Soil sampling system based on cooperation of unmanned aerial vehicle and robot dog

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a soil sampling system based on cooperation of an unmanned aerial vehicle and a robot dog, and the system comprises an investigation sampling unit which carries out the investigation of a target region through monitoring an unmanned aerial vehicle, and generates a sampling hotspot map; the cooperative control unit is used for distributing sampling point sets for at least two robot dogs according to the sampling hotspot map and planning a delivery path of the transportation unmanned aerial vehicle; the delivery sampling unit is used for delivering the robot dogs to initial positions in sequence by the transportation unmanned aerial vehicle, and the robot dogs autonomously move in a target area and complete soil sampling and storage; the monitoring adjustment unit monitors the unmanned aerial vehicle to provide environment monitoring and communication relay support in the whole sampling process, and a center console dynamically adjusts tasks according to the real-time state; and after sampling is completed, the transportation unmanned aerial vehicle recycles the robot dogs in sequence according to a planned recycling path and returns to the starting point. The soil sampling efficiency and the operation safety in a complex environment are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and more specifically, to a soil sampling system based on the collaboration of a drone and a robot dog. Background Technology

[0002] Soil sampling is a fundamental task in fields such as mineral exploration sampling. Traditional sampling methods mainly rely on manual labor to carry tools into the target area. In flat, open, and easily accessible areas, manual sampling is feasible. However, in complex or dangerous environments such as mountainous areas, swamps, polluted areas, and post-disaster sites, manual sampling faces problems such as low efficiency, poor safety, and insufficient data consistency, and it is difficult to achieve large-scale, high-density sampling coverage.

[0003] Furthermore, existing technologies lack systematic solutions for multi-machine collaboration and fully automated scheduling, especially in the "air-ground collaborative" operation mode, where efficient task planning, dynamic risk response, and equipment recovery mechanisms have not yet been formed, resulting in significant room for improvement in the overall system's operational efficiency, environmental adaptability, and task reliability.

[0004] Therefore, there is an urgent need for a soil sampling system that can adapt to complex terrain and environment, achieve efficient collaboration, and has intelligent scheduling and safety control capabilities to solve the problems of low efficiency, high risk, and difficulty in coverage in current sampling operations. Summary of the Invention

[0005] This application provides a soil sampling system based on the collaboration of drones and robotic dogs, solving the technical problems of low efficiency, poor safety, limited coverage, and reliance on manual intervention in traditional soil sampling methods in complex or hazardous environments. By constructing an air-ground collaborative intelligent system composed of monitoring drones, transport drones, and multiple robotic dogs, semi-automated operations are achieved, from area reconnaissance, task planning, automatic delivery, parallel sampling, dynamic monitoring to safe retrieval. This system can adapt to varying terrains and environments, significantly improving sampling efficiency, accuracy, and operational safety. It also supports large-scale, high-density soil sample collection, providing a reliable and efficient automated solution for applications such as environmental monitoring.

[0006] To achieve the above objectives, the present invention provides a soil sampling system based on the collaboration of a drone and a robotic dog, comprising:

[0007] The reconnaissance sampling unit uses monitoring drones to reconnoiter the target area and generate sampling heat maps; The collaborative control unit allocates sampling point sets to at least two robot dogs based on the sampling heat map and plans the delivery path of the transport drone; The sampling unit is delivered by a transport drone, which sequentially delivers each robot dog to its starting position. Each robot dog moves autonomously within the target area and completes soil sampling and storage. The monitoring and adjustment unit provides environmental monitoring and communication relay support for the monitoring drone throughout the sampling process, and the central control station dynamically adjusts the tasks according to the real-time status. After sampling is completed, the transport drone will retrieve each robot dog sequentially according to the planned retrieval path and return to the starting point.

[0008] Furthermore, by using surveillance drones to reconnoiter the target area and generate sampling heat maps, specifically including: The monitoring drone is equipped with a multispectral imager, lidar and thermal imager to perform aerial scanning of the target area and acquire terrain elevation data and surface feature data. The onboard data processing unit performs fusion analysis on the collected data, identifies the target area, and marks appropriate sampling points. Based on preset sampling density rules and terrain accessibility analysis, a sampling heat map containing the coordinate priorities of multiple sampling points is automatically generated and transmitted to the collaborative control unit.

