Motion planning is a crucial, basic issue in robotics, which aims at driving vehicles or robots towards to a given destination with various constraints, such as obstacles and limited resource. This paper presents a new version of rapidly exploring random trees (RRT), that is, liveness-based RRT (Li-RRT), to address autonomous underwater vehicles (AUVs) motion problem. Different from typical RRT, we define an index of each node in the random searching tree, called “liveness” in this paper, to describe the potential effectiveness during the expanding process. We show that Li-RRT is provably probabilistic completeness as original RRT. In addition, the expected time of returning a valid path with Li-RRT is obviously reduced. To verify the efficiency of our algorithm, numerical experiments are carried out in this paper.

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random searching tree expected time rapidly exploring random trees rrt planning limited autonomous underwater vehicles auvs motion lirrt robotics

Yang Li,Fubin Zhang,Demin Xu,Jiguo Dai,.Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning. 2017 (),.

Yang Li,Fubin Zhang,Demin Xu,Jiguo Dai,"Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning" 2017

Yang Li,Fubin Zhang,Demin Xu,Jiguo Dai,"Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning"