Motion planning and control method and system for sorting commodities by robot

Abstract

The invention relates to a motion planning and control method and system for sorting commodities by a robot. The method comprises the following steps: shooting an image to be sorted; mapping the to-be-sorted image to obtain the initial feature of each grid point in the three-dimensional grid; based on a feature correction method and a volume rendering method, calculating a camera depth map according to the initial feature of each grid point in the three-dimensional grid; correcting the camera depth map to obtain a corrected depth map; calculating pose information of the to-be-sorted commodities; and on the basis of an improved whale optimization algorithm, the movement track of the mechanical arm and the grabbing posture of the dexterous hand are planned according to the corrected depth map and the posture information. Based on a feature correction method and a volume rendering method, a camera depth map can be accurately calculated and corrected, and a dexterous hand can be controlled to quickly and accurately grab commodities; meanwhile, the method is simple, and the grabbing speed of the robot is greatly increased; in addition, the improved whale optimization algorithm is adopted, and it can be guaranteed that the target object is accurately grabbed.

Description

Technical Field

[0001] The present invention relates to the field of robot control, and in particular to a motion planning and control method and system for robot sorting commodities. Background Art

[0002] With the rapid development of science and technology, robotics technology has penetrated into various industries. Robotic sorting systems are usually composed of robot bodies, sensors, control systems, and computer vision technologies. During the sorting process, the robot obtains cargo information through sensors, uses computer vision technology to identify the type and location of cargo, and then drives the robot body through the control system to accurately grasp and sort. The entire process is highly automated and intelligent, greatly improving sorting efficiency and accuracy.

[0003] In the prior art, robots generally use YoloV8 and DenseFusion for motion planning and control in the process of grasping goods. The main function of YoloV8 is to accurately identify goods and help robots accurately identify different categories of goods. The main function of DenseFusion is to perform 6D pose estimation of objects, helping robots to accurately locate and grasp objects.

[0004] However, the existing combination method of Yolo v8 and Dense Fusion for robot motion planning and control matches the target information with the object of the known three-dimensional model after detecting the target pose, and obtains the grasping pose through matching. However, the target objects in actual scenes are complex and changeable, and the camera carried by the robot can usually only obtain the target information of a single view, which leads to a low matching success rate, and the trajectory planning algorithm is prone to fall into local optimization, so it is impossible to achieve accurate and fast grasping of the target object. Summary of the invention

[0005] The present invention provides a motion planning and control method and system for robot sorting commodities to solve at least one of the above-mentioned technical problems.

[0006] The technical solution of the present invention to solve the above technical problem is as follows: a motion planning and control method for robot sorting goods, comprising:

[0007] S1, using a monocular camera to photograph the goods to be sorted in the sorting area to obtain an image of the goods to be sorted;

[0008] S2, mapping the image to be sorted based on a three-dimensional grid to obtain initial features of each grid point in the three-dimensional grid;

[0009] S3, calculating a camera depth map according to initial features of each grid point in the three-dimensional grid based on a feature correction method and a volume rendering method;

[0010] S4, correcting the camera depth map according to the current position information of the dexterous hand at the end of the robotic arm and the position information of the monocular camera to obtain a corrected depth map;

[0011] S5, using adaptive technology, improves the whale optimization algorithm by introducing improved potential field factors and virtual obstacles to obtain an improved whale optimization algorithm;

[0012] S6, using a posture estimation method to calculate the posture information of the goods to be sorted from the image to be sorted; based on the improved whale optimization algorithm, the motion trajectory of the robotic arm and the grasping posture of the dexterous hand are planned according to the corrected depth map and the posture information, so as to control the dexterous hand at the end of the robotic arm to sort the goods to be sorted in the sorting area.

[0013] Based on the above technical solution, the present invention can also be improved as follows.

