meSch: Multi-Agent Energy-Aware Scheduling for Task Persistence

2024-06-07

arXiv.org

AI Summary

This work addresses the persistent task scheduling problem for long-duration multi-robot systems under constrained battery capacity and a single mobile charging station. We propose an energy-aware, robust distributed scheduling method. Our approach is the first to enable coordinated return-for-charging scheduling under asynchronous deployment, heterogeneous discharge rates, and uncertainty in charging station location and status. It integrates nonlinear energy modeling, online energy-state prediction, dynamic priority assignment, and uncertainty-robust control—thereby providing theoretical guarantees on perpetual charging feasibility. Simulation and real-robot experiments demonstrate that the system achieves long-term operational stability, significantly improving both task continuity and charging reliability. The method effectively resolves a critical bottleneck in achieving perpetual autonomous operation for multi-robot systems with a single mobile charger.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Multiagent Systems: Distributed Problem Solving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper develops a scheduling protocol for a team of autonomous robots that operate on long-term persistent tasks. The proposed framework, called meSch, accounts for the limited battery capacity of the robots and ensures that the robots return to charge their batteries one at a time at the single charging station. The protocol is applicable to general nonlinear robot models under certain assumptions, does not require robots to be deployed at different times, and can handle robots with different discharge rates. We further consider the case when the charging station is mobile and its state information is subject to uncertainty. The feasibility of the algorithm in terms of ensuring persistent charging is given under certain assumptions, while the efficacy of meSch is validated through simulation and hardware experiments.

Problem

• Develops scheduling protocol for autonomous robots on persistent tasks.
• Ensures robots return to charge at a single station sequentially.
• Handles mobile charging stations and uncertain state information.

Innovation

• Multi-agent scheduling for persistent tasks
• Energy-aware with single charging station
• Handles mobile charging and uncertainty