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

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.

Figures (5)

• Figure 1: Direct and charging transitions with shared-charger non-overlap coupling.
• Figure 2: Hierarchical matheuristic workflow.
• Figure 3: Degradation--tardiness Pareto front as $\mu$ varies.
• Figure 4: Warehouse trajectories (3 robots, 30 tasks, 2 chargers).
• Figure 5: SOC traces; upward jumps indicate charging events.