CN110763247A

CN110763247A - Robot path planning method based on combination of visual algorithm and greedy algorithm

Info

Publication number: CN110763247A
Application number: CN201910998309.7A
Authority: CN
Inventors: 唐刚; 唐从强; 鲁鹏; 胡雄
Original assignee: Shanghai Maritime University
Current assignee: Shanghai Maritime University
Priority date: 2019-10-21
Filing date: 2019-10-21
Publication date: 2020-02-07

Abstract

The invention relates to a robot path planning method based on a combination of a visual algorithm and a greedy algorithm. The method comprises the following steps: (1) and scanning the map environment related to the robot motion by using a computer, identifying obstacles in the map environment for enveloping, and screening vertexes through visual conditions. (2) Connecting the starting point and the target point, the connecting line can pass through a plurality of obstacles, the vertex of the barrier which is passed through is put into a set S, and the vertex of the barrier which is not passed through is put into another set V. (3) And continuously updating the next point and the current according to the minimum included angle principle. And solving the top point of each section, connecting the top points of all the obstacles, and finally obtaining the global optimal solution. (4) The method greatly reduces the calculated amount, eliminates unnecessary barriers and vertexes, greatly reduces the complexity of modeling composition, improves the operation efficiency and avoids the possibility of collision between the robot and the barriers in the motion process.

Description

Robot path planning method based on combination of visual algorithm and greedy algorithm

Technical Field

The invention relates to the field of robot path planning, in particular to a robot planning algorithm of a simplified visual graph method.

Background

In the traditional robot path planning, all obstacles are equivalent to a polygon set projected in a plane by using a visual graph method, after the free space is modeled, the robot path planning problem is converted into the shortest path problem of a network graph, and the existing mathematical theory is utilized: when the planar obstacle is a polygon, the shortest path is always a broken line from the starting point to the end point via the vertices of the visible obstacles, and the shortest path by which the robot bypasses the obstacle is obtained.

The visual principle of the visual graph method is defined as that the starting point, the target point and each vertex of the barrier are connected in a combined mode, and the connecting lines among the starting point, the target point and each vertex of the barrier cannot penetrate through the barrier, namely, a straight line is visible. Its advantages are visual concept and simple structure. The shortest path from the starting point to the target point can be obtained, and the defects are that the robot is equivalent to one point theoretically, the influence of the size of the robot on path planning cannot be considered, the number of possible paths is increased gradually along with the increase of the complexity of the environment, and the calculation amount is large.

The greedy algorithm generally divides the solving process into a plurality of steps, but each step applies a greedy principle, selects the best or optimal selection in the current state, and hopes that the result finally stacked is also the best or optimal solution. The greedy algorithm adopts a top-down iteration method to make successive selection, and each time the greedy selection is made, the problem to be solved is simplified into a sub-problem with smaller scale.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a method based on the combination of a visual graph method and a greedy algorithm. The problems of complex modeling, various paths and low calculation efficiency of the traditional visual graph method are solved, and the method has the advantages of strong self-adaptive capacity and high calculation efficiency.

The technical scheme adopted by the invention is as follows: a robot path planning method based on the combination of a visual graph method and a greedy algorithm is carried out according to the following steps:

s1, scanning the map environment related to the robot motion by the computer, identifying obstacles in the map environment, enveloping the obstacles by a closed polygon, determining the maximum geometric dimension of the robot as l, calculating the parallel envelope of the robot as a graph with a shift of 0.5l, and keeping the graph (as shown in FIG. 1). The selection of the top point needs to meet the visual condition that (1) the line segment of the starting point and the top point of the barrier cannot cross any barrier (2) the line segment of the current top point and the next top point of the barrier cannot cross any barrier.

S2, determining a starting point S and a target point g, constructing a rectangular coordinate system by taking a connecting line of the starting point and the target point as an x axis, initializing the remaining vertexes in the map environment to obtain corresponding coordinates of each polygon vertex, and assigning an access starting point, wherein the initialization function and the starting point assignment function are as follows:

(1)

(2)

(3) θ(s,s)＝0

(4) θ(s,g)＝0

wherein, θ (s, A)_i) Denotes a start point s and a vertex A_iThe coordinate of the starting point s is (0,0), and the coordinate of the target point g is (x)_g,0). With A_iThe included angle formed by the connecting line of each vertex and the starting point s on the obstacle and the x axis is calculated by representing the selected vertex on the obstacle. The smaller the included angle formed, the shape of the polygonal line path formed is the closest to the shortest path, which is the principle of minimum included angle (as shown in fig. 3). And selecting the line segment with the minimum included angle as the first section of shortest path, and recording the top point on the barrier as a node to obtain the first section of shortest path and the first section of locally optimal top point.