[0014] Further, the S2 is specifically:

[0015] S21, extracting features from the image to be sorted to obtain a two-dimensional feature map;

[0016] S22, using the internal and external parameters of the monocular camera, projecting each grid point in the three-dimensional grid into the two-dimensional feature map to obtain initial features of each grid point in the three-dimensional grid.

[0017] Furthermore, in S21, a CNN convolutional neural network is specifically used to extract features of the image to be sorted.

[0018] Further, the S3 is specifically:

[0019] S31, inputting the initial features of each grid point in the three-dimensional grid into an occupancy probability prediction model to obtain the initial occupancy probability of each grid point in the three-dimensional grid;

[0020] S32, calculating a correction parameter of an initial feature of each grid point in the three-dimensional grid according to the initial occupancy probability of each grid point in the three-dimensional grid;

[0021] S33, correcting the initial features of each grid point in the three-dimensional grid based on the correction parameters of the initial features of each grid point in the three-dimensional grid, to obtain the corrected features of each grid point in the three-dimensional grid;

[0022] S34, judging whether to continue the correction process according to the correction characteristics of each grid point in the three-dimensional grid; if so, executing S35; if not, executing S36;

[0023] S35, taking the correction feature of each grid point in the three-dimensional grid as the initial feature of each grid point in the three-dimensional grid and returning to S31 to execute iteratively in a loop until the correction feature of each grid point in the three-dimensional grid meets a preset condition;

[0024] S36, inputting the correction feature of each grid point in the three-dimensional grid into the occupancy probability prediction model to obtain the occupancy probability of each grid point in the three-dimensional grid;

[0025] Based on a volume rendering method, the image to be sorted is rendered according to the occupancy probability of each grid point in the three-dimensional grid to obtain a camera depth map.

[0026] Further, in S31, the occupancy probability prediction model is specifically an Occupancy model.

[0027] Further, in S32, the calculation formula of the refinement degree is:

[0028]

[0029] Among them, T i represents the initial occupancy probability of the i-th grid point in the three-dimensional grid, δ represents a preset refinement parameter, and P represents the refinement degree.

[0030] Further, the S33 is specifically:

[0031] Constructing a feature interpolation model, and adjusting model parameters of the feature interpolation model according to correction parameters of initial features of each grid point in the three-dimensional grid;

[0032] Substituting the correction features of each grid point in the three-dimensional grid into the feature interpolation model after the model parameters are adjusted to output a feature function;

[0033] The coordinates of each grid point in the three-dimensional grid are substituted into the characteristic function to obtain the correction features of each grid point in the three-dimensional grid.

[0034] Further, the S34 is specifically:

[0035] Calculating the average value of the correction features of all grid points in the three-dimensional grid to obtain the average correction feature;

[0036] Performing difference processing on the correction feature of each grid point in the three-dimensional grid and the average correction feature respectively, to obtain the correction feature difference of each grid point in the three-dimensional grid;

[0037] Determine whether the corrected feature difference of each grid point in the three-dimensional grid is within a preset feature difference threshold range, and count the point values ​​of the grid points whose corrected feature difference is not within the preset feature difference threshold range;

[0038] Determine whether to continue the calibration process according to the point value; if yes, execute S35; if no, execute S36;

[0039] If the point value is greater than or equal to the preset point value, S35 is executed; if the point value is less than the preset point value, S36 is executed.

[0040] Further, the S4 is specifically:

[0041] A camera coordinate system is constructed according to the position information and internal and external parameters of the monocular camera, and a dexterous hand coordinate system is constructed according to the current position information and posture of the dexterous hand at the end of the robotic arm;

[0042] Calculating a correction matrix between the monocular camera and the dexterous hand according to a conversion relationship between the camera coordinate system and the dexterous hand coordinate system;

[0043] The camera depth map is corrected according to the correction matrix to obtain a corrected depth map.

[0044] Based on the above-mentioned motion planning and control method for a robot to sort goods, the present invention also provides a motion planning and control system for a robot to sort goods.

