Forecast-Aware Cooperative Planning on Temporal Graphs under Stochastic Adversarial Risk

TL;DR

This paper introduces a forecast-aware planning framework for multi-robot teams operating in environments with evolving stochastic risks, enabling proactive support and path planning to reduce mission costs.

Contribution

It presents a novel integration of stochastic risk forecasting with anticipatory support allocation on temporal graphs for cooperative robot planning.

Findings

Reduces total expected team cost compared to non-anticipatory methods

Approaches the performance of an oracle planner in experiments

Effectively models adversary dynamics as a Markov process

Abstract

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.