A Novel Dynamic Motion Planning Based on Error Tolerance Batch Informed Tree*

Abstract

In a static environment for robotic manipulators, many present motion planning algorithms can generate collision-free paths effectively. But in a human-robot work scenario or a unstructured environment, these algorithms may fail or difficultly avoid obstacles in real-time planning due to missing updating environment model. To realize dynamic obstacle avoidance, a novel dynamic motion planning algorithm, called error tolerance batch informed tree (et-BIT*) is proposed. Experiments showed that the et-BIT* planner can avoid a suddenly appeared obstacle and bypass it by choosing a candidate motion path.