Risk-Bounded Multi-Agent Visual Navigation via Iterative Risk Allocation
The framework introduces Risk-Bounded Multi-Agent Path Finding (∆-MAPF) to help autonomous agents navigate hazardous environments using visual data. It dynamically shares a global risk budget among agents, allowing them to trade safety for speed. The system uses learned maps and two strategies (EQUIRIS or WALRIS) to adjust individual risk limits in real-time, enabling a tunable balance between mission safety and travel time efficiency.