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arXiv:2610.41558 · v2

A complete analytical characterization of optimal collision-free motion for two robots

首次提交 2026-10-30 18:53
版本更新 2026-11-08 23:23
雷达观测 2026-11-10 12:16
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摘要

Collision-free motion planning for multi-agent robotic systems remains an active research area because of the complexity of coordinating robot trajectories, with applications ranging from coordinated drone shows to autonomous operations on land, in the air, and on water. Importantly, the analytical difficulty of nonconvex collision-avoidance constraints has so far driven the development of exclusively numerical methods. We derive a complete analytical characterization of the optimal trajectories for two robots with zero initial and final velocities, under double-integrator dynamics and a squared-acceleration objective. We prove that these trajectories belong to eight distinct contact families and determine which family applies for each configuration of initial and final positions. We analyze the geometrical properties of the optimal trajectories and provide a numerical algorithm for computing them. These insights have already been adopted to improve the performance of numerical methods for multi-agent motion planning by several orders of magnitude.

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v2 · 2026-11-08 23:23 · 最新观测版本

A complete analytical characterization of optimal collision-free motion for two robots

Collision-free motion planning for multi-agent robotic systems remains an active research area because of the complexity of coordinating robot trajectories, with applications ranging from coordinated drone shows to autonomous operations on land, in the air, and on water. Importantly, the analytical difficulty of nonconvex collision-avoidance constraints has so far driven the development of exclusively numerical methods. We derive a complete analytical characterization of the optimal trajectories for two robots with zero initial and final velocities, under double-integrator dynamics and a squared-acceleration objective. We prove that these trajectories belong to eight distinct contact families and determine which family applies for each configuration of initial and final positions. We analyze the geometrical properties of the optimal trajectories and provide a numerical algorithm for computing them. These insights have already been adopted to improve the performance of numerical methods for multi-agent motion planning by several orders of magnitude.

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