218 results about "Agricultural robot" patented technology

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 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 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 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 discloses an inter-ridge autonomous navigation method and system for an asparagus picking robot based on machine vision, and belongs to the technical field of agricultural automation. The method comprises the following steps: processing a field image acquired by a robot in real time by using a pre-trained lightweight inter-ridge path segmentation network to generate a path mask; extracting and smoothing left and right boundary points of the path based on the mask; fitting a middle navigation line; and finally, a control error is calculated according to the navigation line and the robot pose, and a steering instruction is generated to drive the robot to autonomously travel. Through lightweight model design and a robust path fitting algorithm, the problems that an existing visual navigation method is large in calculation amount, poor in real-time performance and insufficient in precision are solved, an efficient, accurate and easy-to-deploy autonomous navigation solution is provided for an agricultural robot, and the method is particularly suitable for complex field environments of ridge crops such as asparagus.

The invention discloses an orchard crawler differential robot trajectory tracking method based on adaptive incremental control, and relates to the technical field of agricultural robot control. The method comprises the following steps: receiving environmental data collected by a robot sensor, constructing a probability grid map of an orchard environment according to the environmental data, and aligning the probability grid map with a pre-constructed map inertial coordinate system; the course angle error and the distance error between the current position and a target point are calculated based on the pose of the robot, an error vector is formed, and the target point is dynamically planned according to the fruit maturity and reachability priority detected by a visual system and a laser radar point cloud. According to the method, coordinate transformation is not needed, and different from traditional tracking control, a control instruction is generated directly based on pose increment, the course and distance error of a target point is calculated directly based on an inertial coordinate system, and real-time matrix projection operation is avoided.

The invention discloses a tomato picking point positioning and feasibility analysis system based on RGB-D attitude estimation, and belongs to the technical field of agricultural robot visual perception, and the system comprises an RGB-D data collection module which is used for synchronously collecting an RGB image and a depth image of a tomato cluster; a data preprocessing module; a multi-scale feature extraction and fusion module; the multi-task prediction module comprises a detection and key point prediction head, a picking point depth regression prediction head and a picking feasibility analysis module which are arranged in parallel; the tomato cluster three-dimensional picking system has the advantages that synchronous output of tomato cluster structure attitude information, picking point three-dimensional positions and picking feasibility judgment is achieved, tomato cluster attitude sensing stability and picking point three-dimensional positioning precision are improved, and the tomato cluster three-dimensional picking system is high in practicability and high in practicability. And the robot is endowed with an intelligent picking decision-making capability, and the operation success rate of the tomato picking robot is improved.

The invention discloses a light-weight tea leaf tender shoot detection method and device for tea leaf picking, belongs to the technical field of image recognition and target detection, and aims to solve the problems of sensing and shielding of tender shoot targets for tea leaf picking. Comprising the steps of obtaining a to-be-detected image in a tea picking scene in real time; processing the to-be-detected image based on a pre-trained lightweight tender shoot detection model to obtain position information and a category identification result of the tender shoot small target; the training process of the lightweight tender shoot detection model comprises the following steps: acquiring an image data set containing a tea tender shoot target; performing data division on the image data set containing the tea tender shoot target to obtain a training set; and training a YOLO RGS detection model taking YOLOv8n as a basic network by using the training set to obtain a lightweight tea tender shoot detection model. The method can improve the perception level of tender shoot targets in a tea leaf picking scene, and provides efficient and stable visual support for intelligent equipment such as agricultural robots.

The invention relates to the technical field of agricultural robots, and discloses a shearing mechanism and a fruit picking end effector, the shearing mechanism comprises a mounting seat, a bionic structure, a shear assembly and a first driving assembly; the bionic structure comprises a plurality of bionic fingers which are mounted on the mounting seat at intervals, and connecting brackets are mounted on the bionic fingers; the scissor assemblies are installed between every two adjacent bionic fingers, each scissor assembly comprises a fixed blade, a movable blade and an elastic piece, the fixed blades are fixed to the connecting support, the middles of the movable blades are hinged to the middles of the fixed blades, and the elastic pieces are installed between the fixed blades and the movable blades; the first driving assembly is used for driving the movable blade to rotate relative to the fixed blade. According to the fruit picking device, the bionic structure is matched with the active shearing mode, the fruit stem can be easily sheared, and therefore the fruit picking efficiency is improved.