S3 represents the shortest path connecting the starting point and the target point by line segment as an auxiliary line l₁. Auxiliary line l₁A plurality of obstacles are crossed, the vertexes of the obstacles crossed by the auxiliary lines are put into a set S, and the vertexes of the obstacles not crossed are put into another set V.

S4 puts the points in set S that satisfy the visual condition into set M, and the vertices that do not satisfy the condition remain in set S. Determining the vertex A of the first segment from the set M according to the principle of minimum included angle_iWhile clearing the remaining points in set M (as shown in fig. 6).

S5 shows the vertex A_iRecording as current point, connecting the current point and the target point to form a second auxiliary line l₂Auxiliary line l₂Will pass through some barriers again, the auxiliary line l is selected from the set V₂The vertices on the traversed barrier are updated into S. According to the same method, the line segment with the minimum included angle in the set M is selected as the second shortest path, and the next point in the current shortest path is updated as the current point of the next segment.

S6 is repeated to find the local optimal solution vertex of each segment, and the polygonal line formed by connecting the local optimal vertices of the respective obstacles is also the closest shortest path in shape, thereby obtaining the global optimal solution.

Has the advantages that: the method comprises the steps of generating a necessary visual graph through a set rule, constructing a connecting line between a starting point S and a target point g as an auxiliary line, dividing vertex coordinates on an obstacle into a set S and a set V according to whether the auxiliary line passes through the obstacle, and then further screening the set S by using a visual condition to obtain a set M of possible vertexes. Then, according to the minimum included angle principle, calculating the minimum angle between the segment connected with the vertex in the set M and the starting point s and the minimum angle included by the x axis, so as to obtain the optimal solution vertex of the first segment, and finally, gradually transitioning from the local optimal solution to the global optimal solution by using the setting of a greedy algorithm; meanwhile, in the process of drawing construction, vertex information in the set S and the set V is continuously updated, new vertices are accessed, and redundant vertex coordinates in the set M are deleted, so that the calculated amount is greatly reduced; unnecessary barriers and peaks are eliminated by setting a visual rule, so that the complexity of modeling composition is greatly reduced, and the operation efficiency is improved; and finally, an envelope curve of the deviation of the obstacle is set, so that the robot is prevented from possibly colliding with the obstacle in the movement process.

Drawings

FIG. 1 is a schematic diagram of a parallel envelope of obstacles according to the present invention

FIG. 2 is a first view of the present invention

FIG. 3 is a second view of the present invention

FIG. 4 is a third view of the present invention

FIG. 5 is a fourth view of the present invention

FIG. 6 is a block diagram of a process for identifying a first leg of a path according to the present invention

Detailed Description

The invention is described in detail below by way of examples and figures to facilitate the understanding of the invention by those skilled in the art, but it is to be understood that the invention is not limited to the scope of the specific embodiments, and that various changes may be apparent to those skilled in the art without departing from the spirit and scope of the invention as defined and defined by the appended claims, and all matter which is encompassed by the invention utilizing the inventive concept is protected.

Example one: a robot path planning based on the combination of a visual algorithm and a greedy algorithm is characterized in that obstacles are firstly structured into polygons, and then the polygons are subjected to envelope offset by 0.5l, so that the outline of the polygons is finally obtained.

As shown in fig. 1 to 5, the path planning method includes the following steps:

s1 determining origin and establishing rectangular coordinate system

And determining a starting point s and a target point g, constructing a rectangular coordinate system by taking the starting point s as an origin of coordinates and a connecting line of the starting point s and the target point g as an x axis, and recording the vertex coordinates of each polygon.

S2 divides set S and set V

Connecting the starting point s and the target point g to obtain an auxiliary line l₁. Auxiliary line l₁A plurality of barrier polygons are traversed (as shown in fig. 2, connecting lines traverse barrier A, B), vertices on the traversed barrier are placed in set S (i.e., vertices on barrier A, B are added to set S), and vertices on the non-traversed barrier are placed in another set V (vertices of the barrier outside A, B are placed in set V).

