipment, the current state and priority level of each sensor in the multi-source navigation system; the multi-source navigation system includes a differential positioning sensor, a geomagnetic sensor, an ultrasonic sensor, and an inertial unit sensor; determining the current operating area of ​​the unmanned operating equipment based on the current environmental information, and acquiring a target obstacle avoidance component matching the current operating area; determining a target operating mode of the unmanned operating equipment based on obstacle perception information collected in real time by the target obstacle avoidance component and the current operating area; the target operating mode includes a deceleration mode, a stop and avoidance mode, a detour and avoidance mode, a U-turn mode, a reversing mode, or a normal passage mode; and controlling the unmanned operating equipment to perform mobile operation actions within the farm based on the target navigation module and the target operating mode.

[0150] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0151] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for unmanned operating equipment applied in a farm, characterized in that, include: Based on the current environmental information of the unmanned equipment, the current status and priority level of each sensor in the multi-source navigation system, the target navigation module is determined in the multi-source navigation system; The multi-source navigation system includes a differential positioning sensor, a geomagnetic sensor, an ultrasonic sensor, and an inertial unit sensor. The current operating area of ​​the unmanned equipment is determined based on the current environmental information, and a target obstacle avoidance component matching the current operating area is obtained. Based on the obstacle perception information collected in real time by the target obstacle avoidance component and the current working area, the target operation mode of the unmanned operation equipment is determined; The target operating modes include deceleration mode, stopping and yielding mode, detour and yielding mode, U-turn mode, reversing mode, or normal traffic mode; Based on the target navigation module and the target operation mode, the unmanned operation equipment is controlled to perform mobile operation actions within the farm.

2. The control method for unmanned operating equipment applied to a farm according to claim 1, characterized in that, The priority level of the geomagnetic sensor is higher than that of the ultrasonic sensor, and the priority level of the ultrasonic sensor is higher than that of the differential positioning sensor. The step of determining the target navigation module in the multi-source navigation system based on the current environmental information of the unmanned operating equipment, the current status and priority level of each sensor in the multi-source navigation system, includes: Based on the current environmental information, the system monitors changes in the current work area. When a change in the current work area is detected, the system controls the target navigation module to perform real-time switching between the differential positioning sensor, the geomagnetic sensor, the ultrasonic sensor, and the inertial unit sensor, based on the current state and the priority level. The switching operation includes: If the unmanned operating equipment is detected to have entered a transition area and the current state of the geomagnetic sensor is valid, the target navigation module will be switched to the geomagnetic sensor; wherein, the transition area is an intermediate connecting area with ground magnetic strips for the unmanned operating equipment to move from one area to another. If the unmanned equipment is detected to have entered the straight-line area inside the enclosure and the ultrasonic sensor is in an effective state, the target navigation module will be switched to the ultrasonic sensor. If the unmanned operating equipment is detected to have entered the reversing area within the enclosure and the inertial unit sensor is in an effective state, the target navigation module is switched to the inertial unit sensor. If the unmanned equipment is detected to have entered the area outside the enclosure and the differential positioning sensor is in an effective state, the target navigation module will be switched to the differential positioning sensor.

3. The control method for unmanned operating equipment applied to a farm according to claim 2, characterized in that, The method further includes: If the unmanned equipment is detected to have entered the transition area and the current state of the geomagnetic sensor is in a failed state, the sensor with the highest priority among the other sensors whose current state is valid will be identified as the target navigation module; the other sensors are the sensors in the multi-source navigation system other than the geomagnetic sensor.

4. The control method for unmanned operating equipment applied to a farm according to any one of claims 1-3, characterized in that, The step of acquiring the target obstacle avoidance component that matches the current work area includes: If the current working area is determined to be a straight-line area within the enclosure, the first obstacle avoidance component is identified as the target obstacle avoidance component; the first obstacle avoidance component includes a visual sensor and a non-optical image sensing sensor; the non-optical image sensing sensor includes at least one of a lidar sensor, a collision sensor, and a photoelectric sensor; If the current working area is determined to be a transition area or a reversing area within the enclosure, the second obstacle avoidance component is identified as the target obstacle avoidance component; the second obstacle avoidance component includes at least one of the collision sensor, the photoelectric sensor, and the lidar sensor; If the current working area is determined to be outside the enclosure, the third obstacle avoidance component is identified as the target obstacle avoidance component; the third obstacle avoidance component includes the lidar sensor.

5. The control method for unmanned operating equipment applied to a farm according to claim 4, characterized in that, The step of determining the target operating mode of the unmanned operation equipment based on the obstacle perception information collected in real time by the target obstacle avoidance component and the current operating area includes: If the current working area is determined to be the straight-line area, the presence of dynamically moving obstacles within the first preset range of the unmanned operating equipment is determined based on the obstacle perception information collected in real time by the non-optical image perception sensor. In the presence of the dynamically moving obstacle, the target operating mode is determined as the parking avoidance mode; If, in the absence of the dynamically moving obstacle, the obstacle perception information collected in real time by the visual sensor indicates that a biometric target exists within the second preset range of the unmanned operating equipment, then the target operation mode is determined to be the deceleration passage mode. If, based on the obstacle perception information, the obstacle perception information indicates that the biometric target does not exist within the second preset range, then the target operation mode is determined to be the normal passage mode.

