CN115592663B - Full-automatic motion planning method for industrial robot machining system with additional external shaft - Google Patents

Abstract

The invention discloses a full-automatic motion planning method of an industrial robot processing system with an additional external shaft, which comprises the following steps: step 1, acquiring a processing path point set; step 2, selecting a station mode; the automatic station setting mode is divided into intelligent planning station and fixed allocation station; step 3, executing a collision-free motion planning algorithm on each path; the delay collision and the path shearing are fused into a PRM algorithm, so that the purposes of reducing time and shortening paths are achieved; and 4, integrating the motion paths of robots of all stations to enable the robots to execute processing tasks. The invention can furthest increase the processing range of a single stop, effectively improve the quality of a motion planning path and the motion planning efficiency, thereby improving the processing efficiency of the robot.

Description

Fully automatic motion planning method for industrial robot machining system with additional external axes

Technical Field

The invention belongs to the field of industrial robot motion planning, and in particular relates to a fully automatic motion planning method for an industrial robot processing system with an additional external axis.

Background Art

Industrial robots are widely used in the field of processing due to their ease of use, high flexibility and high efficiency. However, when performing large workpiece processing tasks, the robot with a fixed base has a limited working range and is difficult to complete all processing tasks. Therefore, robots with additional external axes are generally used to complete large parts processing tasks. Traditional station planning is to divide the external axis into areas according to the robot's working radius and select several fixed workstations of the robot to complete local processing tasks. However, this station allocation method cannot maximize the number of processing paths covered by a single stop, nor is it the optimal station allocation. In addition, the robot uses traditional motion planning methods, which takes a long time to plan and has poor quality of planned paths. To this end, a fully automatic motion planning method for an industrial robot processing system with additional external axes is proposed.

Summary of the invention

The purpose of the present invention is to address the problems of unreasonable station planning, long motion planning time and poor motion path quality in large workpiece processing tasks, and to propose a fully automatic motion planning method for an industrial robot processing system with an additional external axis, which can maximize the processing range of a single stop, effectively improve the quality of the motion planning path, improve the motion planning efficiency, and thus improve the robot processing efficiency.

To achieve the above object, the technical solution provided by the present invention is: a fully automatic motion planning method for an industrial robot processing system with an additional external axis, comprising the following steps:

Step 1, obtaining a processing path point set;

Input the sequence set of paths to be processed C = {C ₁ ,C ₂ ,…,C _j }, j = 1, 2,…, n, n is the number of processing paths, and the order of processing paths has been determined. Each processing path C _j is composed of m path points P _k , where P _k (x, y, z, Rx, Ry, Rz), k = 1, 2,…, m;

Step 2, select the station mode;

There are two optional modes for setting the station position: automatic station setting and manual station setting. The automatic station setting mode is divided into intelligent planning station position and fixed allocation station position.

If you select the intelligent station allocation mode, go to step 2.1;

Step 2.1, intelligent planning of the station is to determine the robot workstation according to the path to be processed;

Step 2.1.1, assign processing paths to different stations;

Step 2.1.2, find the optimal solution of the processing path points in each station;

Step 2.1.3, go to step 3;

If you select the intelligent station allocation mode, go to step 2.2;

Step 2.2, the fixed allocation station is divided into the external axis working area according to the robot working radius;

Step 2.2.1, obtain a number of stations in sequence;

Step 2.2.2, assign the machining path, determine whether there is an inverse solution at different stations, if yes, then successfully add it to this station, if not, proceed to the next station until the last station, if still no solution, then the workpiece exceeds the maximum range of the external axis;

Step 2.2.3, go to step 3;

If you select manual station setting mode, go to step 2.2.1;

Step 3, each path executes the collision-free motion planning algorithm;

Integrate delayed collision and path clipping into the PRM* algorithm to reduce time and shorten the path;

Step 4: Integrate the robot motion paths of all stations and let the robots perform processing tasks.

