Mobile robot path planning method based on improvement of ant colony algorithm and particle swarm optimization

Abstract

The invention discloses a mobile robot path planning method based on an improvement of an ant colony algorithm and particle swarm optimization. The method mainly solves the problems that in the prior art, the operating speed of an algorithm is low, and frequency of turning of an optimized path is high. The planning method includes the steps that modeling is carried out on a work environment of a robot; the particle swarm optimization is utilized to quickly carry out path planning, pheromones more than those around an obtained path are scattered on the obtained path, and guiding is provided for an ant colony; an ant colony algorithm optimized by the principle of inertia is adopted, and optimization is conducted on the basis of the particle swarm optimization; the motion path of the robot is output according to an optimization result. According to the planning method, comprehensive consideration is given to stability and robustness of the algorithm, iterations can be effectively reduced, searching efficiency is improved, the path length is shortened, the frequency of turning is reduced, path quality is substantially improved, and the planning method accords with an artificial planning intention and is suitable for autonomous navigation of various mobile robots in a static environment.

Description

technical field [0001] The invention relates to the technical field of mobile robots, in particular to a path planning method for a mobile robot based on an improved ant colony particle swarm algorithm in a global static environment. Background technique [0002] Path planning is one of the key technologies of mobile robots. To a certain extent, it marks the intelligence level of mobile robots. Being able to quickly find a convenient and collision-free path not only ensures the safety of the mobile robot itself, but also reflects the robot's Efficiency and reliability. [0003] At present, the commonly used path planning methods include visual graph method, heuristic graph search algorithm, artificial potential field method, A* algorithm, etc. These algorithms have their own advantages and disadvantages. For example, the artificial potential field method has good real-time performance, but there are trap areas, and the path cannot be found between similar obstacles. The A* ...

AI Technical Summary

Problems solved by technology

These algorithms have their own advantages, but there are also many problems, such as algorithm complexity, local optimum, too large search space, etc.

These algorithms have high requirements on hardware conditions and do not meet the real-time requirements of mobile robots.

[0004] Although many algorithms have been improved at this stage, and the optimal path can be better found, there are still problems such as a large number of iterations and too long calculation time, which cannot well meet the real-time requirements of mobile robots; and , there are many turns in the obtained path, which will seriously affect the work efficiency of the mobile robot and reduce the work reliability

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