427 results about "Agricultural robot" patented technology

The invention discloses an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

The invention relates to a multi-sensor cross-scene dynamic preferential fusion positioning and mapping method, and the method comprises the steps: obtaining the data of a plurality of sensors, and completing the unification of the time-space relation of the data of the plurality of sensors; processing the data, carrying out loopback detection on image key frame data acquired by a camera, constructing to obtain an I MU pre-integration factor, a visual inertial odometer factor, a laser radar odometer factor, a GPS inertial odometer factor, a UWB factor, a GNSS factor and a loopback detection factor, and adding the factors into a factor graph for optimization; a global positioning pose and a map are obtained; and optimizing the multi-sensor data fusion strategy based on a deep fuzzy neural network. According to the multi-sensor cross-scene dynamic preferential fusion positioning and mapping method provided by the invention, high-precision positioning and navigation of an agricultural robot in different scenes are realized through real-time fusion of various sensor data, and the problems of scene dependence and insufficient precision of an existing single sensor scheme are solved.

The invention provides an orchard agricultural robot navigation method, and belongs to the technical field of agricultural robots. The method comprises the following steps: constructing a priori map of an orchard; carrying out odometer initialization and rear end correction on the robot; carrying out ground segmentation and obstacle identification; constructing a cost map: constructing the cost map through static obstacles of a prior map and dynamic obstacles after ground segmentation, and realizing visualization of the obstacles; and path planning: assigning an initial point and an end point on the cost map, firstly realizing global path planning, bypassing an obstacle by using local path planning to realize an obstacle avoidance function when a dynamic obstacle appears in the advancing process, and performing real-time trajectory optimization and smooth control. According to the method, accurate positioning and autonomous navigation of the robot in a complex orchard environment can be realized; meanwhile, through the combination of global path planning and local path planning, the robot can flexibly process static and dynamic obstacles, and the robustness of path planning and the effectiveness of a navigation task are ensured.

The invention relates to the technical field of intelligent agricultural robots, and provides a multi-agent collaborative anti-collision picking method based on digital twinning and deep reinforcement learning, which comprises the following steps: constructing a digital twinning model of a picking scene, and generating environmental geometric parameters, agent kinetic parameters and fruit position parameters through three-dimensional point cloud reconstruction; acquiring environment state data in real time and inputting the environment state data into the digital twin model for space-time alignment processing to generate synchronous state data; based on the synchronous data, a collaborative strategy containing a collision avoidance priority matrix, a path planning sequence and a task allocation weight is generated through a deep reinforcement learning network; an action instruction set is generated according to the strategy, and multiple agents are controlled to execute a picking task after virtual-physical space bidirectional verification of the digital twin model. According to the invention, efficient collision avoidance and accurate picking of multiple agents in a dynamic environment can be realized, and the picking efficiency, safety and system robustness are improved.

The invention relates to the field of agricultural robots, and discloses an agricultural spraying robot system with visual navigation, and the system comprises a multi-mode sensing module, a dynamic environment modeling module, a time sequence prediction module, a multi-target path planning module, a spraying control module, and a hardware acceleration unit. The method comprises the steps that a dynamic semantic map is constructed through multi-modal perception, agricultural semantics and an attention mechanism are fused to predict an obstacle trajectory, a safe path is generated in combination with multi-objective optimization, the spraying amount is adjusted based on crop density, and real-time operation is ensured by means of hardware acceleration. According to the method, a dynamic semantic map is constructed through multi-modal perception, agricultural semantics is combined to enhance trajectory prediction, a safe path is generated by utilizing multi-objective optimization, the spraying amount is adaptively adjusted according to the crop density, and real-time control is realized by adopting heterogeneous hardware acceleration; the defects of a traditional agricultural robot in the aspects of navigation precision, obstacle avoidance safety, resource utilization and response speed are overcome, and the intelligent level of complex farmland operation is improved.

The invention relates to the technical field of agricultural robot motion control, in particular to a mechanical arm posture stable adjusting method based on vibration feedback, which comprises the following steps: S1, collecting a vibration signal of an end effector of a mechanical arm in real time; s2, frequency domain analysis and amplitude-phase decomposition are conducted on the vibration signals, and vibration characteristic parameters of the mechanical arm in the X / Y / Z axis direction are extracted; s3, generating a multi-axis vibration vector in combination with the terrain inclination angle data; s4, attitude compensation parameters of all the joints are calculated; s5, a mechanical arm joint angle adjusting instruction is generated according to the posture compensation parameters; and S6, circularly executing the steps S1 to S5 until the vibration amplitude is lower than a preset threshold value. According to the method, the multi-axis vibration characteristics are extracted in real time, the joint compensation angle is calculated through the anti-offset and anti-overturning matrix, rapid and stable attitude control of the mechanical arm in the high-frequency disturbance environment is achieved, and therefore the tail end positioning precision and the overall operation reliability are remarkably improved.

