Potential and Sampling Based RRT Star for Real-Time Dynamic Motion Planning Accounting for Momentum in Cost Function

摘要

Path planning is an extremely important step in every robotics related activity today. In this paper, we present an approach to a real-time path planner which makes use of concepts from the random sampling of the Rapidly-exploring random tree and potential fields. It revises the cost function to incorporate the dynamics of the obstacles in the environment. Not only the path generated is significantly different but also it is much more optimal and rigid to breakdowns and features faster replanning. This variant of the Real-Time RRT* incorporates artificial potential field with a revised cost function.