Execution Monitoring and Local Coordination for Multi-Agent Fleet Management

Abstract

Robot fleet-management systems combine high-level scheduling with local motion control to coordinate multiple robots operating in shared environments. However, during execution robots can deviate from a pre-computed schedule, resulting in delays or conflicts. This thesis extends an existing scheduling–control framework by introducing an execution-monitoring local coordinator and implementing the framework on a physical multi-robot platform. The coordinator monitors robot states and predicted trajectories and handles identified conflicts through waiting, priority changes, and re-planning. The proposed method is evaluated in simulation under execution-time disturbances. The distributed Model Predictive Control (MPC) framework and coordinator were also implemented on physical robots (Duckiebots). Hardware experiments showed mean tracking error values between 0.039 m and 0.076 m, while schedule-adherence experiments revealed accumulated delays caused by differences between idealized scheduling assumptions and physical robot behavior. In simulation, the coordinator resolved interactions that otherwise caused large delays or incomplete execution. The results demonstrate that execution monitoring and online coordination can complement high-level scheduling and distributed MPC, while highlighting the trade-off between conservative offline scheduling and dynamic conflict resolution during execution.