The invention provides a fruit stem clamping and shearing agricultural robot and a picking control method thereof.The robot comprises a control cabinet, a mechanical arm and a visual navigation system which are installed on a crawler-type chassis supporting platform, and an upper computer control system and a lower computer control system are installed in the control cabinet; and the tail end of the mechanical arm is provided with a movable end effector with an identifying and positioning system and a light supplementing device. The movable end effector can flexibly adapt to picking requirements and complex working conditions of different crops; the upper computer control system is combined with a depth camera and a laser radar to collect data, and a YOLOv8-MC model is utilized to perform identification and key point positioning on a target crop in an image, so that the crop identification precision in a complex environment is remarkably improved, the probability of missing collection and mistaken collection is reduced, and the damage to fruits is reduced; when the upper computer control system constructs a global map, depth correction and map optimization are carried out on illumination changes, and the path planning and operation accuracy of the robot is further improved.

The invention relates to the technical field of data processing and robot positioning, in particular to a weighted fusion agricultural robot positioning method and system based on deviation filtering, and the method comprises the steps: obtaining the state data of a real-time dynamic differential positioning system and an inertial measurement unit, obtaining the state priori estimation at the current moment, inputting a kinetic model, and obtaining an observation value; then introducing a deviation mean value based on a difference value between an observation value and an actual measurement value, correcting the state priori estimation based on the deviation mean value to obtain state posteriori estimation, and introducing a smoothing factor to adaptively adjust a weight factor based on a difference value of the state posteriori estimation of the real-time dynamic differential positioning system and the inertial measurement unit to obtain the state priori estimation. And the weight factor at the current moment is obtained, and adaptive weighted fusion processing is carried out, so that high-precision positioning of the agricultural robot can be realized, and the robustness and the anti-interference capability of the system are improved.

The invention relates to the field of intelligent agricultural robots, in particular to a multi-robot collaborative harvesting method and system for woody oil plants based on swarm intelligence, and provides efficient harvesting of the woody oil plants by constructing a distributed sensing network and a semantic enhanced digital twinborn model, and the system adopts a three-layer decision architecture to formulate a harvesting strategy. The working efficiency is improved through self-adaptive task allocation and collaborative harvesting, multiple robots share experience through federated learning, group collaborative learning is achieved, harvesting performance is optimized, the reliability of the system is improved by 85%, single-point fault risks do not exist, the recognition accuracy is improved by 50% through a multi-modal sensing system, and the system is suitable for popularization and application. And an innovative harvesting solution is provided for the field of intelligent agricultural robots.

The invention provides an agricultural robot navigation method based on masked residual Transformer quantum reinforcement learning, and the method is composed of a masked residual Transformer (MRT) network model and the Qua-SAC quantum reinforcement learning supporting the model. The input of the MRT is an original RGB image in a target state under polar coordinates and an original RGB image in a visual state, the feature extraction part is mainly composed of a layer normalization operation layer, a mask multi-head self-attention block, a multi-layer sensor block and a full connection layer, and through the design of the MRT network, target key features can be extracted. The problem of low data efficiency caused by decoupling of a sensing module and target information in an agricultural unstructured environment is solved, and scene representation is promoted to be more focused on target related features. Quantum convolution is introduced in the Qua-SAC quantum reinforcement learning, so that the accuracy and the calculation efficiency of Q value estimation are improved to learn an optimal navigation strategy, and the navigation performance of an agricultural robot in an agricultural non-institution environment is improved.

The invention discloses an agricultural robot control method, apparatus and device, and a storage medium. The method comprises the steps of obtaining a farmland environment image collected by an image collection device, and a basic attribute, a driving speed and a driving direction of an agricultural robot; inputting the farmland environment image into a pre-trained U-shaped semantic segmentation model based on a channel and a space attention mechanism, and outputting a feature image of a crop row in the farmland environment image; determining position information of the crop row closest to wheels of the agricultural robot according to the wheel track and the spatial position; converting the position information to a robot coordinate system according to the spatial position; the steering angular velocity of the agricultural robot is determined according to the position information converted to the robot coordinate system and the current running speed, and the agricultural robot is controlled to control the course angle of the robot according to the steering angular velocity. By means of the method, the operation efficiency of the agricultural robot is remarkably improved through the automatic crop row identification and control technology.

