Heavy-load multi-joint industrial robot path planning method and system

Abstract

The invention relates to the technical field of industrial robots, in particular to a heavy-load multi-joint industrial robot path planning method and system, and the method comprises the steps: determining a robot path constraint condition based on a production demand, carrying out the interpolation of a node through a polynomial difference function, and generating a joint transformation curve; an obstacle avoidance strategy is generated through a path image collected by a camera in combination with an A star algorithm; motion control is conducted on all joints of the robot based on the joint transformation curve, the robot motion strategy is adjusted through the obstacle avoidance strategy, the variable transformation curve of all the joints is generated through joint space path planning under the given joint angle constraint condition, and the obstacle avoidance strategy is automatically generated through a camera in combination with an A star algorithm. And the obstacle suddenly appearing on the planned path is avoided so as to ensure that the robot can safely avoid the obstacle to arrive at the destination.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robots, and in particular to a path planning method and system for a heavy-loaded multi-joint industrial robot. Background Art

[0002] A heavy-load multi-joint robot has the characteristics of compact structure, flexible operation, and large range of motion. It is a typical robot form with a multi-joint manipulator or multiple degrees of freedom.

[0003] Path planning refers to searching for an optimal collision-free path in an environment with certain obstacles, given a starting point and a target point, with reference to certain criteria, such as the shortest path factor, minimum time spent, minimum number of turns, minimum cost, etc.

[0004] In the actual working scenarios of multi-joint robots, optimizing time and energy consumption is the main goal. By optimizing by considering both time and energy consumption, the flexibility and efficiency of trajectory planning can be improved, so that the robot can complete tasks more quickly, improve production efficiency and service quality. At the same time, reducing energy consumption helps to reduce operating costs, extend the service life of the robot, and meet the requirements of sustainable development. In addition, optimizing the trajectory can also reduce mechanical wear and fatigue, and improve the reliability and stability of the robot. In scenarios with high precision and safety requirements, the trajectory planning method that optimizes time and energy consumption can ensure the smoothness of the robot's movement for heavy-duty multi-joint robots and avoid potential harm to the surrounding environment and personnel. In summary, by considering the trajectory planning method that optimizes time and energy consumption, the comprehensive performance, economic benefits and reliability of heavy-duty multi-joint robots can be improved to meet the needs of modern industry and service industries.

[0005] Currently, multi-joint robots are unable to make safe predictions about target positions and avoid obstacles during movement, posing a safety hazard. Summary of the invention

[0006] The purpose of the present invention is to provide a path planning method and system for a heavy-loaded multi-joint industrial robot, aiming to solve the problem that the multi-joint robot is unable to make a safe prediction of the target position and avoid obstacles during movement, resulting in potential safety hazards.

[0007] To achieve the above objectives, in a first aspect, the present invention provides a path planning method for a heavy-load multi-joint industrial robot, comprising the following steps:

[0008] Determine the robot path constraints based on production requirements, and interpolate the nodes through polynomial difference functions to generate joint transformation curves;

[0009] The path image collected by the camera is combined with the A-star algorithm to generate an obstacle avoidance strategy;

[0010] The motion of each joint of the robot is controlled based on the joint transformation curve, and the robot motion strategy is adjusted through the obstacle avoidance strategy.

[0011] The specific method of determining the robot path constraint conditions based on production requirements and interpolating the nodes through a polynomial difference function to generate a joint transformation curve is as follows:

[0012] Generate motion path points based on production requirements and pre-process the path points to obtain the robot path;

[0013] Determine the starting point and end point of the robot path, and the position, velocity and acceleration of the intermediate nodes to obtain constraint conditions;

[0014] The nodes are interpolated by a polynomial difference function based on the constraint conditions to generate a joint transformation curve.

[0015] The polynomial difference function includes a cubic polynomial interpolation method, a quintic polynomial interpolation method and a high-order polynomial interpolation method.

[0016] The specific method of generating the obstacle avoidance strategy by combining the path image collected by the camera with the A-star algorithm is as follows:

[0017] The robot camera collects images of the robot's surrounding environment in real time, and the workshop camera collects images of the robot's running path in real time;

[0018] Based on the surrounding environment image and the running path image combined with the A-star algorithm, an obstacle avoidance strategy is generated.

[0019] The specific method of controlling the motion of each joint of the robot based on the joint transformation curve and adjusting the robot motion strategy through the obstacle avoidance strategy is as follows:

[0020] The joint transformation curve is used to simulate the movement of the robot, and the joint transformation curve is adjusted based on the simulation result to control the movement of each joint of the robot;

[0021] The robot motion strategy is adjusted through the obstacle avoidance strategy to avoid obstacles and send obstacle removal information to the administrator.