The preferred technical solution provided by the present invention is:

In step 3, the specific PRM* algorithm is as follows:

Step 3.1, first establish a roadmap, add the starting point and the end point to the roadmap, use random sampling to generate a sampling point v in the robot configuration space and add it to the vertex set V of the roadmap (G(v, E)), and select k nearest neighbor vertices n ₁ ,n ₂ ,…, _nk according to the nearest neighbor algorithm, where k is determined by a function based on the number of vertices in the roadmap, specifically k(n)＝k _PRM log(n), Add k edges e(c,n) to the edge set E of the roadmap (G(v,E)), and especially do not check for collisions between vertices and edges to obtain the initial roadmap. According to the A* search algorithm, find the optimal path. When finding the path, first check whether all nodes of the path collide. If there is a collision, delete all nodes, remove the connected edges and search the path again. If all nodes do not collide, check the edges. If the edges collide, delete the edges and search the path again. Finally, get the robot's target motion path.

Step 3.2: A further optimization method may be adopted for the robot target motion path obtained in step 3.1, using a cutting optimization method to further make the robot motion path short and smooth;

Starting from the first node of the path, each node is processed as follows:

(4) Three nodes in sequence form a triangle ΔP _i P _j P _k ;

(5) Take _the interpolation point _Pt of _PiPk , that is, _Pt satisfies the formula

(6) Order It can be seen that the smaller δ _t is, the larger ∠P _i P _j P _k is, that is, the path is smoother. By selecting a suitable threshold δ and deleting P _j when δ _t >δ, the path can be shortened.

Compared with the prior art, the present invention has the following beneficial effects:

Under the same number of iterations, the improved Prm* algorithm has a shorter path; under different numbers of iterations, the improved Prm* algorithm can not only shorten the planning time but also obtain a shorter path by reducing the number of iterations by half. Therefore, the present invention can maximize the processing range of a single stop, effectively improve the quality of motion planning paths, improve motion planning efficiency, and thus improve robot processing efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of the overall steps of the present invention;

Figure 2 is a diagram of the robot's external axes;

Figure 3 is a diagram of the reverse coordinate system;

Figure 4 is a schematic diagram of key point selection for rectangular hole flange processing;

FIG5 is a flowchart of the improved motion planning algorithm.

DETAILED DESCRIPTION

The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings.

FIG. 1 is a flowchart of the overall steps of the present invention. As shown in FIG. 1 , a fully automatic motion planning method for an additional external axis industrial robot processing system of the present invention includes the following steps:

Step 1, obtaining a processing path point set;

Input the sequence set of paths to be processed C = {C ₁ ,C ₂ ,…,C _j }, j = 1, 2,…, n, n is the number of processing paths, and the order of processing paths has been determined. Each processing path C _j is composed of m path points P _k , where P _k (x, y, z, Rx, Ry, Rz), k = 1, 2,…, m;

If you select manual station setting mode, go to step 2.2.1;

Step 2, select the station mode;

There are two optional modes for setting the position: automatic and manual. The automatic mode is divided into intelligent planning and fixed allocation of positions. The fixed allocation of positions reasonably divides the external axis working area according to the robot's working radius, while the intelligent planning determines the robot's working position according to the path to be processed.

If you select the intelligent station allocation mode, go to step 2.1;

Step 2.1, intelligent planning of the station is to determine the robot workstation according to the path to be processed;

Step 2.1.1, assign processing paths to different stations;

Step 2.1.2, find the optimal solution of the processing path points in each station;

The position of the robot actuator is determined by the first three joints of the robot, and the posture of the robot actuator is determined by the last three joints. The solution method of the robot workspace can be used for reference, and the robot coordinate system {F ₁ , F ₂ , F ₃ , F ₄ , F ₅ , F ₆ , F ₇ } is established inversely with the robot's wrist center position, as shown in Figure 3. The DH parameters are shown in Table 1.

Table 1 DH table

θ(°) d(mm) α(°) a(mm) J1 0 0 90 0 J2 0 0 -90 0 J3 0 451 90 42 J4 0 0 0 448 J5 90 0 90 0 J6 0 399 0 0

According to the DH algorithm, the expression of the base relative to the wrist center is:

According to the data in Table 1, substitute T to get the base position, as shown in (Equation 2)

Since only the position constraint of the robot is considered, only joint axis 2 and joint axis 4 are considered, and the cross-sectional view of the feasible position of the robot base is obtained. Therefore, firstly, let θ ₁ = 0, θ ₃ = 0, and obtain (Equation 3)

According to the joint limit value method, the cross-sectional diagram of the feasible position space of the robot base can be obtained, and then a three-dimensional diagram of the feasible position space of the robot base can be obtained by rotating it around the wrist axis.