The invention relates to a tracking navigation method for positioning a crop center point based on laser radar and image fusion, and belongs to the technical field of navigation, and the method comprises the steps: carrying out the marking of a farmland image after distortion correction, generating a training sample, inputting the training sample into a U-net model, carrying out the training, obtaining a crop segmentation model, and carrying out the classification of crops and weeds; performing feature fusion on the filtered laser radar point cloud data and the farmland image after distortion correction to obtain crop point cloud with semantic information; calculating a crop center point according to the crop point cloud with the semantic information; and providing navigation for the agricultural robot by taking the crop center point as a preview point. The spatial structure information of the target crop is obtained through the laser radar and the camera, the semantic point cloud is generated through feature fusion, the crop center point is dynamically extracted from the point cloud to serve as a real-time preview target, a fixed preview distance mechanism in a tracking algorithm is replaced, centimeter-level precise navigation is achieved, and the precision of the target crop is improved. And the operation precision and the operation safety of the agricultural robot are greatly improved.

The invention provides an agricultural robot path navigation method based on a vision-language model, and the method comprises the steps: adjusting the constraint condition of path planning according to the real-time result of risk level evaluation, and if the risk level exceeds a preset threshold value, preferentially selecting a path for avoiding a high-risk region, and determining an optimized navigation route; a dynamic sensing capability module is driven through a refined operation instruction, environment feedback data in the operation process is continuously collected, whether sudden change occurs or not is judged, and if abnormity is detected, an updating mechanism of path planning adjustment is triggered; according to an updating mechanism of path planning adjustment, recalculating the matching degree of the navigation route and the behavior strategy, obtaining a newest operation execution scheme, and determining a final operation path and a final action sequence; and aiming at the final operation path and action sequence, generating a real-time execution log, recording whole-process data of emotion semantic recognition, operation situation modeling and dynamic sensing ability, and obtaining complete operation state tracking information.

The invention discloses a path planning method for an agricultural robot, and the method comprises the steps: building a reinforcement learning interaction environment based on a prior map, and segmenting a global environment through an intermediate target point, and designing a DQN algorithm network architecture; designing a DQN algorithm reward function based on a forward bow-shaped operation mode of the agricultural robot, wherein the reward function is composed of a center line area reward function and a target point reward function; setting a DQN algorithm action space and an exploration strategy by combining a neighborhood exploration mode of an A * algorithm; a priority experience playback mechanism is adopted to access and transfer sample data to design an evaluation network and a target network, and path planning and decision making of the agricultural robot are carried out. And a forward bow-shaped obstacle avoidance path is planned between the starting point and the ending point in the scanned environment, so that the agricultural robot can perform autonomous navigation tracking control. The line feed operation position can be automatically adjusted after the operation environment is switched, and the problem that the environment self-adaptive adjustment capability is insufficient in the automatic navigation process is solved.

The invention belongs to the technical field of agricultural robots, and discloses a visual perception digital twinborn cotton weeding robot identification obstacle avoidance method, which comprises the following specific steps: step 1, establishing a digital cotton and weed database, cotton field image information and cotton field environment information of real scenes in the same area under the condition of changing illumination in different time periods of the whole day are collected through a data collection system, and timestamps during collection and collected real-time data are transmitted to a cloud server. Through mutual cooperation of the data acquisition system, the cloud server and the virtual simulation platform, a cotton and weed virtual scene is established, through simulation of a cotton field and a weeding robot on the simulation platform, time-varying digital scene construction is perfected, and the time-varying digital scene construction efficiency is improved. The problem that an existing digital scene is not perfect in reduction of the time-varying process of the influence of illumination on the cotton field in the real environment is solved, and therefore the consistency of the virtual scene and the real environment and the weeding accuracy are further improved.

The invention discloses an agricultural scene three-dimensional reconstruction method based on multi-modal sensor fusion, and belongs to the technical field of three-dimensional modeling. The method comprises the following steps: acquiring multi-modal data of a target scene based on a tracked robot, wherein the multi-modal data comprises image data acquired by a binocular camera, point cloud data acquired by a laser radar, time sequence inertial data acquired by an inertial measurement unit and position data acquired by a Beidou positioning system; extracting different modal data features in the multi-modal data, and carrying out heterogeneous fusion on the different modal data features to obtain multi-modal data fusion features; and agricultural scene three-dimensional reconstruction is carried out by using a three-dimensional dynamic construction method based on space-time-semantic joint embedding. According to the invention, the environment scene modeling precision of the agricultural robot under the interference of muddy, raised dust and vibration can be improved.