[0009] Furthermore, the sampling point set is allocated, specifically including: Based on the location information and priority of each sampling point in the sampling heat map, and combined with the endurance and terrain traversal ability of each robot dog, a set of sampling points is assigned to each robot dog with the goal of minimizing the total path length and maximizing sampling efficiency. After allocating the sampling point set, the flight path of the transport drone is planned based on the spatial distribution of the robot dog's initial deployment location. The flight path is optimized with the shortest flight distance and the fewest take-offs and landings as the objectives. The collaborative control unit also synchronizes the planned delivery path and the task information of each robot dog's sampling point set to the control systems of the transport drone and each robot dog in real time.

[0010] Furthermore, each robot dog autonomously moves within the target area and completes soil sampling and storage, specifically including: After each robot dog lands, it autonomously plans its movement path and proceeds to each sampling point one by one, based on its built-in navigation system and perception module, combined with the sampling heat map and the allocated sampling point set. At each sampling point, the robot dog uses its robotic arm to operate the sampling drill to collect soil samples, and then stores the collected samples into the corresponding numbered test tubes in the storage box it carries. After completing the sampling task at all sampling points, each robot dog autonomously moves to the predetermined retrieval waiting position and sends a task completion signal to the collaborative control unit.

[0011] Furthermore, the built-in navigation system and perception module specifically include: The navigation system integrates a positioning module to provide the robot dog with high-precision position and attitude information; The environmental perception module includes at least a stereo vision camera, a lidar, and an obstacle avoidance sensor, used to perceive the surrounding terrain, obstacles, and sampling point markers in real time. The path planning module generates or adjusts local movement paths online based on real-time data from the positioning module and the environmental perception module, combined with the allocated sampling point set. The path planning module aims to optimize energy consumption and traffic safety. The data collected by the navigation system and the sensing module is uploaded to the monitoring and adjustment unit in real time or near real time for environmental monitoring and dynamic task adjustment.

[0012] Furthermore, it autonomously plans its movement path and proceeds to each sampling point one by one, specifically including: Each robot dog, based on its assigned set of sampling points, invokes a global path planning algorithm to generate an initial global path within the target area that sequentially traverses all sampling points. During the movement, each robot dog acquires local environmental information in real time through the perception module and synchronizes it to the central control station. The central control station dynamically adjusts the initial global path based on the local environmental information to avoid obstacles or difficult areas that appear in real time. The path planning aims to minimize energy consumption, shorten the time, and maximize terrain safety. The planned movement path is sent to the robot dog's motion control system for execution in real time.

[0013] Furthermore, the monitoring drones provide environmental monitoring and communication relay support throughout the sampling process, specifically including: The surveillance drone continuously collects environmental images of the target area using its onboard lidar, thermal imager, and multispectral camera, and transmits the data back to the central control station in real time. The central control console analyzes the environmental images, identifies emergencies or risks that affect the sampling operation, and generates corresponding task adjustment instructions. The adjustment instructions include at least one of the following: adjusting sampling points, suspending the operation, changing the sampling order, or emergency retrieval. The monitoring drone also serves as a communication relay node, establishing a stable communication link between the transport drone, the robot dog, and the central control station, ensuring the real-time and reliable transmission of commands and status data under complex terrain conditions.

[0014] Furthermore, the environmental images are analyzed to identify unforeseen circumstances or risks that may affect the sampling operation, specifically including: The collaborative control unit processes the environmental images in real time and uses image recognition algorithms to detect whether there are any new obstacles, unidentified personnel or animal activities in the target area; When new obstacles or activity targets are detected, the collaborative control unit determines that there is a sudden operational risk and synchronizes it to the central control console. The central control console triggers the corresponding early warning and task adjustment process according to the risk type and level.

