Fast and Flexible Sampling-Based Local Replanning for Single-Query Paths in Unknown Environments

Abstract

Path planning in unknown environments remains a challenging research problem in autonomous robotics. Although single-query path planning algorithms such as Rapidly-exploring Random Trees (RRT) and its variants have been proven effective in environments where the obstacle information does not change during the mission, their ability to adapt to unforeseen obstacles during navigation is limited. This limitation is particularly evident when robots encounter static obstacles not part of the initial information about the environment. In such cases, the robot must replan its trajectory to avoid collisions and continue its task efficiently. To this end, we propose a sampling-based fast replanning strategy, which is easy to implement yet effective. Importantly, our proposed approach allows the developers to easily plug-and-play different single-query path planning techniques (e.g., RRT*, RRT-connect). We have tested our proposed approach through MATLAB simulations. When compared to RRT-X, an asymptotically optimal single-query sampling-based motion planning technique that provides quick replanning, our proposed approach outperforms RRT-X in runtime while showing a modest trade-off in the path length metric.