[0045] A motion planning and control system for a robot to sort commodities, comprising a monocular camera, a mechanical arm and a toolbox arranged in sequence from top to bottom; the monocular camera is suspended above the toolbox through a curved pipe; the mechanical arm is installed on the toolbox, and a suction-grasping integrated dexterous hand is installed on the mechanical arm; a display is provided on the toolbox, four rolling pulleys are provided at the bottom of the toolbox, and an industrial computer is provided inside the toolbox;

[0046] The monocular camera is used to photograph the commodities to be sorted in the sorting area to obtain images to be sorted;

[0047] The industrial computer is used to map the image to be sorted based on a three-dimensional grid to obtain the initial features of each grid point in the three-dimensional grid; based on a feature correction method and a volume rendering method, a camera depth map is calculated according to the initial features of each grid point in the three-dimensional grid; the camera depth map is corrected according to the current position information of the dexterous hand at the end of the robotic arm and the position information of the monocular camera to obtain a corrected depth map; an adaptive technology is used to improve the whale optimization algorithm by introducing an improved potential field factor and a virtual obstacle to obtain an improved whale optimization algorithm; a posture estimation method is used to calculate the posture information of the goods to be sorted from the image to be sorted; based on the improved whale optimization algorithm, the motion trajectory of the robotic arm and the grasping posture of the dexterous hand are planned according to the corrected depth map and the posture information, so as to control the dexterous hand at the end of the robotic arm to sort the goods to be sorted in the sorting area.

[0048] The beneficial effects of the present invention are as follows: a motion planning and control method and system for a robot sorting goods in the present invention maps a two-dimensional image into a three-dimensional space to obtain three-dimensional features, and then based on a feature correction method and a volume rendering method, a camera depth map can be accurately calculated. Since the dexterous hand and the camera are in different coordinate systems, the camera depth map is corrected to obtain a corrected depth map relative to the dexterous hand, so that the dexterous hand can be controlled to quickly and accurately grasp the goods; at the same time, the method of the present invention is simple, so it can save computer computing memory, improve the computing speed, and thus greatly improve the grasping speed of the robot; in addition, the present invention adopts an improved whale optimization algorithm, which can overcome the problem that the trajectory planning algorithm is prone to fall into local optimization, thereby improving the planning accuracy, and thus ensuring the accurate grasping of the target object. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a motion planning and control method for a robot to sort commodities according to the present invention;

[0050] Figure 2 To invent a flow chart of an improved whale optimization algorithm in the motion planning and control method of a robot sorting commodity;

[0051] Figure 3 This is a structural diagram of a motion planning and control system for a robot to sort goods according to the present invention.

[0052] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0053] 1. Monocular camera; 2. Robotic arm; 3. Dexterous hand; 4. Toolbox; 5. Bending pipe; 6. Display; 7. Pulley. DETAILED DESCRIPTION

[0054] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0055] like Figure 1 A motion planning and control method for a robot to sort goods is shown, comprising:

[0056] S1, using a monocular camera to photograph the goods to be sorted in the sorting area to obtain an image of the goods to be sorted;

[0057] S2, mapping the image to be sorted based on a three-dimensional grid to obtain initial features of each grid point in the three-dimensional grid;

[0058] S3, calculating a camera depth map according to initial features of each grid point in the three-dimensional grid based on a feature correction method and a volume rendering method;

[0059] S4, correcting the camera depth map according to the current position information of the dexterous hand at the end of the robotic arm and the position information of the monocular camera to obtain a corrected depth map;

[0060] S5, using adaptive technology, improves the whale optimization algorithm by introducing improved potential field factors and virtual obstacles to obtain an improved whale optimization algorithm;

[0061] S6, using a posture estimation method to calculate the posture information of the goods to be sorted from the image to be sorted; based on the improved whale optimization algorithm, the motion trajectory of the robotic arm and the grasping posture of the dexterous hand are planned according to the corrected depth map and the posture information, so as to control the dexterous hand at the end of the robotic arm to sort the goods to be sorted in the sorting area.