S3 obtaining a set M

The set M is defined as the set of all possible nodes, and the line segment of the starting point and the top point of the barrier cannot cross any barrier according to the visual condition (1) (2) and the line segment of the current top point and the next top point of the barrier cannot cross any barrier. And selecting nodes meeting the visual condition from the set S and putting the nodes into the set M, namely, the local optimal nodes of the first section of path exist in the set M at the moment, and the rest nodes are left in the set S.

S4 screening local optimal nodes of the first path

Because the local optimal node of the first segment of the path exists in the set M, when the included angle formed by the straight line formed by the starting point s and the points in the set M and the x axis is smaller, the shape of the straight line in the coordinate system is also closest to the x axis, and the obtained line segment is also the current local optimal broken line. When the angles formed by two nodes and the starting point s are equal, the point close to the x axis is selected as the node of the first path, namely the local optimal node.

S5 determining the local optimum node of the second path

Recording the local optimal node of the first section of path as a current point, and connecting the current point and the target point g to form a second auxiliary line l₂Auxiliary line l₂And traversing some obstacles again (as shown in fig. 4), updating vertex information of the obstacles traversed by the auxiliary lines in the set V into a set S, and finding out appropriate points from the set S according to the same method and updating the appropriate points into a set M. And finally, according to the minimum included angle principle, finding the local optimal node of the second section of path from the set M.

S6 from the local optimal node to the global optimal path

According to the same method, the steps S3 to S5 are repeated, and the information of the possible nodes in the set S and the set V is continuously updated, so that the paths of the third step, the fourth step or even the nth step and the corresponding local optimal nodes are determined until the target point is reached, and all the obtained local optimal nodes are connected, so that the final broken line graph, namely the global optimal path, is obtained (as shown in fig. 5).

In fig. 2 to 5, the solid line indicates the actual path, and the broken line indicates the auxiliary line. And continuously updating the set S and the set V through a visual condition and an included angle minimum principle, so as to obtain a local optimal vertex of each section of path and finally obtain a global optimal solution. And connecting all the vertexes into a broken line to obtain the shortest path through which the robot can avoid the obstacle in the movement process.

It is to be understood that the above-described examples are merely illustrative of some of the principles, concepts and processes of the invention in a detailed description and are not restrictive. It will be apparent to those skilled in the art that various modifications and variations can be made in the form and arrangement of the parts without departing from the scope and spirit of the invention to adapt it to various environments and requirements.

Claims

1. The robot path planning method based on the combination of the visual graph method and the greedy algorithm is characterized in that the maximum geometric dimension of the robot is l, and the specific operation steps are as follows:

s1 divides set S and set V:

determining a starting point S and a target point g, connecting the starting point and the target point by line segments, enabling a connecting line to pass through a plurality of obstacles, putting vertexes of the obstacles which pass through the connecting line into a set S, and putting vertexes of the obstacles which do not pass through the connecting line into another set V;

s2 goes from the local optimal solution to the global optimal solution:

determining a local optimal solution for a segment path: determining the vertex of the first section from the set S as A1 according to the visual condition and the minimum included angle principle, and connecting the vertex A1 with the starting point S to form a first section path;

determining a local optimal solution of a second segment path: connecting the vertex A1 of the first section with the target point g to form an auxiliary line, wherein the auxiliary line passes through some obstacles again, updating the vertices of the obstacles passed through by the auxiliary line in the set V into the set S, and finding out the optimal point of the second section from the set S according to the same method; and analogizing in sequence, determining the paths of the third step and the fourth step until reaching the target point, and obtaining the final broken line graph which is the global optimal path.

2. A method for robot path planning based on a combination of visual and greedy algorithms according to claim 1, characterized in that in step S1, sets S and V differentiated by whether obstacles are crossed are determined, and the sets S and V are updated according to the subsequent continuous construction of auxiliary lines.

3. The method for robot path planning based on combination of visual and greedy algorithms according to claim 1, wherein in step S2, the included angle between the auxiliary line and the x-axis is determined according to the included angleJudging whether the line segment is the current optimal local path or not; when the included angle is smaller, the path is also closest to the shortest path; and simultaneously updating the current point to the next point of the next segment of path, and determining the transition to the final global optimal path from the local optimal path.