A random local-DRM path planning algorithm for dual manipulator mobile robots in changing environments

Path planning for mobile robots with high degree of freedom (DOF) is an extreme challenge. Although lots of algorithms have focused on the planning of fixed manipulators and mobile robots with low degree of freedom, seldom of them can be employed to deal with high DOF mobile agents. The unpredictable obstacles and too many freedoms increased computational complexity dramatically. In this paper, a novel and general real-time approach is introduced to solve this problem. The core of this approach can be divided into two phases. By locally employing Dynamic Roadmap Mapping, a mobile robot can seek a collision-free path locally without too much collision detection. And a hierarchy sampling strategy is employed to treat narrow passages. Based on the idea of Rapidly-Exploring Random Tree, a high-level guide is developed by randomly generating subgoals. These two phases collaborate to help generate a desired path. Experimental results show that our algorithm can find out a collision free path in real time for mobile robots with 15 DOF in complex environments in the presence of both stationary and changing obstacles.