[0022] In a second aspect, the present invention further provides a heavy-duty multi-joint industrial robot path planning system, which is applied to the heavy-duty multi-joint industrial robot path planning system as described in the first aspect above, comprising an input module, a planning module, a simulation module, an avoidance module and an execution module, wherein the input module, the planning module, the simulation module and the execution module are connected in sequence;

[0023] The input module is used for the user to input the constraints of the running path and the robot parameters to the robot;

[0024] The planning module interpolates nodes through a polynomial difference function based on the constraint conditions and the robot parameters, generates joint transformation curves, and performs trajectory planning;

[0025] The simulation module simulates the robot motion based on the joint transformation curve, performs simulation verification, and adjusts the joint transformation curve;

[0026] The avoidance module automatically generates an avoidance strategy based on the path image collected by the camera;

[0027] The execution module controls each joint of the robot to execute movement based on the joint transformation curve and the avoidance strategy.

[0028] Wherein, the execution module includes an execution unit and an early warning unit, and the execution unit is connected to the early warning unit;

[0029] The execution unit controls each joint of the robot to execute movement based on the joint transformation curve and the avoidance strategy, and adjusts the robot movement strategy through the obstacle avoidance strategy to avoid obstacles;

[0030] The early warning unit is used to send obstacle removal information of the obstacle avoidance strategy to the administrator to issue an obstacle early warning.

[0031] The heavy-duty multi-joint industrial robot path planning method of the present invention determines the robot path constraint conditions based on production requirements, and interpolates the nodes through a polynomial difference function to generate a joint transformation curve; generates an obstacle avoidance strategy based on the path image captured by a camera and combined with an A-star algorithm; performs motion control on each joint of the robot based on the joint transformation curve, and adjusts the robot motion strategy through the obstacle avoidance strategy. The method generates a variable change curve of each joint under the constraint condition of a given joint angle through joint space path planning, and automatically generates an obstacle avoidance strategy through a camera combined with an A-star algorithm to avoid obstacles that suddenly appear on the planned path, so as to ensure that the robot can safely avoid obstacles and reach the destination, thereby solving the problem that the multi-joint robot cannot make a safe prediction of the target position and avoid obstacles during movement, resulting in safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 It is a flow chart of the path planning method for a heavy-loaded multi-joint industrial robot provided by the present invention.

[0034] Figure 2 It is a flowchart of a specific method for determining the robot path constraints based on production requirements, and interpolating the nodes through polynomial difference functions to generate joint transformation curves.

[0035] Figure 3 It is a flowchart of the specific method of generating obstacle avoidance strategy by combining the path image collected by the camera with the A-star algorithm.

[0036] Figure 4 It is a flowchart of a specific method of controlling the motion of each joint of the robot based on the joint transformation curve and adjusting the robot motion strategy through the obstacle avoidance strategy.

[0037] Figure 5 It is a schematic diagram of a heavy-load multi-joint industrial robot path planning system provided by the present invention.

[0038] In the figure: 1-input module, 2-planning module, 3-simulation module, 4-avoidance module, 5-execution module, 6-execution unit, 7-early warning unit. DETAILED DESCRIPTION

[0039] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0040] See also Figures 1 to 4 In a first aspect, the present invention provides a path planning system for a heavy-duty multi-joint industrial robot, comprising the following steps:

[0041] S1 determines the robot path constraints based on production requirements, and interpolates the nodes through polynomial difference functions to generate joint transformation curves;

[0042] Specific method:

[0043] S11 generates motion path points based on production requirements and pre-processes the path points to obtain the robot path;

[0044] In an embodiment of the present invention, motion path points are generated based on production requirements, and the path points are preprocessed, such as denoising and smoothing, to ensure the continuity and smoothness of the path, thereby obtaining a robot path.

[0045] S12 determines the starting point and end point of the robot path, and the position, speed and acceleration of the intermediate nodes to obtain constraint conditions;

[0046] S13 interpolates the nodes through a polynomial difference function based on the constraint conditions to generate a joint transformation curve.

[0047] In an embodiment of the present invention, the polynomial difference function includes a cubic polynomial interpolation method, a quintic polynomial interpolation method and a high-order polynomial interpolation method. The cubic polynomial interpolation method only needs to solve a set of four-variable linear equations, and the amount of calculation is relatively small, but its angular acceleration curve may change suddenly at the beginning, and the continuity of the angular acceleration during the robot's movement cannot be guaranteed, which may cause a certain impact on the motor. The quintic polynomial interpolation rule can generate a smoother joint acceleration curve, which is suitable for scenarios with high requirements for motion smoothness. It selects a suitable interpolation function for trajectory interpolation and optimizes the trajectory according to multiple performance indicators.

