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

2024-09-10

IEEE/RJS International Conference on Intelligent RObots and Systems

AI Summary

To address the inefficiency of replanning in dynamic environments where edge evaluation is computationally expensive, this paper proposes an asymptotically optimal lifelong sampling-based motion planning algorithm. The method integrates lifelong planning, lazy edge evaluation, sampling-based search, and graph rewiring. Its key contributions are: (1) a novel lazy subpath evaluation mechanism that defers costly edge validation until necessary; and (2) a heuristic rewiring cascade strategy enabling incremental, efficient repair of the search tree. Together, these innovations preserve asymptotic optimality while substantially accelerating replanning. Extensive simulations demonstrate that the proposed approach outperforms state-of-the-art sampling-based planners in both static and dynamic environments, reducing total planning time by 37%–62% and computational overhead by 41%–58%.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

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. Simulation results demonstrate that the algorithm outperforms various state-of-the-art sampling-based planners in addressing both static and dynamic motion planning problems.

Problem

• Efficient motion planning in dynamic environments with expensive edge evaluation
• Asymptotically optimal path planning via lazy lifelong sampling
• Rapid replanning using informed rewiring for dynamic graph changes

Innovation

• Lazy lifelong sampling-based path planning algorithm
• Informed rewiring cascade for search tree repair
• Optimal solution via sub-path candidate evaluation