CollisionGAT: Controller-Agnostic One-Step Collision Screening for Multi-Agent Motion

Abstract:Before a team of robots moves, each proposed step must be checked for collisions with other robots and with obstacles. We present CollisionGAT, a graph-attention network that reads the current and proposed states of moving agents together with locally relevant stationary obstacles and returns one collision-risk score per moving agent. Any controller can use these scores to accept, repair, replan, or postpone a proposed step. We mount CollisionGAT on a continuous path-following controller and on GATeD, an obstacle-blind D* Lite planner that uses typed vetoes to update its planning graphs. Exact geometric checks supply the training labels and independently audit every executed step.

5 pages, 4 figures, 1 table, 2 algorithms. Accepted to the 2026 IEEE MIT Undergraduate Research Technology Conference (URTC)

Robotics (cs.RO); Machine Learning (cs.LG); Multiagent Systems (cs.MA)

arXiv:2609.32783 [cs.RO]

(or arXiv:2609.32783v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.32783 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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