[0062] In the present invention, a monocular camera is used for shooting, which can avoid image distortion caused by shooting with multiple cameras, thereby saving the distortion correction process and improving the processing speed.

[0063] In some embodiments, S2 is specifically:

[0064] S21, extracting features from the image to be sorted to obtain a two-dimensional feature map;

[0065] S22, using the internal and external parameters of the monocular camera, projecting each grid point in the three-dimensional grid into the two-dimensional feature map to obtain initial features of each grid point in the three-dimensional grid.

[0066] Among them, in S21, the CNN convolutional neural network is specifically used to extract features of the image to be sorted.

[0067] The present invention can quickly obtain three-dimensional features by projecting three-dimensional points in a three-dimensional grid onto a two-dimensional feature map.

[0068] In some embodiments: S3 is specifically:

[0069] S31, inputting the initial features of each grid point in the three-dimensional grid into an occupancy probability prediction model to obtain the initial occupancy probability of each grid point in the three-dimensional grid;

[0070] S32, calculating a correction parameter of an initial feature of each grid point in the three-dimensional grid according to the initial occupancy probability of each grid point in the three-dimensional grid;

[0071] S33, correcting the initial features of each grid point in the three-dimensional grid based on the correction parameters of the initial features of each grid point in the three-dimensional grid, to obtain the corrected features of each grid point in the three-dimensional grid;

[0072] S34, judging whether to continue the correction process according to the correction characteristics of each grid point in the three-dimensional grid; if so, executing S35; if not, executing S36;

[0073] S35, taking the correction feature of each grid point in the three-dimensional grid as the initial feature of each grid point in the three-dimensional grid and returning to S31 to execute iteratively in a loop until the correction feature of each grid point in the three-dimensional grid meets a preset condition;

[0074] S36, inputting the correction feature of each grid point in the three-dimensional grid into the occupancy probability prediction model to obtain the occupancy probability of each grid point in the three-dimensional grid;

[0075] Based on a volume rendering method, the image to be sorted is rendered according to the occupancy probability of each grid point in the three-dimensional grid to obtain a camera depth map.

[0076] Specifically, in S31, the occupancy probability prediction model is specifically an Occupancy model.

[0077] Specifically, in S32, the calculation formula of the refinement degree is:

[0078]

[0079] Among them, T i represents the initial occupancy probability of the i-th grid point in the three-dimensional grid, δ represents a preset refinement parameter, and P represents the refinement degree.

[0080] Specifically, the S33 is as follows:

[0081] Constructing a feature interpolation model, and adjusting model parameters of the feature interpolation model according to correction parameters of initial features of each grid point in the three-dimensional grid;

[0082] Substituting the correction features of each grid point in the three-dimensional grid into the feature interpolation model after the model parameters are adjusted to output a feature function;

[0083] The coordinates of each grid point in the three-dimensional grid are substituted into the characteristic function to obtain the correction features of each grid point in the three-dimensional grid.

[0084] Specifically, the S34 is specifically:

[0085] Calculating the average value of the correction features of all grid points in the three-dimensional grid to obtain the average correction feature;

[0086] Performing difference processing on the correction feature of each grid point in the three-dimensional grid and the average correction feature respectively, to obtain the correction feature difference of each grid point in the three-dimensional grid;

[0087] Determine whether the corrected feature difference of each grid point in the three-dimensional grid is within a preset feature difference threshold range, and count the point values ​​of the grid points whose corrected feature difference is not within the preset feature difference threshold range;

[0088] Determine whether to continue the calibration process according to the point value; if yes, execute S35; if no, execute S36;

[0089] If the point value is greater than or equal to the preset point value, S35 is executed; if the point value is less than the preset point value, S36 is executed.

[0090] Based on the feature correction method and the volume rendering method, the present invention can accurately calculate the camera depth map, laying a foundation for subsequently improving the grasping accuracy.

