Asymptotically Optimal Lazy Lifelong Sampling-based Algorithm for Efficient Motion Planning in Dynamic Environments

TL;DR

This paper presents an asymptotically optimal lifelong sampling-based motion planning algorithm that efficiently replans in dynamic environments by evaluating only promising sub-paths, significantly reducing computation time.

Contribution

The paper introduces a novel lazy lifelong planning algorithm with an informed rewiring cascade, improving efficiency and optimality in dynamic, high-dimensional environments.

Findings

Outperforms state-of-the-art planners in static and dynamic tasks

Reduces planning time by evaluating fewer sub-paths

Successfully plans for a Turtlebot 4 in real-world dynamic scenarios

Abstract

The paper introduces an asymptotically optimal lifelong sampling-based path planning algorithm that combines the merits of lifelong planning algorithms and lazy search algorithms for rapid replanning in dynamic environments where edge evaluation is expensive. By evaluating only sub-path candidates for the optimal solution, the algorithm saves considerable evaluation time and thereby reduces the overall planning cost. It employs a novel informed rewiring cascade to efficiently repair the search tree when the underlying search graph changes. Theoretical analysis indicates that the proposed algorithm converges to the optimal solution as long as sufficient planning time is given. Planning results on robotic systems with $\mathbb{SE}(3)$ and $\mathbb{R}^7$ state spaces in challenging environments highlight the superior performance of the proposed algorithm over various state-of-the-art sampling-based planners in both static and dynamic motion planning tasks. The experiment of planning for a Turtlebot 4 operating in a dynamic environment with several moving pedestrians further verifies the feasibility and advantages of the proposed algorithm.