Planning, Execution, and Adaptation for Multi-Robot Systems using Probabilistic and Temporal Planning

Abstract

**Read paper on the following link:** https://ifaamas.org/Proceedings/aamas2022/pdfs/p217.pdf **Abstract:** Planning for multi-robot coordination during long-horizon missions in complex environments requires considering robot resources, temporal constraints, and probabilistic uncertainty. This could be computationally expensive and impractical for online planning and execution. We propose a decoupled framework for temporal and probabilistic planning. At the high-level, we plan for multi-robot missions that require coordination amongst robots considering temporal and numeric constraints. The temporal plan is decomposed into low-level plans for individual robots. At the low-level, we perform online learning and adaptation due to unexpected probabilistic outcomes to achieve mission goals. Our framework learns over time from goal execution failures to improve the performance by (1) updating the learned domain model to reduce model prediction errors and (2) constraining the robot's capabilities which in turn improves goal allocation. The approach provides a solution to planning problems by pondering mission execution quality over implementation time. We demonstrate our approach to experiments involving a fleet of heterogeneous robots.