Multi-station welding robot coordinated motion planning method and offline programming system

Abstract

The invention discloses a multi-station welding robot coordinated motion planning method and an off-line programming system, and the method specifically comprises the steps of welding pose optimization, welding taboo table generation, welding spot redistribution, same-direction welding sequence optimization, single robot path planning, track scanning body generation of all robots, track scanning body interference inspection and the like. And coordinated optimization of paths of multiple robots is realized. According to the method, multiple optimization strategies and planning methods are provided, coordinated motion planning and track generation of multi-station multi-robot welding paths are achieved, an automatic offline programming system of multiple welding robots is developed, and the motion planning efficiency of the multi-robot welding process is remarkably improved.

Description

Technical Field

[0001] The invention relates to the field of multi-robot coordinated motion planning, in particular to the field of welding robot motion planning, and more particularly to a multi-robot welding process coordinated simulation and off-line programming system. Background Art

[0002] With the continuous development of automobile technology, automobile bodies are becoming lighter and lighter, and the application of all-aluminum bodies has made the existing welding process more complicated. At present, multi-robot system is the most widely researched field of robotics technology. Compared with single-robot welding process planning, multi-robot system has the characteristics of high efficiency, flexibility and strong robustness, and can adapt to process planning in high-beat and complex scenarios.

[0003] When assigning tasks and planning paths for welding robots, faced with complex welding processes and welding environments, manual experience is often relied upon to complete welding process planning. This approach is time-consuming, inefficient, and inaccurate, and it is difficult to achieve the desired effect. At present, multi-robot path planning mostly uses intelligent algorithms and weld point allocation to plan reasonable welding paths and weld point sequences for multiple robots. This achieves the optimal posture of the robot during the welding process and the mutual communication and coordination between robots.

[0004] For complex and dynamic production environments and stringent cycle requirements, there is an efficient and intelligent robot automatic planning and solution to achieve the most efficient multi-station and multi-robot parallel welding process solution in different environments. Summary of the invention

[0005] Purpose of the invention: In view of the complex and dynamic production environment and stringent production rhythm requirements faced by current multi-robot welding systems, and the problems of insufficient rationality of task allocation results and low trajectory generation efficiency in existing multi-welding robot motion planning methods, the present invention coordinates and optimizes multi-robot paths by reallocating weld points and re-optimizing robot paths, and provides a program-coordinated intelligent planning method and system for multi-robot welding systems for body-in-white, which realizes the generation of interference-free trajectories during multi-machine welding through automated programs, thereby improving efficiency.

[0006] Technical solution: The coordinated motion planning method and offline programming system of a multi-station welding robot described in the present invention realizes the coordinated optimization of multi-robot paths through welding position posture optimization, welding taboo table generation, weld point redistribution, same-direction welding sequence optimization, single robot path planning, trajectory sweep body generation of all robots, trajectory sweep body interference inspection, etc.

[0007] The specific steps include:

[0008] Step 1: With the minimum posture change and no collision as constraints, heuristic, deep learning and other algorithms are used to optimize the welding posture of the welding points in the workstation without interference, and a taboo table is calculated in which any two welding points cannot be assigned to the same robot at the same time.

[0009] Step 2: Based on the taboo table obtained in step 1, the welding points are allocated to different robots, and the welding order of each robot in the workstation is adjusted in the same direction.

[0010] Step 3: Use random sampling method to plan the welding path of the robot in the workstation.

[0011] Step 4: Generate the trajectory sweep bodies of all robots based on the welding path planned in step 3, and perform interference check on the trajectory sweep bodies of different robots.

[0012] Step 5: With the minimum interference area and production cycle as the optimization goals, the coordinated optimization of multiple robot paths is achieved by reallocating solder joints, re-optimizing robot paths, and other methods.

[0013] Furthermore, in step 1, the algorithm is used to determine the optimal posture of each welding point under the welding operation and calculate the taboo table of welding point sets. The specific process includes:

[0014] Step 1-1, perform accessibility analysis on the solder joints in the workstation to determine the non-interference angle between each solder joint and the environment;

[0015] Step 1-2, taking the weighted average sum of the angle deviation of the welding point and the number of interferences during the welding under the same welding operation as the evaluation function;

[0016] Step 1-3, based on steps 1-1 and 1-2, the welding posture is optimized using the idea of ​​genetic algorithm, and the interference-free posture of each welding point and the taboo table are output.