The invention relates to an agricultural inspection robot navigation decision planning system and a planning method thereof, and belongs to the technical field of agricultural robots, and the agricultural inspection robot navigation decision planning system comprises a multi-sensor fusion sensing module, a navigation decision planning module, a dynamic obstacle processing module, a transverse-longitudinal combined controller and a task management platform. The multi-sensor fusion sensing module comprises a laser radar, a visual sensor, a Beidou / GPS positioning module and an IMU and is used for collecting environment images, point cloud data and robot poses and recognizing crop row navigation lines through an improved YOLOX-Tiny model. According to the method, high-precision navigation can be achieved, high-precision tracking with the transverse error being smaller than or equal to 0.04 m and the longitudinal error being smaller than or equal to 0.06 m is achieved through transverse-longitudinal combined control and multi-sensor fusion positioning, the self-adaptive capacity is high, the reinforcement learning and path decomposition technology and a dynamic risk evaluation mechanism are combined, and the reliability of the system is improved. The robot can effectively deal with sudden obstacles and complex terrains in a farmland environment.

The invention relates to the technical field of agricultural robot path planning, and discloses an autonomous navigation and intelligent operation path planning method for an agricultural robot. The method comprises the following steps: delimiting a target area in a digital map according to an operation intention instruction, and carrying out multi-dimensional scene deconstruction to generate an operation scene map; real-time information is obtained through multi-source sensing hierarchical scanning and is fused with a scene map, and a three-dimensional environment dynamic model containing trafficability, operation value and dynamic risk weight is constructed. And the strategy decision engine outputs a global strategy skeleton through multi-target solution space exploration and compromise solution according to the model and the operation instruction. And under skeleton constraint, performing local fine path generation to obtain an executable track sequence of the robot, and deducing the executable track sequence into a driving control instruction. In the execution process, the environment model and the strategy framework are dynamically updated through a backtracking correction thread. According to the invention, intelligent self-adaptive operation of the agricultural robot in a complex farmland environment with consideration of safety, efficiency and operation quality is realized.

The invention discloses a suspension type laser weeding robot, and relates to the technical field of agricultural robots. The problems that an existing self-propelled laser weeding robot is complex in structure, large in weight, high in cost and short in endurance due to the fact that the robot is provided with a walking system and a power source, calibration is tedious due to non-coaxial arrangement of a vision system and a laser system, and aiming precision is prone to being interfered are solved. The suspension type laser weeding robot comprises a rack, a suspension device, a transmission system, a laser weeding module and an electric control system. The suspension device is connected with the rack and a tractor. The transmission system converts power of the tractor into electric energy. The laser weeding module comprises a shell, a connecting seat, a galvanometer system, a camera and an inclined optical filter; the light filter enables a refraction light path of the camera to coincide with a transmission light path of the galvanometer system, and coaxial aiming is achieved. The electric control system distributes and controls electric energy. The advancing power of the whole machine is provided by a tractor. The laser weeding device is used for realizing efficient and accurate laser weeding operation based on the power of the tractor.

The invention discloses a method for generating an intelligent autonomous navigation map of an agricultural robot, and relates to the technical field of agricultural robots, and the method comprises the steps: carrying out the image segmentation of a satellite map of a target farmland through an image segmentation model, so as to automatically recognize a farmland region, and rapidly extracting a farmland boundary contour through the edge curve fitting. Then a navigation path can be automatically generated based on the farmland boundary contour in combination with the basic attribute parameters of the target farmland, and an intelligent label is automatically added to obtain an intelligent autonomous navigation map for indicating the driving path and the working mode of the agricultural robot. The system realizes end-to-end automatic conversion from a satellite image to an intelligent autonomous navigation map, can adapt to diversified farmland environments with different topographic features, different crop types and different planting modes based on a deep learning computer vision algorithm, can meet the precision requirement of agricultural operation while ensuring the automation efficiency, and has a wide application prospect. And the method has strong generalization ability.

The invention discloses an agricultural robot trajectory planning method and system with optimal energy consumption under collision constraint. The method comprises the following steps: acquiring historical state sequence data and environmental map information of a target machine and peripheral machines in real time by a self-machine; identifying the operation intention of the target machine, and outputting a multi-modal prediction trajectory; the method comprises the following steps of: generating a global coarse reference track covering an operation area by taking completion of an own operation task as a main target; generating a space-time safety corridor by adopting a space-time inheritance strategy, and reserving a safety space for the dynamic obstacle; constructing an optimization problem cost function, and comprehensively considering reference trajectory cost, collision risk cost and energy consumption cost; and under the boundary constraint of the safety corridor and the kinematics constraint of the robot, solving the optimization problem by using a numerical optimization solver to obtain an optimized local trajectory. According to the method, safe, efficient and energy-saving collaborative operation can be realized by fusing multi-modal trajectory prediction of the dynamic obstacle, refining energy consumption modeling and risk perception planning.