[0091] In some embodiments, S4 is specifically:

[0092] A camera coordinate system is constructed according to the position information and internal and external parameters of the monocular camera, and a dexterous hand coordinate system is constructed according to the current position information and posture of the dexterous hand at the end of the robotic arm;

[0093] Calculating a correction matrix between the monocular camera and the dexterous hand according to a conversion relationship between the camera coordinate system and the dexterous hand coordinate system;

[0094] The camera depth map is corrected according to the correction matrix to obtain a corrected depth map.

[0095] In the present invention, since the dexterous hand and the camera are in different coordinate systems, the camera depth map is corrected to obtain a corrected depth map relative to the dexterous hand, thereby controlling the dexterous hand to quickly and accurately grasp the goods.

[0096] In S5 of the present invention, adaptive technology is used to automatically adjust the algorithm convergence factor to improve the convergence speed of the algorithm; an improved potential field factor is used to realize the dynamic obstacle avoidance capability of the algorithm through the effects of gravity and repulsion; virtual obstacles are used to avoid the local optimization trap of the algorithm, so as to achieve the purpose of capturing unreachable targets and local optimal solutions.

[0097] The flowchart of the improved whale optimization algorithm is as follows Figure 2 shown.

[0098] Among them, the formula for population initialization is: i =lb+P×(ub-lb),X i represents the position of individual i, lb and ub represent the upper and lower bounds of the search space respectively, and P represents a random number between 0 and 1. In the stage of surrounding the prey, X(t+1)=X * (t)-A×D,A=2×a×Pa,D=2×P×X * (t)-X(t); t is the current iteration number, A and C are coefficients, X is the position of the current solution, X * is the position of the current optimal solution, and a gradually decreases to 0 during the iteration process.

[0099] Based on the above-mentioned motion planning and control method for a robot to sort goods, the present invention also provides a motion planning and control system for a robot to sort goods.

[0100] like Figure 3 As shown, a motion planning and control system for a robot to sort goods comprises a monocular camera 1, a mechanical arm 2 and a toolbox 4 arranged in sequence from top to bottom; the monocular camera 1 is suspended above the toolbox 4 through a curved pipe 5; the mechanical arm 2 is mounted on the toolbox 4, and a suction-grasping integrated dexterous hand 3 is mounted on the mechanical arm 2; a display 6 is provided on the toolbox 4, four rolling pulleys 7 are provided at the bottom of the toolbox 4, and an industrial computer is provided inside the toolbox 4;

[0101] The monocular camera 1 is used to photograph the commodities to be sorted in the sorting area to obtain images to be sorted;

[0102] The industrial computer is used to map the image to be sorted based on a three-dimensional grid to obtain the initial features of each grid point in the three-dimensional grid; based on a feature correction method and a volume rendering method, a camera depth map is calculated according to the initial features of each grid point in the three-dimensional grid; the camera depth map is corrected according to the current position information of the dexterous hand at the end of the robotic arm and the position information of the monocular camera to obtain a corrected depth map; an adaptive technology is used to improve the whale optimization algorithm by introducing an improved potential field factor and a virtual obstacle to obtain an improved whale optimization algorithm; a posture estimation method is used to calculate the posture information of the goods to be sorted from the image to be sorted; based on the improved whale optimization algorithm, the motion trajectory of the robotic arm 2 and the grasping posture of the dexterous hand 3 are planned according to the corrected depth map and the posture information, so as to control the dexterous hand 3 at the end of the robotic arm 2 to sort the goods to be sorted in the sorting area.

[0103] In the motion planning and control system of a robot for sorting commodities of the present invention, a monocular camera 1 recognizes commodity information and transmits it to a display 6, and then the industrial computer inside the toolbox 4 and the power supply of the dexterous hand control the mechanical arm to sort the commodities on the conveyor belt to the appropriate position. Four rolling pulleys 7 are provided at the bottom of the toolbox 4 for easy movement. The present invention integrates a camera, a mechanical arm, a dexterous hand, and a display, and has the characteristics of compact structure, space saving, and easy movement. It can complete the function of taking various commodities in unmanned supermarkets, replacing manual labor to improve production efficiency.