[0015] Furthermore, based on the risk type and level, corresponding early warning and task adjustment processes are triggered, specifically including: The central control console has a built-in risk response rule base, which predefines the warning levels and task adjustment strategies corresponding to different risk types and levels. When a sudden operational risk is identified, the collaborative control unit first determines the warning level of the current risk based on the rule base, and sends a warning message to the monitoring and adjustment unit to activate an audible and visual alarm or an interface prompt. Based on the risk type and warning level, the collaborative control unit matches and executes the corresponding task adjustment strategy from the rule base.

[0016] Furthermore, the transport drones sequentially retrieve each robot dog according to the planned retrieval path, specifically including: Before or during the sampling task, the collaborative control unit plans a recovery path with the shortest total flight distance and a recovery sequence that connects with the delivery sequence, based on the sampling point set assigned to each robot dog and the recovery waiting position. During the recovery phase, the transport drones fly sequentially to the recovery waiting positions of each robot dog according to the recovery path. Upon arrival, the transport drone uses its onboard visual recognition system to accurately locate the ground robot dog and uses its onboard grasping or docking mechanism to complete the physical connection and locking with the robot dog. Once the connection is confirmed to be secure, the transport drone carrying the recovered robot dog flies to the next recovery point or returns directly to the starting point.

[0017] Compared with existing technologies, the advantages of this invention are as follows: By constructing an air-ground collaborative intelligent system composed of monitoring drones, transport drones, and multiple robotic dogs, semi-automated operations are achieved, from area reconnaissance, mission planning, automatic delivery, parallel sampling, dynamic monitoring to safe retrieval. This system can significantly improve soil sampling efficiency and operational safety in complex environments, adapt to varied terrain, support large-scale, high-density sampling, and enhance system reliability and adaptability through real-time environmental monitoring and intelligent risk response mechanisms, effectively solving the problems of low efficiency, high risk, and difficulty in coverage associated with traditional sampling methods. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic diagram of a protection-based automatic sludge scraper control system is shown in an embodiment of the present invention. Detailed Implementation

[0019] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0020] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0022] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0023] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0024] like Figure 1 As shown, an embodiment of the present invention discloses a soil sampling system based on the collaboration of a drone and a robot dog, comprising: 1. Reconnaissance and sampling unit: monitors drones to reconnoiter the target area and generate sampling heat maps; In some embodiments of the present invention, the target area is reconnoitered and a sampling heat map is generated by monitoring a drone, specifically including: The monitoring drone is equipped with a multispectral imager, lidar and thermal imager to perform aerial scanning of the target area and acquire terrain elevation data and surface feature data. The onboard data processing unit performs fusion analysis on the collected data, identifies the target area, and marks appropriate sampling points. Based on preset sampling density rules and terrain accessibility analysis, a sampling heat map containing the coordinate priorities of multiple sampling points is automatically generated and transmitted to the collaborative control unit.

[0025] In this embodiment, the preset sampling density rule is to dynamically set the number of sampling points and the distribution spacing per unit area according to the task type and the identification result of the target area, so as to ensure higher density sampling coverage in areas with significant changes in soil properties. In this embodiment, terrain accessibility analysis refers to comprehensively evaluating the slope, surface undulation, and obstacle distribution around each potential sampling point based on terrain elevation data obtained by lidar, and combining it with the robot dog's motion performance parameters to automatically filter and prioritize the sampling locations that the robot dog can safely and efficiently reach, thereby achieving a balance between the scientific nature of sampling and the feasibility of operation when generating sampling heat maps.

[0026] The beneficial effects of the above technical solution are: by monitoring drones equipped with multispectral imagers, lidar and thermal imagers for collaborative reconnaissance and data fusion, combined with preset sampling density rules and automated analysis based on terrain accessibility, the scientific nature of sampling point positioning and the rationality of task planning are significantly improved, ensuring the safety, efficiency and success rate of subsequent robot dog sampling operations.