[0048] S2 generates obstacle avoidance strategies by combining the path images collected by the camera with the A-star algorithm;

[0049] Specific method:

[0050] S21 uses the robot camera to collect images of the robot's surrounding environment in real time, and the workshop camera collects images of the robot's running path in real time;

[0051] In an embodiment of the present invention, the robot's planned path is double-shotted by a camera mounted on the robot and a camera installed on the wall of the workshop. By combining the two captured image data, the planned path can be photographed in all directions, and obstacles on the planned path can be discovered at the first time.

[0052] S22 generates an obstacle avoidance strategy based on the surrounding environment image and the running path image combined with the A-star algorithm.

[0053] In the embodiment of the present invention, the A* algorithm combines the characteristics of the breadth-first search and the best-first search of the Dijkstra algorithm, estimates the distance from the current node to the target node through a heuristic function, thereby improving the search efficiency, and includes the following steps:

[0054] 1. Initialization: Create an open list (also called a front list) to store nodes that have been found but not yet evaluated, and a closed list to store nodes that have been fully evaluated.

[0055] 2. Select the starting node: Add the starting point to the open list and assign it an initial f value (f(n) = g(n) + h(n)), where g(n) is the actual cost (or path length) from the starting point to node n, and h(n) is the estimated cost from node n to the target point, which is the value calculated by the heuristic function.

[0056] 3. Node evaluation and selection: Select the node n with the lowest f value from the open list as the node currently evaluated. If node n is the target node, the path has been found and the algorithm ends. Move node n from the open list to the closed list, indicating that it has been fully evaluated.

[0057] 4. Neighbor node processing: If the neighbor node is already in the closed list, ignore it. If the neighbor node is not in the open list, calculate its g value (the cost from the starting point to the neighbor node) and add it to the open list. If the neighbor node is already in the open list, but the g value of reaching it through node n is smaller, update its g value and update its parent node to node n.

[0058] 5. Path reconstruction: Once the target node is found, the path is reconstructed by backtracking the parent node.

[0059] The core of the A* algorithm is the choice of heuristic function h(n), which directly affects the efficiency of the algorithm and the degree of optimization of the path. Common heuristic functions include Manhattan distance (applicable to situations where movement can only be made along the axis), Euclidean distance (applicable to situations where movement is free), and Chebyshev distance (applicable to situations where movement is diagonal).

[0060] S3 controls the motion of each joint of the robot based on the joint transformation curve, and adjusts the robot motion strategy through the obstacle avoidance strategy.

[0061] Specific method:

[0062] S31 simulates the movement of the robot through the joint transformation curve, adjusts the joint transformation curve based on the simulation result, and controls the movement of each joint of the robot;

[0063] In the embodiment of the present invention, the joint transformation curve is combined with the robot parameters to model and simulate the robot movement, and the joint transformation curve is adjusted based on the simulation result to control the movement of each joint of the robot.

[0064] S32 adjusts the robot's motion strategy through the obstacle avoidance strategy to avoid obstacles and sends obstacle removal information to the administrator.

[0065] In an embodiment of the present invention, the movement strategy of the robot in the planned path is adjusted by the obstacle avoidance strategy to ensure that the robot can safely avoid obstacles, and send obstacle removal information to the administrator to issue an obstacle warning, informing the administrator to go and remove the obstacles.

[0066] See also Figure 5 In a second aspect, the present invention further provides a heavy-duty multi-joint industrial robot path planning system, which is applied to the heavy-duty multi-joint industrial robot path planning system as described in the first aspect above, and includes an input module 1, a planning module 2, a simulation module 3, an avoidance module 4 and an execution module 5, wherein the input module 1, the planning module 2, the simulation module 3 and the execution module 5 are connected in sequence, and the avoidance module 4 is connected to the execution module 5;

[0067] The input module 1 is used for the user to input the constraints of the running path and the robot parameters to the robot;

[0068] The planning module 2 interpolates nodes through a polynomial difference function based on the constraint conditions and the robot parameters, generates a joint transformation curve, and performs trajectory planning;

[0069] The simulation module 3 simulates the robot motion based on the joint transformation curve, performs simulation verification, and adjusts the joint transformation curve;

[0070] The avoidance module 4 automatically generates an avoidance strategy based on the path image collected by the camera;

[0071] The execution module 5 controls each joint of the robot to execute movement based on the joint transformation curve and the avoidance strategy.

[0072] In an embodiment of the present invention, the user inputs the constraints of the running path and the robot parameters to the robot through the input module 1. The planning module 2 interpolates the nodes based on the constraints and the robot parameters through a polynomial difference function to generate a joint transformation curve and perform trajectory planning. The simulation module 3 simulates the robot movement based on the joint transformation curve, performs simulation verification, and adjusts the joint transformation curve. The avoidance module 4 automatically generates an avoidance strategy based on the path image captured by the camera. The execution module 5 controls the movement of each joint of the robot based on the joint transformation curve and the avoidance strategy.

