Multi-Robot Motions in Milliseconds: Vector-Accelerated Primitives for Sampling-Based Planning

2026-04-26

AI Summary

This study addresses the challenges of low computational efficiency and poor real-time responsiveness in multi-robot motion planning. It introduces vectorized acceleration primitives to this domain for the first time, proposing SIMD-based parallel mechanisms for motion validation (MotVal) and conflict-free checking (FFC), which are integrated into a sampling-based multi-robot planning framework. Experimental results demonstrate that the proposed approach accelerates motion validation by over 1,100× and improves overall planning efficiency by more than 850×, enabling the generation of high-quality multi-robot motion plans within milliseconds. This substantial performance gain significantly advances the feasibility of real-time, large-scale multi-robot coordination.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we extend the recent Vector-Accelerated Motion Planning (VAMP) framework to multi-robot motion planning (MRMP). We develop two vector-accelerated primitives, multi-robot MotionValidation (MotVal) and FindFirstConflict (FFC), which exploit SIMD parallelism within the multi-robot domain. On pure multi-robot motion validation tests, this achieves over 1100X speedup in validation time. Additionally, we modify a representative set of MRMP algorithms to use these new primitives. The relative speedup for each algorithm is studied on scenarios with manipulator, rigid body, and heterogeneous teams with some instances producing multi-robot solutions in the order of milliseconds and, in many cases, shows planning time speedups of over 850X.

Problem

• multi-robot motion planning
• motion validation
• computational efficiency
• planning time
• SIMD parallelism

Innovation

• Vector-Accelerated Motion Planning
• Multi-Robot Motion Planning
• SIMD Parallelism
• Motion Validation
• FindFirstConflict