The invention discloses a cotton field robot fusion navigation path generation method, and relates to the field of agricultural robot positioning navigation, and the method comprises the steps: 1, installing an RTK positioning module on a cotton field robot for position navigation; a deep learning visual camera is installed in front of the cotton field robot and used for recognizing and keeping lane lines, and visual navigation is achieved; a walking device, a turning device and an in-situ turning device are installed on the cotton field robot and used for achieving the control effect of walking, turning and in-situ turning. 2, constructing a visual navigation model based on semantic segmentation, and deploying the visual navigation model to an automatic driving system of the cotton field robot; step 3, dotting and marking land parcel information by the RTK dotting device, and importing the land parcel information into an automatic driving system of the cotton field robot; and step 4, starting the cotton field robot to walk and work in the sown cotton field, generating a fusion navigation path, and synchronously recording RTK position track coordinates. According to the invention, an operation path can be automatically planned on a sown standard cotton field.

The invention belongs to the technical field of agricultural robots, and relates to a pneumatic quantitative fertilization, powder fixation and deep fertilization robot and a working method thereof.The crawler chassis is provided with a material conveying assembly, a deep scarification fertilization assembly, an air compression spray explosion assembly and an automatic following assembly, and the material conveying assembly, the deep scarification fertilization assembly and the air compression spray explosion assembly are connected through a three-way valve; the material conveying assembly is used for providing fertilizer for the deep scarification and fertilization assembly, the air compression spray explosion assembly is used for providing compressed air for the deep scarification and fertilization assembly, the deep scarification and fertilization assembly is used for conveying air and fertilizer for soil, and the automatic following assembly is used for detecting and identifying people and obstacles in the environment; a discharging motor of the material conveying assembly drives a rotating shaft and a partition plate to rotate, a discharging baffle is opened, fertilizer enters a material storage bin from a discharging bin, and the material storage bin is communicated with the deep scarification and fertilization assembly. Quantitative discharging and fertilizing are achieved, discharging is conducted in a gas pushing mode, fertilizer particles are evenly distributed, and damage caused by low-frequency vibration to equipment is reduced; and the automatic following assembly automatically follows the working personnel to walk.

The invention relates to an agricultural robot, and discloses an animal husbandry pushing robot autonomous navigation method based on a laser radar, which comprises a microsecond-level multi-sensor time synchronization architecture based on an FPGA (Field Programmable Gate Array); in a navigation layer, seamless switching between the GNSS and the laser SLAM is realized by constructing a virtual fusion observation value by utilizing extended Kalman filtering and a self-adaptive dynamic weight algorithm; in the sensing layer, an RGB-I multi-mode model fused with laser reflectivity and a biological micro-motion detection technology are adopted, and feed and biological obstacles are accurately distinguished; and in the execution layer, flexible self-adaptive material pushing is achieved in combination with material groove edge fitting and force-position hybrid control, and the navigation precision, the operation quality and the safety of the material pushing robot in the all-weather and unstructured pasture environment are effectively improved.

The invention relates to the technical field of agricultural robot navigation, and discloses an agricultural robot autonomous navigation system based on a three-dimensional point cloud model.The agricultural robot autonomous navigation system comprises a three-dimensional point cloud collection module, a data processing center and a navigation control unit, robot path planning and obstacle avoidance are achieved by collecting environment data in real time and constructing a precise three-dimensional map, and the navigation efficiency is improved. The three-dimensional point cloud acquisition module acquires point cloud data and image information in an environment, and the data processing center adopts a multi-source data fusion algorithm, and performs dynamic environment perception and path optimization, so as to improve the robustness and the real-time performance of the navigation system. The system adopts multi-module cooperative control, realizes dynamic self-adaption of global and local paths, improves the flexibility and reliability of robot operation, combines a PID control algorithm with real-time feedback adjustment, accurately controls the advancing track of the robot, and ensures the operation precision and stability.

