A Framework for Sustainable Management of Autonomous Ground Robot Fleets

Ensuring low battery degradation in Autonomous Ground Robot (AGR) fleets operating in online environments (e.g., delivery services) is essential for enhancing their long-term sustainability. However, most existing studies either rely on offline methods—unsuitable for scenarios requiring real-time decisions—or focus solely on maximizing task allocation, resource utilization, or revenue, with limited consideration for battery health. Additionally, maximizing fleet sustainability requires bounded relative revenue losses from unassigned tasks within a user-defined acceptable limit to make it an attractive option for industry. To address these limitations, we propose an online task and charge allocation framework that jointly optimizes revenue generation and battery lifespan, while allowing users to explicitly constrain relative revenue losses. The framework includes three event-driven algorithms: BTC-M, which computes optimal decisions at each event, and two computationally efficient greedy variants, BTC-G and BTC-WG, which provide sub-optimal solutions with reduced overhead. We evaluate the performance of our approach under different task arrival distributions representative of real-world applications. Simulation results based on a real AGR, compared against multiple baselines, demonstrate that our framework can extend battery lifespan by up to 19% with minimal revenue loss.