[0027] 2. The collaborative control unit allocates sampling point sets to at least two robot dogs based on the sampling heat map and plans the delivery path of the transport drone; In some embodiments of the present invention, allocating a set of sampling points specifically includes: Based on the location information and priority of each sampling point in the sampling heat map, and combined with the endurance and terrain traversal ability of each robot dog, a set of sampling points is assigned to each robot dog with the goal of minimizing the total path length and maximizing sampling efficiency. After allocating the sampling point set, the flight path of the transport drone is planned based on the spatial distribution of the robot dog's initial deployment location. The flight path is optimized with the shortest flight distance and the fewest take-offs and landings as the objectives. The collaborative control unit also synchronizes the planned delivery path and the task information of each robot dog's sampling point set to the control systems of the transport drone and each robot dog in real time.

[0028] In this embodiment, the priority determination method for each sampling point in the sampling heat map is based on the degree of soil anomaly identified by multispectral imaging data, the importance level of the area where the point is located in the preset task objective, and the lack of historical sampling data for the point. Each sampling point is given a quantitative priority score. The higher the degree of anomaly in the suspected soil pollution area, the greater the weight of the key area preset by the task, or the scarcer the historical data of the point, the higher its priority score. In the subsequent task allocation, it will be included in the sampling point set and executed first.

[0029] In this embodiment, the flight path is planned by taking the initial deployment location of each robot dog as the necessary waypoint and the take-off and landing point of the transport drone as the starting and ending points of the path. An optimization model is constructed with the shortest total flight distance as the core objective and the number of take-offs and landings as the consideration. By adopting an improved Traveling Salesman Problem (TSP) solution algorithm, an efficient and orderly flight sequence is calculated under the spatial constraints of avoiding known no-fly zones and obstacles.

[0030] The beneficial effects of the above technical solution are as follows: By introducing a quantitative priority scoring mechanism based on multi-dimensional information, key areas and anomaly points are ensured to receive priority and sufficient sampling coverage, significantly improving the scientific value and data representativeness of the sampling operation. Simultaneously, based on the improved Traveling Salesman Problem (TSP) flight path planning strategy, the delivery path of the transport drone is optimized while strictly adhering to airspace safety constraints, significantly reducing invalid flight mileage and takeoffs / landings, thereby effectively reducing system energy consumption and task time, and improving the operational efficiency and economy of the entire collaborative operation system.

[0031] 3. Delivering sampling units: The transport drone delivers each robot dog to its starting position in sequence. Each robot dog moves autonomously within the target area and completes soil sampling and storage. In some embodiments of the present invention, each robot dog autonomously moves within a target area and completes soil sampling and storage, specifically including: After each robot dog lands, it autonomously plans its movement path and proceeds to each sampling point one by one, based on its built-in navigation system and perception module, combined with the sampling heat map and the allocated sampling point set. At each sampling point, the robot dog uses its robotic arm to operate the sampling drill to collect soil samples, and then stores the collected samples into the corresponding numbered test tubes in the storage box it carries. After completing the sampling task at all sampling points, each robot dog autonomously moves to the predetermined retrieval waiting position and sends a task completion signal to the collaborative control unit.

[0032] In this embodiment, the built-in navigation system and perception module specifically include: The navigation system integrates a positioning module to provide the robot dog with high-precision position and attitude information; The environmental perception module includes at least a stereo vision camera, a lidar, and an obstacle avoidance sensor, used to perceive the surrounding terrain, obstacles, and sampling point markers in real time. The path planning module generates or adjusts local movement paths online based on real-time data from the positioning module and the environmental perception module, combined with the allocated sampling point set. The path planning module aims to optimize energy consumption and traffic safety. The data collected by the navigation system and the sensing module is uploaded to the monitoring and adjustment unit in real time or near real time for environmental monitoring and dynamic task adjustment.

[0033] In this embodiment, the autonomous planning of the movement path and the movement to each sampling point are specifically included as follows: Each robot dog, based on its assigned set of sampling points, invokes a global path planning algorithm to generate an initial global path within the target area that sequentially traverses all sampling points. During the movement, each robot dog acquires local environmental information in real time through the perception module and synchronizes it to the central control station. The central control station dynamically adjusts the initial global path based on the local environmental information to avoid obstacles or difficult areas that appear in real time. The path planning aims to minimize energy consumption, shorten the time, and maximize terrain safety. The planned movement path is sent to the robot dog's motion control system for execution in real time.