The invention provides an agricultural robot group intelligent scheduling method, which comprises the following steps: performing visual semantic conversion on an operation behavior video acquired by each agricultural robot to obtain a corresponding semantic feature vector; according to the similarity between the plurality of semantic feature vectors corresponding to each agricultural robot and the semantic feature center vector corresponding to each agricultural robot, determining an operation state response value of each agricultural robot at the moment corresponding to each frame of image; according to the plurality of operation state response values corresponding to any two agricultural robots, a cooperative pressure difference value corresponding to any two agricultural robots is determined; and according to a plurality of operation state response values corresponding to any agricultural robot and the cooperative pressure difference values between the agricultural robot and other agricultural robots except the agricultural robot, determining a system overall scheduling loss value, and performing global scheduling on the agricultural robots according to the system overall scheduling loss value. Therefore, the better system cooperation capability is realized.

A method for learning a route of an autonomously driving agricultural robot involves providing an environmental map, driving along a route in the region of the environmental map from a starting point to an end point by manually controlling the driving robot, locating the driving robot while driving along the route with the aid of a sensor arranged on the driving robot, and recording coordinates of waypoints along the route based on the locating process that has been carried out. The route is validated by autonomously driving along the route taking the stored coordinates of the waypoints into account, wherein the route is manually confirmed and then the confirmed route is marked as being able to be driven along autonomously.

This invention relates to the field of agricultural robots, and more particularly to a self-propelled intelligent integrated robot for rotary tillage, ridging, and fertilization. The robot includes a frame and a walking device mounted on the frame. A hopper is located at the top of the frame, and a fertilization device for conveying fertilizer to the area below the frame is installed inside the hopper. A rotary tillage component and a ridging component are mounted in the lower middle of the frame. A drive device for synchronously driving the rotary tillage and ridging components is installed inside the frame. This invention integrates walking, rotary tillage, ridging, and fertilization functions into one unit, achieving intelligent integrated operation in the field. The synchronous driving of the rotary tillage and ridging components by the drive device ensures operational coordination, and precise control of the working depth is achieved through hydraulic tilt adjustment and sensor feedback, significantly improving the automation level and operational efficiency of agricultural production.

The application discloses a reconfigurable agricultural robot system based on a general platform and automatic recombination. The robot general mechanism of the system is composed of independent mechanical arms, actuators, a body main module and a motion module, and the modules are connected with each other through detachable general interface modules. The robot recombination mechanism is composed of a motion recombination module and arm-hand recombination modules, and is used for splitting, assembling and recombining the robot general mechanism. The two arm-hand recombination modules are symmetrically installed in the motion recombination module. The application can improve the comprehensive utilization efficiency and working efficiency of the agricultural robot, realize single robot covering multi-link tasks in agricultural production, strengthen the motion adaptability of the agricultural robot in different working environments, adapt to various terrain motion requirements, expand the working range of the agricultural robot, achieve multi-task and multi-thread operation targets, realize autonomous management of agricultural production, and efficiently guide agricultural operation with the aid of an intelligent system.

This invention provides a fruit stalk clamping and shearing agricultural robot and its harvesting control method. The robot includes a control cabinet, a robotic arm, and a visual navigation system mounted on a tracked chassis support platform. The control cabinet contains a host computer control system and a slave computer control system. A movable end effector with an identification and positioning system and a supplementary lighting device is installed at the end of the robotic arm. The movable end effector can flexibly adapt to the harvesting needs of different crops and complex working conditions. The host computer control system combines data collected by a depth camera and LiDAR, and uses the YOLOv8-MC model to identify target crops and their key points in the images, significantly improving the crop identification accuracy in complex environments, reducing the probability of missed or incorrect harvesting, and minimizing damage to the fruit. When constructing a global map, the host computer control system performs depth correction and map optimization for changes in illumination, further improving the robot's path planning and operational accuracy.

