A Knowledge-Based Fast Motion Planning Method Through Online Environmental Feature Learning

The sampling-based partial motion planning algorithm has come into widespread application in dynamic mobile robot navigation due to its low calculation costs and excellent performance in avoiding obstacles. However, when confronted with complicated scenarios, the motion planning algorithms are easily caught in traps. In order to solve this problem, this paper proposes a knowledge-based fast motion planning algorithm based on Risk-RRT, which guides motion planning by constructing a topological feature tree and generating a heuristic path from the tree. Firstly, an online topological feature learning method is proposed to simultaneously extract the features during the motion of the robot by means of the dual-channel scale filter and the secondary distance fusion. The learning process is completed until the feature points can represent arbitrary obstacle-free grid points of the whole map. Secondly, the topological feature tree is constructed with environmental feature points and the heuristic motion planning can be carried out on the feature tree. For one map, once the construction of the feature tree finishes, it can be reused as a prior knowledge in the following heuristic motion planning process, which will further improve the efficiency of searching feasible paths. The experimental results demonstrate that our proposed method can remarkably reduce the time taken to find a heuristic path and enhance the success rate of navigation in trapped environments.