The invention discloses an agricultural robot control method based on multi-agent cooperation, and the method comprises the following steps: S1, collecting environment data of a farmland region, and carrying out the preprocessing of the environment data; s2, constructing a job task graph, and forming structured task graph data; s3, inputting the operation task graph into the graph neural network model, and performing task initialization on all agricultural robots; s4, sharing the local operation sub-graphs among the agricultural robots, and fusing all the local operation sub-graphs based on a graph neural network model; s5, executing a multi-agent cooperative task division operation, and generating a task allocation matrix; s6, after the agricultural robot receives the operation instruction, executing a path planning and obstacle avoidance algorithm; and S7, summarizing operation states of the agricultural robots in real time, and dynamically updating weight parameters of the graph neural network model according to operation feedback. Efficient cooperation and intelligent scheduling of the agricultural robot are realized, and the operation efficiency and the coverage rate are improved.

The invention relates to the technical field of agricultural robots, in particular to a ploughing and fertilizing integrated agricultural robot which comprises a machine shell, wheels, a ploughing roller and a ploughing blade. The device further comprises a material storing and discharging assembly, a material mixing assembly, a water tank, a spraying and cleaning assembly, a lifting control assembly and a transverse line reciprocating movement assembly. Wheels symmetrically distributed front and back are mounted on the left and right sides of the lower end of the shell; cylinders symmetrically distributed front and back are mounted at the upper end of the left connecting frame; the telescopic end of the lower side of the air cylinder penetrates through the left connecting frame to be fixedly connected with a damping shock absorber. The lower ends of the two damping shock absorbers are fixedly connected with a ploughing frame; a ploughing roller is rotationally connected into the ploughing frame; ploughing blades which are annularly distributed at equal intervals are mounted on the outer side of the ploughing roller; the plowing and fertilizing device has the functions of plowing and fertilizing, so that the agricultural production efficiency is effectively improved, the plowing assembly and the fertilizing assembly do not need to be disassembled and assembled repeatedly, abrasion of parts is reduced, the cleaning mechanism is further arranged, after the plowing and fertilizing device is used, the blades can be cleaned, and the cleaning mode is simpler and more convenient.

The invention relates to the technical field of robot control, and discloses a mechanical arm specified performance fault-tolerant control method based on a fuzzy logic system, and the method comprises the steps: building a kinematics and dynamics model and a fault model of an agricultural robot; establishing a relative error dynamic equation between the actual position and the expected position; designing a specified performance function, and constraining the tracking error within an expected range by using the specified performance function; on the basis, designing a virtual control law by using a backstepping method under the condition of meeting error constraints; designing an adaptive control law by adopting a fuzzy logic system based on an uncertain item, unknown environment interference and a fault model in the kinetic model; a specified performance controller is designed based on a virtual control law, fuzzy logic adaptive control and full-state feedback agricultural robot, and specified performance fault-tolerant control of the agricultural robot is realized. The stability proves that the tracking error of the agricultural robot converges to any small neighborhood near zero within fixed time.

The invention relates to the technical field of fault early warning, in particular to an agricultural robot self-learning fault early warning method based on multi-source data fusion, and the method comprises the steps: obtaining the operation data and environment data of an agricultural robot, and generating a multi-source fusion tensor through time sequence synchronization and normalization processing; constructing a neural network comprising a self-supervised check branch and a minimum decoupling branch, and generating a reconstruction tensor; mapping operation and environment vectors by using an independent encoder, stripping linear and nonlinear environment features through an antagonism prediction path established by an orthogonal penalty term and a gradient inversion layer, and generating operation features; and constructing a feature topological graph, calculating local relative density, and generating a fault early warning signal. According to the method, deep association of multi-source data is mined through self-supervised verification, the problem of false alarm caused by environmental noise in an unstructured environment is effectively solved by forcibly eliminating environmental interference in a feature space by using orthogonal constraint and antagonism training, and the robustness and accuracy of fault early warning are remarkably improved.

The invention discloses an agricultural robot laser strike positioning method and device based on a virtual mapping surface, and the method comprises the following steps: S1, constructing a virtual mapping surface for the projection of a coordinate system of a visual system through a calibration board in a to-be-operated scene, establishing a mapping function between projection coordinates on the virtual mapping surface and instruction coordinates of the laser galvanometer through data fitting; s2, dynamically compensating target coordinates acquired in real time according to a built-in attitude sensor of the visual system; and S3, projecting the target coordinate after dynamic compensation to the virtual mapping surface, converting the target coordinate into a preliminary instruction coordinate of the laser galvanometer according to the mapping function, and performing dynamic compensation on the preliminary instruction coordinate to obtain a final instruction coordinate to control the laser galvanometer to act. The calibration process is simple, the coordinate conversion calculation amount is small, and fluctuation generated in the robot movement process can be dynamically compensated to ensure the positioning accuracy.