[0034] In this embodiment, the global path planning algorithm employs an optimized search mechanism that integrates prior environmental information and task constraints. Its algorithm is based on an improved A... The core of the algorithm is a search algorithm that combines the spatial topological relationships between sampling points with the kinematic model of the robot dog to construct a weighted cost map. The working mechanism is as follows: starting from the current position of the robot dog, based on the priority order and geographical distribution of the sampling point set, on the preloaded terrain accessibility grid map, using the passage cost, estimated energy consumption and Euclidean distance as heuristic functions, iteratively searches for a global path sequence that connects all sampling points and has an approximately optimal total cost, providing an efficient and feasible basic trajectory for subsequent local real-time adjustments.

[0035] The beneficial effects of the above technical solution are: by integrating a global path planning algorithm with dynamic adjustments from the central control console, efficient, safe, and adaptive navigation of the robot dog in complex and unknown terrain is achieved. While ensuring complete coverage of the sampling task, the robot dog's movement efficiency and operational safety are significantly improved.

[0036] 4. Monitoring and Adjustment Unit: The monitoring drone provides environmental monitoring and communication relay support throughout the sampling process, and the central control station dynamically adjusts the tasks based on the real-time status. In some embodiments of the present invention, the monitoring drone provides environmental monitoring and communication relay support throughout the sampling process, specifically including: The surveillance drone continuously collects environmental images of the target area using its onboard lidar, thermal imager, and multispectral camera, and transmits the data back to the central control station in real time. The central control console analyzes the environmental images, identifies emergencies or risks that affect the sampling operation, and generates corresponding task adjustment instructions. The adjustment instructions include at least one of the following: adjusting sampling points, suspending the operation, changing the sampling order, or emergency retrieval. The monitoring drone also serves as a communication relay node, establishing a stable communication link between the transport drone, the robot dog, and the central control station, ensuring the real-time and reliable transmission of commands and status data under complex terrain conditions.

[0037] In this embodiment, the environmental imagery is analyzed to identify unforeseen circumstances or risks that may affect the sampling operation, specifically including: The collaborative control unit processes the environmental images in real time and uses image recognition algorithms to detect whether there are any new obstacles, unidentified personnel or animal activities in the target area; When new obstacles or activity targets are detected, the collaborative control unit determines that there is a sudden operational risk and synchronizes it to the central control console. The central control console triggers the corresponding early warning and task adjustment process according to the risk type and level.

[0038] In this embodiment, the corresponding early warning and task adjustment process triggered according to the risk type and level specifically includes: The central control console has a built-in risk response rule base, which predefines the warning levels and task adjustment strategies corresponding to different risk types and levels. When a sudden operational risk is identified, the collaborative control unit first determines the warning level of the current risk based on the rule base, and sends a warning message to the monitoring and adjustment unit to activate an audible and visual alarm or an interface prompt. Based on the risk type and warning level, the collaborative control unit matches and executes the corresponding task adjustment strategy from the rule base.

[0039] The beneficial effects of the above technical solution are as follows: By monitoring the drone to collect and transmit environmental images in real time, the collaborative control unit can quickly identify sudden risks based on intelligent algorithms and automatically trigger graded warnings and task adjustments according to a preset risk response rule base. Simultaneously, the monitoring drone, acting as a mobile communication relay node, effectively overcomes signal obstruction problems in complex terrain, ensuring the stability of commands and status data throughout the entire operation.

[0040] 5. Equipment recovery unit: After sampling is completed, the transport drone will sequentially recover each robot dog according to the planned recovery path and return to the starting point.