First, obtain several key points on the periphery from the processing path, as shown in Figure 4. To process the rectangular flange of the part, the corner points of the outer rectangle can be selected to obtain the feasible position space of the base of the key point. The midpoint of the center line of the intersection of the feasible position space of all key points is taken as the optimal position of the robot. This position can select the optimal position according to the position of the processing path, and at the same time, it can expand the processing range of a single stop to a certain extent.

Obtain the optimal solution of the processing path point in each station, and obtain the robot joint value J (θ ₁ , θ ₂ , θ ₃ , θ ₄ , θ ₅ , θ ₆ ) corresponding to each processing path point;

Step 2.1.3, go to step 3;

If you select the intelligent station allocation mode, go to step 2.2;

Step 2.2, the fixed allocation station is divided into the working area of the external axis according to the working radius of the robot. The external axis is shown in Figure 2.

Step 2.2.1, obtain a number of stations in sequence;

Step 2.2.2, assign the machining path, determine whether there is an inverse solution at different stations, if yes, then successfully add it to this station, if not, proceed to the next station until the last station, if still no solution, then the workpiece exceeds the maximum range of the external axis;

Step 2.2.3, go to step 3;

Step 3, executing the collision-free motion planning algorithm;

The proposed motion planning algorithm is an improvement on the traditional PRM* algorithm. The traditional PRM* algorithm is an asymptotically optimal algorithm. Asymptotically optimal means that the path obtained after a finite number of planning iterations is a suboptimal path close to the optimal path, and each iteration is closer to the optimal path, which is a gradual convergence process. This algorithm is very time-consuming. There are two main factors. On the one hand, it is very time-consuming to initially establish a collision-free roadmap. On the other hand, the time consumption increases with the increase in the number of iterations. If the number of iterations is simply reduced, the robot's motion path will be too long. Therefore, delayed collision and path clipping are integrated into the PRM* algorithm to achieve the purpose of reducing time and shortening the path.

Step 3.1, the proposed algorithm flow chart is shown in Figure 4. First, a roadmap is established. The starting point and the end point are added to the graph. Random sampling is used to generate a sampling point v in the robot configuration space and add it to the vertex set V of the roadmap (G(v, E)). According to the nearest neighbor algorithm, k nearest neighbor vertices n ₁ ,n ₂ ,…, _nk are selected. k is determined by a function with n vertices as the cardinality in the roadmap, specifically k(n)＝k _PRM log(n), Add k edges e(c,n) to the edge set E of the roadmap (G(v,E)), and especially do not detect collisions between vertices and edges to obtain the initial roadmap. According to the A* search algorithm, find the optimal path. When finding the path, first check whether all nodes of the path collide. If there is a collision, delete all nodes, remove the connected edges and search the path again. If all nodes do not collide, check the edges. If the edges collide, delete the edges and search the path again. Finally, get the robot's target motion path.

Step 3.2: A further optimization method can be adopted for the robot target motion path obtained in step 3.1, using the cutting optimization method to further make the robot motion path short and smooth. Starting from the first node of the path, each node is processed as follows:

(7) Three nodes in sequence form a triangle ΔP _i P _j P _k ;

(8) Take _the interpolation point _Pt of _PiPk , that is, _Pt satisfies the formula

(9) Order It can be seen that the smaller δ _t is, the larger ∠P _i P _j P _k is, that is, the path is smoother. By selecting a suitable threshold δ and deleting Pj when δ _t >δ, the path can be shortened.

Under the condition of generating a roadmap with the same number of vertices, the running time of the Prm* algorithm is in the order of minutes, while the running time of the Prm* algorithm with delayed collision detection is in the order of seconds; under the same number of iterations, 10 groups of Prm* algorithm comparisons before and after improvement were carried out, and the experimental results are shown in Table 2-1. The results show that the improved Prm* algorithm has a shorter path; under different numbers of iterations, five groups of experiments were carried out using only the Prm* algorithm with delayed collision detection and the improved Prm* algorithm. The experimental results are shown in Table 2-2. The results show that the improved Prm* algorithm can not only shorten the planning time, but also obtain a shorter path by reducing the number of iterations by half.