The invention relates to the technical field of agricultural automatic driving, in particular to a coarse-to-fine agricultural aerial survey path generation method and system. The method comprises the following steps: acquiring an orthographic aerial survey image generated by aerial survey of an unmanned aerial vehicle; carrying out image segmentation on the obtained orthographic aerial survey image, and carrying out crude extraction to obtain an operable area and a crop area; according to different operation types, an operation path and a transportation path are planned in the workable area; and the operation path and the transportation path are input into the agricultural robot, and full-autonomous operation is achieved. The method is extremely high in efficiency, and is very suitable for unmanned vehicle operation deployment in orchards or farmlands with ultra-large planting areas.

The invention discloses an agricultural robot control method based on ROS and infrared obstacle avoidance. The method comprises the following steps: acquiring an environment image and an infrared signal around an agricultural robot and initial state data of an end effector; obtaining target data with obstacle information according to the environment image and the infrared signal; according to the target data with the obstacle information and the initial state data, through inverse Cartesian kinematics and motion trail measurement and calculation, a joint control instruction and a driving control instruction are generated respectively so as to control the agricultural robot to work; wherein the joint control instruction and the driving control instruction are respectively transmitted to the joint steering engine and the driving steering engine through the ROS. On one hand, based on mutual cooperation of the environment image and the infrared signal, image parameters can be further adjusted, and the accuracy and reliability of obstacle avoidance are improved; on the other hand, inverse Cartesian kinematics and motion trail calculation are adopted, the accuracy of coordinate analysis is improved, and the problems of motion trail disorder and the like are avoided.

The invention discloses a plant disease and insect pest monitoring robot and an application method thereof, and relates to the technical field of intelligent agricultural robotics.The plant disease and insect pest monitoring robot comprises a robot body, and a moving assembly is arranged on one side of the robot body; the identification module is mounted at the top of the robot body; the insect catching mechanism and the spraying mechanism are both slidably mounted on the moving assembly. Wherein the identification module is used for identifying leaves with plant diseases and insect pests, calculating the distance between the leaves with the plant diseases and insect pests and the identification module, and determining the positions of the plant diseases and insect pests according to the distance; the moving assembly is used for sequentially moving the insect catching mechanism and the spraying mechanism to the positions where diseases and insect pests are located according to the distance, and deinsectization and pesticide spraying treatment are sequentially conducted on the leaves; according to the device, the automatic detection process can be achieved, the target position can be accurately recognized in the complex environment, and accurate deinsectization and pesticide spraying are achieved.

Modular agricultural robot (1) for the automatic execution of field work comprising a left side panel module (2), a right side panel module (3) and a connecting module (5), wherein the connecting module (5) connects the left side panel module (2) and the right side panel module (3) and includes a suspension (12) for attaching an interchangeable work module (6), wherein each side panel module is equipped with at least one wheel unit (4) with a drive for the wheel unit (4) and at least one of the wheel units (4) is rotatable about the vertical axis, characterized in that the connecting module (5) is a telescopic mechanism comprising a central frame (7) and at least one extendable component, wherein the extendable component is movably attached to the central frame (7) at one end and to the side panel module at the other end.wherein the connecting module (5) further comprises a drive of the telescopic mechanism of the connecting module (5) for extending and retracting the extendable component, wherein the agro-robot (1) further comprises a control system (10) for controlling the drive of the telescopic mechanism and for controlling and rotating at least one wheel unit (4).

The invention relates to the technical field of agricultural robots, in particular to a fruit lossless picking end effector which comprises a connecting structure, a loading mechanism, a transmission mechanism and a cutting mechanism, the connecting structure is fixedly connected with the loading mechanism and used for providing a mounting foundation, and the transmission mechanism is assembled in the loading mechanism and used for providing power for the cutting mechanism. And the cutting mechanism is rotatably assembled on the loading mechanism, is in transmission connection with the transmission mechanism and is used for driving fingers to rotate to passively cut fruit stems of fruits in a self-adaptive manner, so that fruit picking is realized. The invention aims to enable an end effector to easily extend into a position shielded by branches and leaves of a fruit tree in a mode of driving fingers to rotate so as to support fruits at different positions, enable fruit stems of the supported fruits to be passively cut in a self-adaptive manner, realize fruit separation and improve the fruit picking success rate, and the whole structure is compact and small, and the fruit stems of the picked fruits are short.