Dynamic path planning method for improving particle swarm optimization

Abstract

The invention discloses a dynamic path planning method for an improved particle swarm algorithm, which is characterized by comprising the following steps of: 1, generating a global path by adopting the improved particle swarm algorithm, and dividing the global path into n sections of local paths consisting of {S1G1, S2G2,... SnGn}; step 2, performing path planning on the local path S1G1 by using a dynamic window method DWA, including building a robot kinematics model, building a robot speed model, building an objective function of the dynamic window method DWA, selecting an optimal track according to the objective function, and performing path planning on the local path S1G1 by using the optimal track; step 3, performing local path planning on the local path S2G2 by using a dynamic window method DWA, and inheriting the course angle of the previous motion planning; 4, the dynamic window method DWA is repeatedly used for conducting local path planning on the paths S3G3,..., SnGn in sequence; and step 5, outputting a complete final path. The method has the characteristics of shortening the length of the planned path, improving the smoothness and real-time performance of the planned path and the like.

Description

technical field [0001] The invention relates to the field of robot motion planning, in particular to a dynamic path planning method for an improved particle swarm algorithm. Background technique [0002] The dynamic path planning of a mobile robot refers to planning a collision-free path to the target point that satisfies certain conditions (usually refers to the optimal) in a dynamic environment. It can be applied in the field of intelligent warehousing to reduce manpower and material resources and improve overall transportation efficiency. Existing techniques include global path planning with known environment and local path planning with unknown environment. The global planning algorithm is mainly aimed at the static known environment, and the map is constructed in advance, which can ensure that the robot reaches the target position with a short path. Commonly used global path planning algorithms include intelligent bionic algorithms (genetic algorithm, ant colony algor...

AI Technical Summary

Problems solved by technology

Particle Swarm Optimization (PSO) is widely used in global path planning because of its simple structure and easy implementation. However, the existing PSO algorithm has low precision, many turning angles, and the generated path is not smooth and the real-time path is poor; while the dynamic window method is local Path planning algorithm, strong real-time performance, but easy to fall into local optimum

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