Path planning is one of the key technologies for autonomous vehicles. Ant Colony Algorithm can effectively achieve the goal of path planning for autonomous vehicles, but the algorithm has the problems of low search efficiency and local optimal solution in path planning. Therefore, this paper improves the classical ant colony algorithm, using adaptive initial pheromone distribution range build initial pheromone distribution, at the same time improve stimulating factor enhanced heuristic search efficiency, and introduces the rollback strategy self-locking and deadlock problem and adopt preferential set limit to update pheromone strategy, help reduce blind ant search path, and reduce the redundancy of map information. The simulation results show that the improved ant colony algorithm can greatly improve the global search ability and convergence speed, and can help the autonomous vehicle to find the optimal path quickly.
Autonomous Vehicle Path Planning Based on Improved Ant Colony Algorithm
Lect. Notes Electrical Eng.
International Conference on Green Intelligent Transportation System and Safety ; 2021 November 19, 2021 - November 21, 2021
2022-10-28
10 pages
Article/Chapter (Book)
Electronic Resource
English
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