[0041] In this embodiment, the transport drone sequentially retrieves each robot dog according to the planned retrieval path, specifically including: Before or during the sampling task, the collaborative control unit plans a recovery path with the shortest total flight distance and a recovery sequence that connects with the delivery sequence, based on the sampling point set assigned to each robot dog and the recovery waiting position. During the recovery phase, the transport drones fly sequentially to the recovery waiting positions of each robot dog according to the recovery path. Upon arrival, the transport drone uses its onboard visual recognition system to accurately locate the ground robot dog and uses its onboard grasping or docking mechanism to complete the physical connection and locking with the robot dog. Once the connection is confirmed to be secure, the transport drone carrying the recovered robot dog flies to the next recovery point or returns directly to the starting point.

[0042] The beneficial effects of the above technical solution are: the collaborative control unit intelligently plans the shortest and most orderly recovery path based on the task position and recovery waiting point of each robot dog, and combined with the onboard visual recognition and grasping mechanism of the transport drone, it achieves accurate and efficient automated recovery, which significantly reduces the time cost and energy consumption of recovery operations, while avoiding the risks and uncertainties of manual intervention, and ensuring the rapid turnover of equipment and the overall operating efficiency of the system in multiple rounds of sampling tasks.

[0043] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0044] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0045] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A soil sampling system based on the collaboration of a drone and a robotic dog, characterized in that, include: The reconnaissance sampling unit uses monitoring drones to reconnoiter the target area and generate sampling heat maps; The collaborative control unit allocates sampling point sets to at least two robot dogs based on the sampling heat map and plans the delivery path of the transport drone; The sampling unit is delivered by a transport drone, which sequentially delivers each robot dog to its starting position. Each robot dog moves autonomously within the target area and completes soil sampling and storage. The monitoring and adjustment unit provides environmental monitoring and communication relay support for the monitoring drone throughout the sampling process, and the central control station dynamically adjusts the tasks according to the real-time status. After sampling is completed, the transport drone will retrieve each robot dog sequentially according to the planned retrieval path and return to the starting point.

2. The soil sampling system based on the collaboration of a drone and a robot dog according to claim 1, characterized in that, By monitoring drones to reconnoiter target areas and generate sampling heat maps, the specific methods include: The monitoring drone is equipped with a multispectral imager, lidar and thermal imager to perform aerial scanning of the target area and acquire terrain elevation data and surface feature data. The onboard data processing unit performs fusion analysis on the collected data, identifies the target area, and marks appropriate sampling points. Based on preset sampling density rules and terrain accessibility analysis, a sampling heat map containing the coordinate priorities of multiple sampling points is automatically generated and transmitted to the collaborative control unit.

3. A soil sampling system based on the collaboration of a drone and a robotic dog according to claim 1, characterized in that, The allocation of the sampling point set specifically includes: Based on the location information and priority of each sampling point in the sampling heat map, and combined with the endurance and terrain traversal ability of each robot dog, a set of sampling points is assigned to each robot dog with the goal of minimizing the total path length and maximizing sampling efficiency. After allocating the sampling point set, the flight path of the transport drone is planned based on the spatial distribution of the robot dog's initial deployment location. The flight path is optimized with the shortest flight distance and the fewest take-offs and landings as the objectives. The collaborative control unit also synchronizes the planned delivery path and the task information of each robot dog's sampling point set to the control systems of the transport drone and each robot dog in real time.

4. A soil sampling system based on the collaboration of a drone and a robotic dog according to claim 1, characterized in that, Each robot dog autonomously moves within the target area and completes soil sampling and storage, specifically including: After each robot dog lands, it autonomously plans its movement path and proceeds to each sampling point one by one, based on its built-in navigation system and perception module, combined with the sampling heat map and the allocated sampling point set. At each sampling point, the robot dog uses its robotic arm to operate the sampling drill to collect soil samples, and then stores the collected samples into the corresponding numbered test tubes in the storage box it carries. After completing the sampling task at all sampling points, each robot dog autonomously moves to the predetermined retrieval waiting position and sends a task completion signal to the collaborative control unit.