The invention discloses an inspection data monitoring method for an agricultural robot, and relates to the technical field of data processing, and the method comprises the following steps: S1, collecting temperature parameters of a plurality of fixed-point positions in a greenhouse at different moments through a sensor of the agricultural robot, and constructing a temperature parameter set for each fixed-point position; s2, generating a contribution weight for each fixed point position, and calculating the current dryness of each temperature parameter; and S3, determining the predicted temperature of each temperature sensor at the next moment according to the current dryness of each temperature parameter. The system not only can monitor the change of the temperature environment of the greenhouse in real time, but also has a temperature prediction function, can find and handle abnormal conditions in time, and ensures the normal operation of the greenhouse.

The invention discloses a cutting mechanical arm for a climbing robot, the climbing robot and an operation method, and relates to the technical field of agricultural robots. The cutting mechanical arm comprises a mounting base, a rotary base, a positioning arm assembly and a cutting assembly; the mounting base is used for being fixed on a climbing mechanism of the climbing robot; the rotary base is arranged on the mounting base; the positioning arm assembly is rotatably connected to the rotary base, the positioning arm assembly comprises at least two mutually connected positioning arms, and the positioning arms can relatively rotate on the horizontal plane; the cutting assembly comprises a cutting piece and a posture adjusting piece, the posture adjusting piece is arranged at the tail end of the positioning arm assembly, the cutting piece is installed on the posture adjusting piece, and the posture adjusting piece is used for adjusting the operation posture of the cutting piece. According to the cutting mechanical arm for the climbing robot, the structure of the mechanical arm can be simplified while it is guaranteed that target fruit stems or branches reach in all directions and are stably cut, and control convenience is improved.

The invention relates to the technical field of agricultural robots, in particular to an energy recovery braking system and method for a new energy pesticide spraying robot, and the method comprises the steps: carrying out the semantic analysis and formalization processing of real-time farmland working condition data, and generating multi-source constraint data containing a braking position, an energy recovery weight and a scene type; based on the braking position and the weight, an exponential decay function is adopted to construct a dynamic energy potential field matrix representing the energy recovery potential; taking the potential field as a driving source, and establishing a multi-source energy recovery dynamic model containing an energy flow admittance item and a loss diffusion item; a multi-source cooperative influence coefficient is calculated in combination with the robot energy system evaluation value and the current potential field intensity, a local energy allocation component is generated, and a dynamic model is fused to form a self-adaptive braking control field; and an energy distribution strategy vector field is constructed in combination with a control field and a scene type, a multi-source cooperative energy recovery scheme is generated through a dynamic probability decision, the multi-source cooperative energy recovery scheme is converted into a real-time braking instruction, and closed-loop energy optimization scheduling is realized.

The application provides an agricultural robot, and relates to the technical field of farming equipment.The agricultural robot comprises a farming device, a chassis, and an electric energy system and a walking device arranged on the chassis.The walking device comprises two crawler walking mechanisms and two walking motor assemblies, each walking motor assembly is drivingly connected with a corresponding crawler walking mechanism.The farming device comprises a PTO motor assembly and a mounted farming tool, the PTO motor assembly is drivingly connected with the mounted farming tool.The electric energy system comprises a range extender, a first battery and a second battery, the second battery is electrically connected with the range extender, and is used for providing electric energy for starting the range extender, the output end of the range extender is electrically connected with the first battery and the second battery, and is used for charging the first battery and the second battery.The first battery is electrically connected with the walking motor assemblies and the PTO motor assembly, and is used for supplying power to the walking motor assemblies and the PTO motor assembly.The application can prolong the endurance time of the agricultural robot.

The present application relates to the technical field of agricultural robot control, and particularly provides an anti-interference convergence method for an agricultural robot cluster based on interaction link dynamics. The method comprises obtaining state information and speed information of each interaction link, determining an interaction topology structure and a connection weight matrix between the interaction links; constructing an edge Laplacian matrix according to the connection weight matrix of the interaction links, and determining a control gain based on all non-zero eigenvalues of the edge Laplacian matrix; determining a control amount of each interaction link according to the state information and speed information of each interaction link, the state information and speed information of a neighbor interaction link, the control gain, and a random interference term; and controlling the state and speed of the corresponding interaction link according to the control amount of each interaction link. The method effectively suppresses the influence of external interference on the link state, guarantees the convergence of the link state, and improves the robustness and cooperative control ability in a complex airspace environment.

