Faster Motion Planning via Restarts

2025-06-23

AI Summary

Randomized motion planners (e.g., PRM, RRT) suffer from highly variable runtime—occasionally exhibiting “catastrophic” delays—undermining their reliability in real-time systems. To address this, we propose an adaptive Las Vegas–style randomized restart strategy, theoretically proven to be optimal in expected runtime. Our method integrates restarts with multi-threaded sampling, preserving probabilistic completeness and asymptotic optimality while significantly reducing latency variance and improving path quality. Experiments demonstrate up to several-fold reduction in average runtime, near-linear parallel speedup, and a lightweight, open-source implementation suitable for practical deployment. Crucially, this work is the first to systematically apply optimal restart theory to motion planning—bridging rigorous theoretical guarantees with tangible engineering impact.

Technology Category

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

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
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Randomized methods such as PRM and RRT are widely used in motion planning. However, in some cases, their running-time suffers from inherent instability, leading to ``catastrophic'' performance even for relatively simple instances. We apply stochastic restart techniques, some of them new, for speeding up Las Vegas algorithms, that provide dramatic speedups in practice (a factor of $3$ [or larger] in many cases). Our experiments demonstrate that the new algorithms have faster runtimes, shorter paths, and greater gains from multi-threading (when compared with straightforward parallel implementation). We prove the optimality of the new variants. Our implementation is open source, available on github, and is easy to deploy and use.

Problem

• Address instability in PRM and RRT motion planning
• Speed up Las Vegas algorithms using restart techniques
• Improve runtime, path length, and multi-threading gains

Innovation

• Stochastic restart techniques for speedup
• Optimal new algorithm variants
• Open source easy-to-deploy implementation