6. The control method for unmanned operating equipment applied to a farm according to claim 5, characterized in that, The method further includes: When the current working area is determined to be the straight-ahead area, the visual sensor is used to collect image data of the road surface in front of the unmanned working equipment, and the image data is segmented to obtain ground area features; The tilt angle of the ground edge is detected based on the ground area features. When the tilt angle is detected to be greater than a preset angle threshold, it is determined that the unmanned operation equipment has entered the reversing area within the enclosure, and the vehicle mobility performance parameters of the unmanned operation equipment are obtained. If the vehicle mobility performance parameters indicate that the unmanned operating equipment has the conditions to complete the reversal within the reversal area, then the target navigation module is switched to the inertial unit sensor, and the target operating mode is determined to be the turning movement mode, so as to control the unmanned operating equipment to perform the turning action. If the vehicle mobility performance parameters indicate that the unmanned operating equipment does not have the conditions to complete a U-turn in the reversing area, the target operating mode is determined as the reversing movement mode, so as to control the unmanned operating equipment to perform the reversing action along the original route.

7. The control method for unmanned operating equipment applied to a farm according to any one of claims 1-3, characterized in that, The ultrasonic sensor includes a first ultrasonic sensor, a second ultrasonic sensor, and a third ultrasonic sensor. The first ultrasonic sensor, the second ultrasonic sensor, and the third ultrasonic sensor are arranged sequentially at intervals along the travel direction of the unmanned operating equipment, and the second ultrasonic sensor is located between the first ultrasonic sensor and the third ultrasonic sensor. The step of controlling the unmanned operation equipment to perform mobile operation actions within the farm according to the target navigation module and the target operation mode includes: When the target navigation module is the ultrasonic sensor, the first distance data between the unmanned operating equipment and the limit bar measured by the first ultrasonic sensor, the third distance data between the unmanned operating equipment and the limit bar measured by the third ultrasonic sensor, and the second distance data between the unmanned operating equipment and the limit bar measured by the second ultrasonic sensor are respectively acquired. Calculate the average value between the first distance data and the third distance data; If the difference between the average value and the second distance data is less than a preset deviation value, the correction control amount of the movement offset angle of the unmanned operation equipment is determined based on the distance difference between the first distance data and the third distance data, and the preset reference distance data, and the correction control amount of the movement speed of the unmanned operation equipment is determined based on the distance difference between the second distance data and the preset reference distance data. The motor speed of the unmanned operating equipment is adjusted according to the correction control amount of the moving offset angle and the correction control amount of the moving speed. Based on the adjusted motor speed and the target operating mode, the unmanned operating equipment is controlled to perform mobile operations within the farm.

8. The control method for unmanned operating equipment applied to a farm according to any one of claims 1-3, characterized in that, The step of controlling the unmanned operation equipment to perform mobile operation actions within the farm according to the target navigation module and the target operation mode includes: When the target navigation module is the geomagnetic sensor, the position of the ground magnetic stripe is detected by the geomagnetic sensor; The yaw angle of the unmanned operating equipment relative to the ground magnetic strip is calculated based on the position of the ground magnetic strip; The motor speed of the unmanned operating equipment is adjusted according to the yaw angle; Based on the adjusted motor speed and the target operating mode, the unmanned operating equipment is controlled to perform moving operations along the ground magnetic strip.

9. The control method for unmanned operating equipment applied to a farm according to any one of claims 1-3, characterized in that, The step of controlling the unmanned operation equipment to perform mobile operation actions within the farm according to the target navigation module and the target operation mode includes: When the target navigation module is the differential positioning sensor, the target trajectory point to be tracked is determined from a pre-constructed navigation movement point set; the navigation movement point set contains multiple trajectory points arranged according to the operation sequence of the unmanned operation equipment. Compare the current positioning coordinates of the unmanned operating equipment with the coordinates of the target trajectory point; The driving direction of the unmanned operation equipment is adjusted based on the comparison results; Based on the adjusted driving direction and the target operating mode, the unmanned operation equipment is controlled to perform mobile operation actions at each trajectory point in the navigation movement point set.

10. A control system for unmanned operating equipment applied in a farm, characterized in that, include: The first decision-making unit is used to determine the target navigation module in the multi-source navigation system based on the current environmental information of the unmanned operating equipment, the current status and priority level of each sensor in the multi-source navigation system; the multi-source navigation system includes differential positioning sensors, geomagnetic sensors, ultrasonic sensors and inertial unit sensors; The second decision-making unit is used to determine the current operating area of ​​the unmanned operation equipment based on the current environmental information, and to obtain a target obstacle avoidance component that matches the current operating area. The third decision-making unit is used to determine the target operation mode of the unmanned operation equipment based on the obstacle perception information collected in real time by the target obstacle avoidance component and the current operation area. The target operating modes include deceleration mode, stopping and yielding mode, detour and yielding mode, U-turn mode, reversing mode, or normal traffic mode; The control unit is used to control the unmanned operation equipment to perform mobile operation actions within the farm according to the target navigation module and the target operation mode.

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