Adaptive replanning in hard changing environments

Abstract

Replanning is a powerful tool for high dimensional mobile agents in changing environments. However, most works employ replanning periodically. In order to fully exert the merits of this powerful tool, we should concentrate on the time interval employed for each replanning (that is “when to replan”) and carry out replanning adaptively. In this paper, an adaptive strategy is proposed to govern replanning in hard changing environments. The key point of this adaptive replanning strategy is to perform local environment accumulation by using grids method, which is a derivative of degenerated potential field. Since the accumulation is only performed locally in the regions between subgoals and only computed towards the changes of obstacles, it increases little computational complexity to parent anytime planners. Our adaptive replanning strategy works as a plug-in to state-of-the-art algorithms and can generate heuristics by using information from projected spaces to overcome high dimensionality. Experiments on different mobile agents in various hard changing environments (environments with crowded and unforseen obstacles) with IDRM-gRRT and IRRT-gRRT showed that the adaptive strategy can improve the performance and robustness of parent anytime planners significantly.