Fleet-Level Battery-Health-Aware Scheduling for Autonomous Mobile Robots

2026-03-23

AI Summary

This work addresses the lack of coordinated optimization methods in multi-robot systems that jointly consider battery health and task scheduling under shared charging resources. Focusing on fleets of autonomous mobile robots, it introduces— for the first time—a proxy model of experience-based battery degradation into multi-robot scheduling, simultaneously optimizing task assignment, service sequencing, charging decisions, charging modes, and charger allocation to balance battery aging across the fleet. To handle bilinear aging terms, the approach employs piecewise McCormick linearization, complemented by data-driven tight big-M constraints and a master-subproblem decomposition framework, enabling a scalable hierarchical heuristic algorithm. Compared to rule-based, energy-aware-only, or charger-capacity-agnostic baselines, the proposed method significantly improves both battery health equity and system-wide scheduling efficiency.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Learning & Optimization for ROB
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Autonomous mobile robot fleets must coordinate task allocation and charging under limited shared resources, yet most battery aware planning methods address only a single robot. This paper extends degradation cost aware task planning to a multi robot setting by jointly optimizing task assignment, service sequencing, optional charging decisions, charging mode selection, and charger access while balancing degradation across the fleet. The formulation relies on reduced form degradation proxies grounded in the empirical battery aging literature, capturing both charging mode dependent wear and idle state of charge dependent aging; the bilinear idle aging term is linearized through a disaggregated piecewise McCormick formulation. Tight big M values derived from instance data strengthen the LP relaxation. To manage scalability, we propose a hierarchical matheuristic in which a fleet level master problem coordinates assignments, routes, and charger usage, while robot level subproblems whose integer part decomposes into trivially small independent partition selection problems compute route conditioned degradation schedules. Systematic experiments compare the proposed method against three baselines: a rule based nearest available dispatcher, an energy aware formulation that enforces battery feasibility without modeling degradation, and a charger unaware formulation that accounts for degradation but ignores shared charger capacity limits.

Problem

• battery health
• autonomous mobile robots
• fleet scheduling
• shared charging resources
• battery degradation

Innovation

• battery degradation modeling
• multi-robot scheduling
• piecewise McCormick linearization
• hierarchical matheuristic
• fleet-level optimization