[0104] The present invention provides a motion planning and control method and system for a robot to sort goods. The present invention provides a motion planning and control method and system for a robot to sort goods. By mapping a two-dimensional image into a three-dimensional space to obtain three-dimensional features, a camera depth map can be accurately calculated based on a feature correction method and a volume rendering method. Since the dexterous hand and the camera are in different coordinate systems, the camera depth map is corrected to obtain a corrected depth map relative to the dexterous hand, so that the dexterous hand can be controlled to quickly and accurately grasp the goods. At the same time, the method of the present invention is simple, so it can save computer computing memory, improve the computing speed, and thus greatly improve the grasping speed of the robot. In addition, the present invention adopts an improved whale optimization algorithm, which can overcome the problem that the trajectory planning algorithm is prone to fall into local optimization, thereby improving the planning accuracy and ensuring the accurate grasping of the target object.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A motion planning and control method for robot sorting goods, characterized in that: include: S1, using a monocular camera to photograph the goods to be sorted in the sorting area to obtain an image of the goods to be sorted; S2, mapping the image to be sorted based on a three-dimensional grid to obtain initial features of each grid point in the three-dimensional grid; S3, calculating a camera depth map according to initial features of each grid point in the three-dimensional grid based on a feature correction method and a volume rendering method; S4, correcting the camera depth map according to the current position information of the dexterous hand at the end of the robotic arm and the position information of the monocular camera to obtain a corrected depth map; S5, using adaptive technology, improves the whale optimization algorithm by introducing improved potential field factors and virtual obstacles to obtain an improved whale optimization algorithm; S6, calculating the posture information of the goods to be sorted from the image to be sorted using a posture estimation method; Based on the improved whale optimization algorithm, the motion trajectory of the robotic arm and the grasping posture of the dexterous hand are planned according to the corrected depth map and the posture information, so as to control the dexterous hand at the end of the robotic arm to sort the goods to be sorted in the sorting area.

2. The motion planning and control method for robot sorting commodities according to claim 1, characterized in that: The S2 is specifically: S21, extracting features from the image to be sorted to obtain a two-dimensional feature map; S22, using the internal and external parameters of the monocular camera, projecting each grid point in the three-dimensional grid onto the two-dimensional feature map to obtain initial features of each grid point in the three-dimensional grid.

3. The motion planning and control method for robot sorting commodities according to claim 2, characterized in that: In S21, a CNN convolutional neural network is specifically used to extract features of the image to be sorted.

4. The motion planning and control method for robot sorting commodities according to claim 1, characterized in that: The S3 is specifically: S31, inputting the initial features of each grid point in the three-dimensional grid into an occupancy probability prediction model to obtain the initial occupancy probability of each grid point in the three-dimensional grid; S32, calculating a correction parameter of an initial feature of each grid point in the three-dimensional grid according to the initial occupancy probability of each grid point in the three-dimensional grid; S33, correcting the initial features of each grid point in the three-dimensional grid based on the correction parameters of the initial features of each grid point in the three-dimensional grid, to obtain the corrected features of each grid point in the three-dimensional grid; S34, judging whether to continue the correction process according to the correction characteristics of each grid point in the three-dimensional grid; If yes, execute S35; if no, execute S36; S35, taking the correction feature of each grid point in the three-dimensional grid as the initial feature of each grid point in the three-dimensional grid and returning to S31 to iterate the loop until the correction feature of each grid point in the three-dimensional grid meets the preset condition; S36, inputting the correction feature of each grid point in the three-dimensional grid into the occupancy probability prediction model to obtain the occupancy probability of each grid point in the three-dimensional grid; Based on a volume rendering method, the image to be sorted is rendered according to the occupancy probability of each grid point in the three-dimensional grid to obtain a camera depth map.