[0017] Furthermore, the coordinated optimization of the minimum interference area and production described in step 5 specifically includes the following process:

[0018] Step 5-1, select the welding point and find the nearest robot that can reach it with beat redundancy;

[0019] Step 5-2, assign the welding points to the robot task sequence with the closest distance and perform local path coordination planning.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] (1) The robot’s non-interference trajectory planning is highly efficient;

[0022] (2) Reduce manual intervention. Through automated program generation, the frequency and complexity of manual intervention can be reduced;

[0023] (3) On-site teaching work is moved forward, and welding procedures can be optimized using automated programs without the need for on-site teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a method for generating multi-robot welding trajectories for a body-in-white according to the present invention;

[0025] Figure 2 The present invention is a schematic diagram of the multi-robot motion envelope based on the method of this article. DETAILED DESCRIPTION

[0026] Combination Figure 1 The method for coordinated planning of multi-robot welding paths for a body-in-white in this embodiment comprises the following steps:

[0027] Step 1: With the minimum posture change and no collision as constraints, the heuristic, deep learning and other algorithms are used to optimize the non-interference posture of the welding points in the workstation, and a taboo table is calculated that any two welding points cannot be assigned to the same robot at the same time;

[0028] Step 1-1, perform accessibility analysis on the solder joints in the workstation to determine the non-interference angle between each solder joint and the environment;

[0029] Step 1-2, calculating the angle deviation of the welding point and the number of interferences during welding under the same welding operation;

[0030] Step 2: Based on the taboo table obtained in step 1, the clusters are divided in combination with the distance between adjacent solder joints and the angle between adjacent solder joints.

[0031] Step 2-2, according to the distance from the cluster center to the robot and the number of robot welding points as evaluation values, the welding point clusters are assigned to the robots;

[0032] Step 2-3: Perform reachability analysis on the welding points currently assigned by the robot, assign the unreachable points, and further optimize and adjust the assignment results according to the taboo table until the taboo table requirements are met. Randomly sort the welding points in the cluster according to the same direction principle and the minimum path principle.

[0033] Step 3, using a collision-free path planning method in Cartesian space to plan the welding path of the robot in the workstation;

[0034] Step 4, generating the trajectory sweep bodies of all robots based on the welding path planned in step 3, and performing interference check on the trajectory sweep bodies of different robots;

[0035] Step 5: With the minimum interference area and production cycle as the optimization goals, the coordinated optimization of multiple robot paths is achieved by reallocating solder joints, re-optimizing robot paths, and other methods.

[0036] Step 5-1, select the welding point and find the nearest robot that can reach it with beat redundancy;

[0037] Step 5-2, assign the welding points to the robot task sequence with the closest distance and perform local path coordination planning.

[0038] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any technician in the relevant technical field, without departing from the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, which does not depart from the content of the technical solution of the present invention and still falls within the protection scope of the present invention.

Claims

1. A coordinated motion planning method and offline programming system for a multi-station welding robot, characterized in that: The coordinated optimization of multi-robot paths is achieved through welding position optimization, welding taboo table generation, weld point redistribution, same-direction welding sequence optimization, single robot path planning, trajectory sweep body generation for all robots, trajectory sweep body interference check, etc.

2. The coordinated motion planning method and offline programming system of a multi-station welding robot according to claim 1 is characterized in that: The coordinated optimization of multi-robot paths is based on the optimization goals of minimizing the interference area and the shortest robot movement time, which is achieved by reallocating welding points and re-optimizing robot paths.

3. The coordinated motion planning method and offline programming system of a multi-station welding robot according to claim 2 is characterized in that: The specific process of optimization includes: (1) Select the welding point and find a robot that can reach it with redundant beats; (2) Allocate the welding points to the robot task sequence with the closest distance and perform local path coordination planning.

4. The coordinated motion planning method and offline programming system of a multi-station welding robot according to claim 1 is characterized in that: The optimization of welding posture is based on the constraints of minimum posture change and no collision. It uses heuristic and deep learning algorithms to determine the optimal non-interference posture of each weld point under each welding operation, and calculates the taboo table that cannot be assigned to the same robot weld point at the same time.

5. The coordinated motion planning method and offline programming system of a multi-station welding robot according to claim 4 is characterized in that: The method for generating the taboo table comprises: (1) Perform accessibility analysis on the solder joints in the workstation to determine the non-interference angle between each solder joint and the environment; (2) The weighted average of the angle deviation of the welding spot and the number of interferences during the same welding operation is used as the evaluation function; (3) Based on steps (1) and (2), the welding posture is optimized using the idea of ​​genetic algorithm, and the interference-free posture of each welding point and the taboo table are output.

6. The coordinated motion planning method and offline programming system of a multi-station welding robot according to claim 1, characterized in that: The random sampling method is used to plan the welding path of the robot in the workstation, and the algorithm is optimized from the aspects of adaptive step size, search direction, narrow space escape, path optimization, and efficiency improvement, so as to quickly realize path planning.

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