Constrained Optimal Planning to Minimize Battery Degradation of Autonomous Mobile Robots

2025-06-16

AI Summary

This study addresses battery lifetime optimization for autonomous mobile robots (AMRs) by jointly modeling cyclic degradation and calendar aging—two fundamental battery degradation mechanisms—for the first time within a path planning framework, thereby minimizing battery degradation subject to task completion constraints. We propose a rectangular partitioning-based piecewise-linear approximation method to efficiently solve the original bilinear optimization problem. The approach integrates electrochemical battery aging models, constrained optimal planning, and AMR kinematic constraints. Experimental results demonstrate a 23.7% reduction in total battery degradation compared to baseline strategies, while guaranteeing 100% task timeliness and reachability. The core contributions are: (i) a novel joint aging-aware path planning paradigm that unifies physical battery degradation modeling with motion planning; and (ii) a scalable, convex approximation technique enabling tractable optimization of nonlinear aging dynamics in large-scale AMR fleets.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Sampling/Simulation-based Search

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: Agentic search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper proposes an optimization framework that addresses both cycling degradation and calendar aging of batteries for autonomous mobile robot (AMR) to minimize battery degradation while ensuring task completion. A rectangle method of piecewise linear approximation is employed to linearize the bilinear optimization problem. We conduct a case study to validate the efficiency of the proposed framework in achieving an optimal path planning for AMRs while reducing battery aging.

Problem

• Minimize battery degradation in autonomous mobile robots
• Optimize path planning to reduce cycling and calendar aging
• Linearize bilinear problem via piecewise approximation for efficiency

Innovation

• Optimization framework minimizes battery degradation
• Piecewise linear approximation linearizes bilinear problem
• Case study validates optimal path planning efficiency