arXiv cs.MA· Himani Sinhmar, Vaibhav Srivastava, Naomi Ehrich Leonard·· 12 小时前AI 评分34
神经形态决策动力学 NDD：拥堵环境中无死锁的安全导航控制器
Neuromorphic Decision Dynamics for Deadlock-free Safe Navigation in Congested Environments
AI 导读
研究者提出神经形态决策动力学（NDD），一种去中心化、基于事件的控制器，在拥堵空间中实现安全、无死锁的协调通行。NDD 无需通信、预设优先级或邻居意图模型，智能体通过局部交互自组织成冲突解决小组，并给出感知范围、反馈增益与执行限制的显式条件，保证有限时间内形成通行顺序且无死锁或活锁。大规模仿真与硬件实验验证了其可扩展性。
正文
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)
来源：arXiv cs.MA · arxiv.org