Table 2-1 Comparison of path lengths under the same number of iterations

serial number PRM*path length (rad) Improved PRM* Path Length (rad) 1 3.8912 3.5267 2 3.7614 3.1542 3 4.0124 3.3776 4 3.6753 2.8979 5 3.6345 3.2884 6 4.1603 3.3605 7 3.2987 3.2987 8 3.8653 3.1835 9 3.5010 3.3062 10 3.4764 2.9163

Table 2-2 Experimental comparison under different iteration times

Step 4: Integrate the robot motion paths of all stations and let the robots perform processing tasks.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims (1)

1. The full-automatic motion planning method for the additional external shaft industrial robot machining system is characterized by comprising the following steps of:

Step 1, acquiring a processing path point set;

Inputting a sequence set of paths to be processed c= { C ₁,C₂,…,C_j }, j=1, 2, …, n, n being the number of processing paths, and the processing path sequence having been determined, each processing path C _j being composed of m path points P _k, wherein P _k (x, y, z, rx, ry, rz), k=1, 2, …, m;

step 2, selecting a station mode;

The automatic station setting mode is divided into intelligent planning station and fixed allocation station;

Selecting an intelligent planning station mode, and entering a step 2.1;

Step 2.1, the intelligent planning station is to determine a robot work station according to a path to be processed;

step 2.1.1, distributing the processing paths to different stations;

step 2.1.2, solving an optimal solution of a processing path point in each station;

step 2.1.3, entering step 3;

Selecting a fixed allocation station mode, and entering step 2.2;

Step 2.2, dividing the working area of the external shaft by the fixed distribution station according to the working radius of the robot;

step 2.2.1, obtaining a plurality of stations in sequence;

Step 2.2.2, distributing processing paths, judging whether inverse solutions exist under different station positions, if so, adding the processing paths to the station position successfully, if not, entering the judgment of the next station position until the last station position, and if not, exceeding the maximum range of an external shaft by the workpiece;

Step 2.2.3, entering step 3;

Selecting a manual station setting mode, and entering step 2.2.1;

step 3, executing a collision-free motion planning algorithm on each path;

the delay collision and the path shearing are fused into a PRM algorithm, so that the purposes of reducing time and shortening paths are achieved;

the PRM algorithm specifically includes:

Step 3.1, firstly, a route map is built, firstly, a starting point and an end point are added into the route map, a sampling point V is generated in a robot configuration space by using random sampling and is added into a route map (G (V, E)) vertex set V, k nearest neighbor vertices n ₁,n₂,…,n_k are selected according to a nearest neighbor algorithm, k is k (n): k _PRM log (n) selected as a function of the cardinality of the vertices of the route map n, Adding k edges E (c, n) into an edge set E of a route map (G (v, E)), particularly detecting no collision on the top points and the edges, obtaining an initial route map, searching an optimal path according to an A-search algorithm, firstly checking whether all nodes of the path are collided or not when searching the path, deleting all nodes if collision exists, removing the connected edges to search the path again, checking the edges if all nodes are not collided, deleting the edges to search the path again if the edges are collided, and finally obtaining a target motion path of the robot;

step 3.2, a further optimization method can be adopted for the robot target motion path obtained in the step 3.1, and the shearing optimization method is used for further enabling the robot motion path to be short and smooth;

Starting from the first node of the path, each node is processed as follows:

(1) Three nodes are sequentially arranged to form a triangle delta P _iP_jP_k;

(2) Taking the interpolation point P _t of P _iP_k, namely P _t meeting the formula

(3) Order theIt can be known that the smaller the delta _t is, the larger the angle P _iP_jP_k is, namely the smoother the path is, the threshold delta is selected, and when delta _t is more than delta, P _j is deleted, the path can be shortened;

And 4, integrating the motion paths of robots of all stations to enable the robots to execute processing tasks.