Improved ant colony algorithm-based mobile robot global path planning method

Abstract

The invention discloses an improved ant colony algorithm-based path planning method. In comparison with the traditional ant colony algorithm, an ant path search strategy is modified to jump out of partial obstacles with special shapes; a heuristic function in the traditional ant colony algorithm probability selection formula is modified, and target node information is added to the heuristic function to accelerate the convergence of the algorithm; and a volatilization coefficient in the traditional ant colony algorithm probability selection formula is modified, and time and space information isadded into the volatilization coefficient to improve the adaptability of the algorithm. Through the above improvements, the convergence time of the ant colony algorithm can be effectively reduced, and the operation efficiency is improved.

Description

[0001] Technical field: [0002] The invention relates to the field of artificial intelligence, and specifically designs a mobile robot global path planning method based on an improved ant colony algorithm. [0003] technical background: [0004] Mobile robot path planning is an important part of the robotics research field. Its task is to find a path from the initial state (including the minimum work cost, the shortest route, etc.) in an environment with obstacles, according to certain evaluation criteria The shortest path from position and attitude) to the target state (including position and attitude) without collision with obstacles. According to the degree of known environmental information, mobile robot path planning can be divided into global path planning with known environment and local path planning with unknown environment. Global path planning is mainly divided into two problems, environment construction and search algorithm. At present, Visible view method, grid method,...

AI Technical Summary

Problems solved by technology

[0005] The ant colony algorithm was first successfully applied to solve the famous TSP (Travelling salesman problem). This algorithm uses a distributed parallel computing mechanism, which is easy to combine with other methods and has strong robustness. Therefore, the ant colony algorithm is often used Applied to the path planning of mobile robots, but the traditional ant colony algorithm has problems such as blindness in the early search, slow convergence speed, and easy to fall into local optimum. In view of the limitations of the traditional ant colony algorithm in robot path planning, many patents also Corresponding improvements have been made to the algorithm. For example, the patent of publication number CN107272679A improves the ant colony algorithm by operating the inflection point of the path and reducing redundant nodes; the patent of publication number CN108413963A integrates machine learning ideas into the ant colony algorithm. Improved Ant Colony Algorithm; Patent Publication No. CN108241375A improves the Ant Colony Algorithm by constructing a point-to-point self-adaptive path selection strategy, and using segmented and combined state transfer optimization methods to select optimal path points. However, the above patent does not involve the path Improvement of search strategy, so far, no patent records similar to the present invention have been seen

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