Static Path Planning Method for Robot

Abstract

A robot static path planning method, comprising: setting a target point as an end point and establishing an artificial potential field in a range of a map; introducing a particle swarm algorithm and setting m particle swarms at the start point of a robot, the flight speed of the i-th particle at the t-th step being ⃑Vi(t); simulating walking along the path of each particle from the start point to the end point according to the artificial potential field and the particle swarm algorithm, each particle forming a movement trajectory during the walk simulation; most of the particles gradually gather and converge toward one of a plurality of trajectories, thus obtaining an optimal walk path from the start point to the end point within the range of the map; the robot finally moves along the optimal walk path to finish the movement from the start point to the end point. The path planning method combines a potential field method, a grid method and a particle swarm algorithm to directly obtain a potential field distribution on a grid map, so as to obtain a pre-planned path along the direction where the potential field drops fastest from the potential field target point, thus being safe and effective, and ensuring accurate and reliable path planning.

Description

technical field [0001] The invention relates to a static path planning method for a robot, and belongs to the technical field of robot path planning. Background technique [0002] The path planning problem of self-moving robot is one of the core problems in the field of robotics. According to the knowledge of the space environment of the robot, the path planning problem can be divided into static path planning and dynamic path planning. Aiming at the problem of path planning, many algorithms have been proposed, mainly including potential field method, grid method and particle swarm method. Taking the traditional artificial potential field method as an example, it uses a method based on vector synthesis to plan the movement path of the robot under the combined force of the repulsive force from the obstacle and the attractive force from the target point through intuitive rules. . In the case of only local information, its obstacle avoidance strategy is very effective. Howev...

AI Technical Summary

Problems solved by technology

However, since the speed and direction of the robot's movement depend on the magnitude and direction of the force vector sum, when the resultant force is zero, the robot cannot move. This is why the artificial potential field method is easy to fall into a local minimum point.

For the particle swarm optimization algorithm, in the initial stage, the walking paths of the particles are scattered, and the convergence speed of the paths is slow, which affects the efficiency of path planning.

Therefore, different algorithms have their own advantages, but also have their methodological limitations

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