5. A soil sampling system based on the collaboration of a drone and a robot dog according to claim 4, characterized in that, The built-in navigation system and perception module specifically include: The navigation system integrates a positioning module to provide the robot dog with high-precision position and attitude information; The environmental perception module includes at least a stereo vision camera, a lidar, and an obstacle avoidance sensor, used to perceive the surrounding terrain, obstacles, and sampling point markers in real time. The path planning module generates or adjusts local movement paths online based on real-time data from the positioning module and the environmental perception module, combined with the allocated sampling point set. The path planning module aims to optimize energy consumption and traffic safety. The data collected by the navigation system and the sensing module is uploaded to the monitoring and adjustment unit in real time or near real time for environmental monitoring and dynamic task adjustment.

6. A soil sampling system based on the collaboration of a drone and a robot dog according to claim 4, characterized in that, Autonomously plan its movement path and proceed to each sampling point one by one, specifically including: Each robot dog, based on its assigned set of sampling points, invokes a global path planning algorithm to generate an initial global path within the target area that sequentially traverses all sampling points. During the movement, each robot dog acquires local environmental information in real time through the perception module and synchronizes it to the central control station. The central control station dynamically adjusts the initial global path based on the local environmental information to avoid obstacles or difficult areas that appear in real time. The path planning aims to minimize energy consumption, shorten the time, and maximize terrain safety. The planned movement path is sent to the robot dog's motion control system for execution in real time.

7. A soil sampling system based on the collaboration of a drone and a robot dog according to claim 1, characterized in that, The surveillance drone provides environmental monitoring and communication relay support throughout the sampling process, specifically including: The surveillance drone continuously collects environmental images of the target area using its onboard lidar, thermal imager, and multispectral camera, and transmits the data back to the central control station in real time. The central control console analyzes the environmental images, identifies emergencies or risks that affect the sampling operation, and generates corresponding task adjustment instructions. The adjustment instructions include at least one of the following: adjusting sampling points, suspending the operation, changing the sampling order, or emergency retrieval. The monitoring drone also serves as a communication relay node, establishing a stable communication link between the transport drone, the robot dog, and the central control station, ensuring the real-time and reliable transmission of commands and status data under complex terrain conditions.

8. A soil sampling system based on the collaboration of a drone and a robot dog according to claim 7, characterized in that, Analyze the environmental images to identify unforeseen circumstances or risks that may affect the sampling operation, specifically including: The collaborative control unit processes the environmental images in real time and uses image recognition algorithms to detect whether there are any new obstacles, unidentified personnel or animal activities in the target area; When new obstacles or activity targets are detected, the collaborative control unit determines that there is a sudden operational risk and synchronizes it to the central control console. The central control console triggers the corresponding early warning and task adjustment process according to the risk type and level.

9. A soil sampling system based on the collaboration of a drone and a robot dog according to claim 8, characterized in that, Based on the risk type and level, corresponding early warning and task adjustment processes are triggered, specifically including: The central control console has a built-in risk response rule base, which predefines the warning levels and task adjustment strategies corresponding to different risk types and levels. When a sudden operational risk is identified, the collaborative control unit first determines the warning level of the current risk based on the rule base, and sends a warning message to the monitoring and adjustment unit to activate an audible and visual alarm or an interface prompt. Based on the risk type and warning level, the collaborative control unit matches and executes the corresponding task adjustment strategy from the rule base.

10. A soil sampling system based on the collaboration of a drone and a robot dog according to claim 1, characterized in that, The transport drones will sequentially retrieve each robot dog according to the planned recovery path, specifically including: Before or during the sampling task, the collaborative control unit plans a recovery path with the shortest total flight distance and a recovery order that connects with the delivery order, based on the sampling point set assigned to each robot dog and the recovery waiting position. During the recovery phase, the transport drones fly sequentially to the recovery waiting positions of each robot dog according to the recovery path. Upon arrival, the transport drone uses its onboard visual recognition system to accurately locate the ground robot dog and uses its onboard grasping or docking mechanism to complete the physical connection and locking with the robot dog. Once the connection is confirmed to be secure, the transport drone carrying the recovered robot dog flies to the next recovery point or returns directly to the starting point.

Images