一种有效的移动机器人行为融合避障方法

移动机器人避障路径规划问题一直是移动机器人自主导航问题的关键。现有的避障路径规划算法多数针对某一指定目标寻优过程,经常出现为了获得最优解或最优路径而导致死锁或震荡现象。引进多目标优化理论,提出一种基于动态区间权重的多目标优化避障算法,该算法把避障过程划分为3种动作行为融合过程,通过赋予行为函数不同的权重,实时动态改变不同动作行为的输出比率。不是获得当前时刻最优解或最优路径,而是仅获得当前时刻的最有效解、最满意路径。从理论上分析并证明多行为输出避障导航系统的有效性和鲁棒性。试验表明该算法可以在确保移动机器人避障过程的实时性和鲁棒性前提下有效地提高避障过程的灵活性和安全性。

Efficient Method of Obstacle Avoidance Based on Behavior Fusion for Mobile Robots

Obstacle avoidance is the key of autonomous navigation process for mobile robots all along.The current methods are almost the optimizing process for some one designated destination.Some deadlocks or collision phenomena often occur for the optimal solution or path.Therefore,the theory of multiple objective optimizations is introduced and an approach of path planning is proposed based on dynamic field weighting.The algorithm divides the obstacle avoidance into the process of three behaviors fusion.It adjusts the ratio of behaviors output by setting different weighting for different sub behavior function.The solution or path obtained at current moment is not optimal,but the most efficient or most satisfying.The availability and robustness of the obstacle avoidance system are analyzed and approved in theory.From the real and simulation experiments of autonomous navigation,the algorithm not only retains the real-time and robustness of obstacle avoidance,but also improves its availability and safety.