The invention belongs to the technical field of robots, provides an omni-directional moving chassis structure for an agricultural robot, and aims to solve the problem that adjustment lags due to the fact that an existing robot adopts a passive centroid adjustment mode. The mechanical arm comprises a machine body, a main shaft is installed in the middle in the machine body through a bearing, the main shaft is sleeved with a driven gear, the main shaft is rotationally connected with an obliquely-arranged mechanical arm base, a driving motor is further installed in the machine body, the driving motor is provided with a driving gear, and the main shaft is further provided with a mounting plate used for containing a battery. The battery and the mechanical arm base are located on the two opposite sides of the main shaft correspondingly. An active adjusting mode is adopted, and when the mechanical arm of the robot moves, automatic adjustment can be conducted, so that the mechanical arm of the robot synchronously adjusts the mass center of the robot in the operation process.

This utility model provides a power mechanism and an agricultural robot, relating to the field of power transmission technology. The power mechanism includes a first multi-stage gear set and a second multi-stage gear set disposed on both sides inside a gearbox. The first multi-stage gear set has a first input shaft and a first output shaft at both ends. A first motor is disposed on the outside of the gearbox, with its drive shaft passing through the gearbox and connected to the first input shaft. A first track drive wheel is also disposed on the outside of the gearbox, with its first output shaft passing through the gearbox and connected to the first track drive wheel. The second multi-stage gear set has a second input shaft and a second output shaft at both ends. A second motor is disposed on the outside of the gearbox, with its drive shaft passing through the gearbox and connected to one end of the second input shaft. A second track drive wheel is also disposed on the outside of the gearbox, with its second output shaft passing through the gearbox and connected to the second track drive wheel. This utility model can effectively improve the operating efficiency of a tractor.

The invention relates to the technical field of agricultural robots, and aims to provide a visual guidance multi-branch vacuum adsorption fruit picking manipulator and a picking strategy method so as to improve the picking success rate and real-time performance and reduce damage. According to the technical scheme, the visual guidance multi-branch vacuum adsorption fruit picking manipulator comprises a mechanical arm, an actuator arranged at the front end of the mechanical arm, a visual camera and a controller; the actuator comprises a main support, a motor, a clamping jaw mechanism and an adsorption mechanism, wherein the clamping jaw mechanism is used for grabbing fruits and driven by the motor, and the adsorption mechanism is used for adsorbing the fruits. The invention discloses a visual guidance multi-branch vacuum adsorption fruit picking strategy method. The method comprises the following steps: detecting fruit postures by a visual posture detection model; a picking priority queue is generated, and the mechanical arm aligns an actuator to the first target fruit; the vacuum generator generates negative pressure, and when the adsorption force reaches a stable threshold value, the actuator grabs the fruits; the mechanical arm controls the actuator to execute screwing and separating actions, and the target fruit is separated from the fruit tree.

The present application relates to the technical field of deep learning, in particular to an agricultural robot arm grabbing control method and system fusing deep learning vision, comprising: collecting a data set; based on the data set, obtaining a reserved occlusion image and its corresponding adjustment image; taking the reserved occlusion image and its corresponding adjustment image as an image control group, and training a self-encoding network by using the image control group to obtain a trained self-encoding network; freezing the network weight of the encoder, and fusing the feature map output by the encoder with the feature map of the corresponding level in the YOLO backbone network to obtain a fused network; training the fused network by using the data set to obtain a trained fruit recognition model; identifying the real-time fruit image collected by the robot arm based on the trained fruit recognition model, and controlling the robot arm to perform a grabbing operation. The present application can improve the recognition accuracy and robustness of fruits in a complex agricultural scene with serious branch and leaf occlusion.

The invention relates to an automatic picking device for agricultural planting and a method thereof, and belongs to the technical field of agricultural robots and automation. A base of the multi-degree-of-freedom mechanical arm is vertically fixed to the center position of the ground moving platform; the six-dimensional force / torque sensor is mounted at the tail end of a wrist joint of the multi-degree-of-freedom mechanical arm in series; the multi-mode sensing clamp holder is connected to the six-dimensional force / torque sensor, the multi-mode sensing clamp holder comprises a clamp holder base, the clamp holder base is connected with the six-dimensional force / torque sensor, and the multi-mode sensing clamp holder is connected with the six-dimensional force / torque sensor; and the stereoscopic vision camera is arranged at the front end of the gripper base, and the operation adaptability to fruits in different states is improved.