Path planning of mobile robot based on adaptive ant colony algorithm
Yan Zheng, Qiang Luo, Haibao Wang, Changhong Wang, Xin Chen
Abstract
The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms’ simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.
Topics & Concepts
Ant colony optimization algorithmsComputer scienceHeuristicMotion planningConvergence (economics)Mathematical optimizationPath (computing)Mobile robotLocal optimumAlgorithmState (computer science)Ant colonyLocal search (optimization)Shortest path problemArtificial intelligenceRobotMathematicsTheoretical computer scienceEconomicsProgramming languageGraphEconomic growthRobotic Path Planning AlgorithmsOptimization and Search ProblemsRobotics and Sensor-Based Localization