复杂环境下基于多目标粒子群的DWA路径规划算法

DWA path planning algorithm based on multi-objective particle swarm optimization in complex environment

针对机器人在障碍物分布密集的复杂环境中运行时,动态窗口法(dynamic window approach,DWA)易出现避障失败或规划不合理的情况,提出一种基于多目标粒子群优化算法(multi-objective particle swarm optimization,MOPSO)的改进DWA规划算法。在建立多障碍物环境覆盖模型的基础上,提出一种障碍物密集度的判断方法;优化DWA算法中的子评价函数;利用改进的MOPSO算法实现DWA权重系数的动态调整,将权重系数的自适应变化问题转化为多目标优化问题;根据路径规划的要求将安全距离和速度作为优化目标,并使用改进的MOPSO算法对相应的多目标优化模型进行优化求解。仿真结果表明,该算法使机器人有效地通过障碍物密集区的同时兼顾了运行的安全性和速度,具有更好的路径规划效果。

When the robot is running in a complex environment with densely distributed obstacles,the DWA(dynamic window approach)algorithm is prone to obstacle avoidance failure or unreasonable planning.In this regard,an improved DWA planning algorithm based on MOPSO(multi-objective particle swarm optimization)was proposed.Based on the establishment of multi obstacle environment coverage model,a method was put forward for judging obstacle-dense areas in complex environments.And the original DWA algorithm was improved by optimizing the sub-evaluation functions.On these basis of the improved MOPSO algorithm,the adaptive change of DWA weight coefficients were transformed into a multi-objective optimization problem.According to the requirements of path planning,the safety distance and speed can be set as the optimization goals,moreover,the corresponding multi-objective optimization model was established.The results of a series of simulations show that this method enables the robot to effectively pass through the dense area of obstacles while taking account of the safety and speed of operation,and has better path planning effect.