复杂环境下机器人路径规划方法研究

摘要

路径规划方法的优劣直接影响到移动机器人实际的工作能力,复杂的环境对于机器人路径规划的要求更高.针对复杂环境下蚁群算法搜索效率慢、易早敛的问题,结合势场法对其进行改进,提出基于势场优化的蚁群路径规划算法.初始解产生阶段,用势场法路径预规划获得的先验知识构造蚁群算法的信息素矩阵.寻优阶段构造综合启发函数,对蚂蚁的转移过程进行有目的地引导,同时提出参数自适应的伪随机转移策略,有助于蚂蚁寻找全局最优解.信息素更新过程中引入代价函数对路径规划结果进行平滑处理,得到适合机器人移动的最优路径.仿真结果表明,新算法明显提高了收敛速度和寻优能力,能够较好的满足复杂环境下机器人路径规划的需求.%Aiming at the problem that the search efficiency of the ant colony algorithm is slow and easy to converge in the complex environment,we propose a hybrid algorithm which combines the artificial potential field method with the ant colony algorithm for path planning.The initial generation of the pheromone of the ant colony algorithm is based on the pri-knowledge of the artificial potential method.In the searching stage,comprehensive heuristic function is constructed for purposeful guidance,and the parameter adaptive pseudorandom transfer strategy helps to find the optimal solution.In the pheromone updating process,the cost function is used to smooth the path planning result,and the optimal planning path suitable for the robot movement is obtained.The simulation results show that the new algorithm can improve the convergence speed and the optimization ability,and can meet the requirements of robot path planning in complex environment.