[0073] Further, the execution module 5 includes an execution unit 6 and an early warning unit 7, and the execution unit 6 and the early warning unit 7 are connected;

[0074] The execution unit 6 controls the joints of the robot to execute motion based on the joint transformation curve and the avoidance strategy, and adjusts the robot motion strategy through the obstacle avoidance strategy to avoid obstacles;

[0075] The early warning unit 7 is used to send obstacle removal information of the obstacle avoidance strategy to the administrator to issue an obstacle early warning.

[0076] In an embodiment of the present invention, the execution unit 6 controls the movement of each joint of the robot based on the joint transformation curve and the avoidance strategy, adjusts the robot movement strategy through the obstacle avoidance strategy to avoid obstacles, and the early warning unit 7 sends obstacle removal information of the obstacle avoidance strategy to the administrator to issue an obstacle warning.

[0077] What is disclosed above is only a preferred embodiment of the path planning method and system for a heavy-duty multi-joint industrial robot of the present invention. Of course, this cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiments are implemented, and equivalent changes made according to the claims of the present invention are still within the scope of the invention.

Claims

1. A path planning method for a heavy-loaded multi-joint industrial robot, characterized in that: The following steps are involved: Determine the robot path constraints based on production requirements, and interpolate the nodes through polynomial difference functions to generate joint transformation curves; The path image collected by the camera is combined with the A-star algorithm to generate an obstacle avoidance strategy; The motion of each joint of the robot is controlled based on the joint transformation curve, and the robot motion strategy is adjusted through the obstacle avoidance strategy.

2. The heavy-load multi-joint industrial robot path planning method according to claim 1, It is characterized by: The specific method of determining the robot path constraint conditions based on production requirements and interpolating the nodes through a polynomial difference function to generate a joint transformation curve is as follows: Generate motion path points based on production requirements and pre-process the path points to obtain the robot path; Determine the starting point and end point of the robot path, and the position, velocity and acceleration of the intermediate nodes to obtain constraint conditions; Based on the constraint conditions, the nodes are interpolated by a polynomial difference function to generate a joint transformation curve.

3. The heavy-duty multi-joint industrial robot path planning method according to claim 3, characterized in that ； The polynomial difference function includes a cubic polynomial interpolation method, a quintic polynomial interpolation method and a high-order polynomial interpolation method.

4. The heavy-load multi-joint industrial robot path planning method according to claim 1, characterized in that ； The specific method of generating the obstacle avoidance strategy by combining the path image collected by the camera with the A-star algorithm is as follows: The robot camera collects images of the robot's surrounding environment in real time, and the workshop camera collects images of the robot's running path in real time; Based on the surrounding environment image and the running path image combined with the A-star algorithm, an obstacle avoidance strategy is generated.

5. The heavy-load multi-joint industrial robot path planning method according to claim 1, It is characterized by: The specific method of controlling the motion of each joint of the robot based on the joint transformation curve and adjusting the robot motion strategy through the obstacle avoidance strategy is: Simulating the movement of the robot through the joint transformation curve, adjusting the joint transformation curve based on the simulation result, and controlling the movement of each joint of the robot; The robot motion strategy is adjusted through the obstacle avoidance strategy to avoid obstacles and send obstacle removal information to the administrator.

6. A heavy-duty multi-joint industrial robot path planning system, applied to a heavy-duty multi-joint industrial robot path planning system as claimed in any one of claims 1 to 5, characterized in that ； It includes an input module, a planning module, a simulation module, an avoidance module and an execution module, wherein the input module, the planning module, the simulation module and the execution module are connected in sequence; The input module is used for the user to input the constraints of the running path and the robot parameters to the robot; The planning module interpolates nodes through a polynomial difference function based on the constraint conditions and the robot parameters, generates joint transformation curves, and performs trajectory planning; The simulation module simulates the robot motion based on the joint transformation curve, performs simulation verification, and adjusts the joint transformation curve; The avoidance module automatically generates an avoidance strategy based on the path image collected by the camera; The execution module controls each joint of the robot to execute movement based on the joint transformation curve and the avoidance strategy.

7. The heavy-duty multi-joint industrial robot path planning system according to claim 6, It is characterized by: The execution module includes an execution unit and an early warning unit, and the execution unit is connected to the early warning unit; The execution unit controls each joint of the robot to execute movement based on the joint transformation curve and the avoidance strategy, and adjusts the robot movement strategy through the obstacle avoidance strategy to avoid obstacles; The early warning unit is used to send obstacle removal information of the obstacle avoidance strategy to the administrator to issue an obstacle early warning.

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