Forecast-Aware Cooperative Planning on Temporal Graphs under Stochastic Adversarial Risk
This addresses the challenge of dynamic risk environments for multi-robot teams, offering an incremental improvement over existing static-risk methods.
The paper tackles the problem of cooperative multi-robot planning in environments with stochastic adversarial risks that evolve over time, proposing a forecast-aware framework that integrates risk forecasting with anticipatory support allocation, resulting in consistently reduced total expected team cost compared to non-anticipatory baselines and approaching oracle planner performance.
Cooperative multi-robot missions often require teams of robots to traverse environments where traversal risk evolves due to adversary patrols or shifting hazards with stochastic dynamics. While support coordination - where robots assist teammates in traversing risky regions - can significantly reduce mission costs, its effectiveness depends on the team's ability to anticipate future risk. Existing support-based frameworks assume static risk landscapes and therefore fail to account for predictable temporal trends in risk evolution. We propose a forecast-aware cooperative planning framework that integrates stochastic risk forecasting with anticipatory support allocation on temporal graphs. By modeling adversary dynamics as a first-order Markov stay-move process over graph edges, we propagate the resulting edge-occupancy probabilities forward in time to generate time-indexed edge-risk forecasts. These forecasts guide the proactive allocation of support positions to forecasted risky edges for effective support coordination, while also informing joint robot path planning. Experimental results demonstrate that our approach consistently reduces total expected team cost compared to non-anticipatory baselines, approaching the performance of an oracle planner.