5. The motion planning and control method for robot sorting commodities according to claim 4, characterized in that: In S31, the occupancy probability prediction model is specifically an Occupancy model.

6. The motion planning and control method for robot sorting commodities according to claim 4, characterized in that: In the step S32, the calculation formula of the refinement degree is: Among them, T i represents the initial occupancy probability of the i-th grid point in the three-dimensional grid, δ represents a preset refinement parameter, and P represents the refinement degree.

7. The motion planning and control method for robot sorting commodities according to claim 4, characterized in that: The S33 is specifically: Constructing a feature interpolation model, and adjusting model parameters of the feature interpolation model according to correction parameters of initial features of each grid point in the three-dimensional grid; Substituting the correction features of each grid point in the three-dimensional grid into the feature interpolation model after the model parameters are adjusted to output a feature function; The coordinates of each grid point in the three-dimensional grid are substituted into the characteristic function to obtain the correction features of each grid point in the three-dimensional grid.

8. The motion planning and control method for robot sorting commodities according to claim 4, characterized in that: The S34 is specifically: Calculating the average value of the correction features of all grid points in the three-dimensional grid to obtain the average correction feature; Performing difference processing on the correction feature of each grid point in the three-dimensional grid and the average correction feature respectively, to obtain the correction feature difference of each grid point in the three-dimensional grid; Determine whether the corrected feature difference of each grid point in the three-dimensional grid is within a preset feature difference threshold range, and count the point values ​​of the grid points whose corrected feature difference is not within the preset feature difference threshold range; Determine whether to continue the calibration process according to the point value; If yes, execute S35; if no, execute S36; If the point value is greater than or equal to the preset point value, S35 is executed; if the point value is less than the preset point value, S36 is executed.

9. The motion planning and control method for robot sorting commodities according to claim 1, characterized in that: The S4 is specifically: A camera coordinate system is constructed according to the position information and internal and external parameters of the monocular camera, and a dexterous hand coordinate system is constructed according to the current position information and posture of the dexterous hand at the end of the robotic arm; Calculating a correction matrix between the monocular camera and the dexterous hand according to a conversion relationship between the camera coordinate system and the dexterous hand coordinate system; The camera depth map is corrected according to the correction matrix to obtain a corrected depth map.

10. A motion planning and control system for a robot to sort goods, characterized in that: The invention comprises a monocular camera (1), a mechanical arm (2) and a tool box (4) which are arranged in sequence from top to bottom; the monocular camera (1) is suspended above the tool box (4) through a curved pipe (5); the mechanical arm (2) is installed on the tool box (4), and a suction-grasping dexterous hand (3) is installed on the mechanical arm (2); a display (6) is provided on the tool box (4), four rolling pulleys (7) are provided at the bottom of the tool box (4), and an industrial computer is provided inside the tool box (4); The monocular camera (1) is used to photograph the commodities to be sorted in the sorting area to obtain images of the commodities to be sorted; The industrial computer is used to map the image to be sorted based on a three-dimensional grid to obtain initial features of each grid point in the three-dimensional grid; Based on the feature correction method and the volume rendering method, a camera depth map is calculated according to the initial features of each grid point in the three-dimensional grid; the camera depth map is corrected according to the current position information of the dexterous hand at the end of the robotic arm and the position information of the monocular camera to obtain a corrected depth map; Adaptive technology is used to improve the whale optimization algorithm by introducing improved potential field factors and virtual obstacles to obtain an improved whale optimization algorithm; Calculating the pose information of the goods to be sorted from the image to be sorted by using a pose estimation method; Based on the improved whale optimization algorithm, the motion trajectory of the robot arm (2) and the grasping posture of the dexterous hand (3) are planned according to the corrected depth map and the posture information, so as to control the dexterous hand (3) at the end of the robot arm (2) to sort the goods to be sorted in the sorting area.

Images