[Submitted on 23 Oct 2026]
Title:Neuromorphic Decision Dynamics for Deadlock-free Safe Navigation in Congested Environments
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Abstract:Safe navigation in congested environments requires both collision avoidance and coordinated passage. We introduce neuromorphic decision dynamics (NDD), a decentralized, event-based controller, that provides navigation in congested spaces with guarantees on safety, liveness, robustness, and scalability. NDD makes safe-and-live deconfliction a controllable, locally tunable emergent property of local agent interactions, without requiring communication, preassigned priorities, models of neighbors' objectives or future actions, or estimates of group size or global network properties. By design, NDD couples the dynamics of internal decision and attention states with changing physical states and locally observed relative motion. Each agent continuously modulates its speed along its prescribed path, responding as soon and as much as conflict resolution requires. Agents self-organize into disjoint, locally engaged groups that resolve conflicts concurrently. When any group's conflict becomes safety-critical, decision-action feedback destabilizes collective indecision, and coordinated conflict-resolution emerges that stabilizes a safe traversal order within the group. Order persists as conflicts clear, but groups may split or merge as agents autonomously adapt to new conflicts and those with resolved conflicts resume their nominal motion. We derive explicit conditions on sensing range, feedback gains, response time scales, and actuation limits that guarantee collision avoidance and traversal-order formation in finite time with no deadlock or livelock for finite, heterogeneous agent populations of arbitrary, changing size. Computational cost scales with the number of local interactions sensed by each agent. Large-scale simulations, comparisons with existing approaches, and hardware experiments demonstrate NDD in complex congested scenarios and validate its scalability.
Subjects:
Robotics (cs.RO); Multiagent Systems (cs.MA)
Cite as: arXiv:2610.33271 [cs.RO]
(or arXiv:2610.33271v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.33271
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Himani Sinhmar [view email] [v1] Fri, 23 Oct 2026 20:32:42 UTC (3,558 KB)
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