[2509.08157] Risk-Bounded Multi-Agent Visual Navigation via Iterative Risk Allocation

Computer Science > Robotics arXiv:2509.08157 (cs) [Submitted on 9 Sep 2025 (v1), last revised 20 Mar 2026 (this version, v3)] Title:Risk-Bounded Multi-Agent Visual Navigation via Iterative Risk Allocation Authors:Viraj Parimi, Brian C. Williams View a PDF of the paper titled Risk-Bounded Multi-Agent Visual Navigation via Iterative Risk Allocation, by Viraj Parimi and Brian C. Williams View PDF HTML (experimental) Abstract:Safe navigation is essential for autonomous systems operating in hazardous environments, especially when multiple agents must coordinate using only high-dimensional visual observations. While recent approaches successfully combine Goal-Conditioned RL (GCRL) for graph construction with Conflict-Based Search (CBS) for planning, they typically rely on deleting edges with high risk before running CBS to enforce safety. This binary strategy is overly conservative, precluding feasible missions that require traversing high-risk regions, even when the aggregate risk is acceptable. To address this, we introduce a framework for Risk-Bounded Multi-Agent Path Finding ($\Delta$-MAPF), where agents share a user-specified global risk budget ($\Delta$). Rather than permanently discarding edges, our framework dynamically distributes per-agent risk budgets ($\delta_i$) during search via an Iterative Risk Allocation (IRA) layer that integrates with a standard CBS planner. We investigate two distribution strategies: a greedy surplus-deficit scheme for rapid feasibility repai...