Extended rapidly exploring random tree–based dynamic path planning and replanning for mobile robots

Abstract

It is necessary for a mobile robot or even a multi-robot team to be able to efficiently plan a path from its starting or current location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing its path. In this article, a new method of dynamic replanning is proposed to allow the robot to efficiently plan a path in such complex environments. Our proposed replanning method is based on an extended rapidly exploring random tree. The robot will modify its current plan when unknown random moving obstacles obstruct the path. We extend the proposed replanning method to multi-robot scenarios in which the ability to share path planning and search tree information is valuable. An efficient method of node sharing is proposed to allow the multi-robot team to quickly develop path plans. Various experimental results in both single and multi-robot scenarios show the effectiveness of the proposed methods.