The Persistent Robot Charging Problem for Long-Duration Autonomy

TL;DR

This paper presents a new ILP-based approach for optimizing robot recharging schedules to minimize resource use and maximize service time in long-duration autonomous missions.

Contribution

It introduces a novel ILP model for scheduling robot recharges, optimizing initial conditions and resource utilization in multi-robot systems.

Findings

The ILP model effectively reduces charging station utilization.

The approach outperforms existing thrift price scheduling algorithms.

Results demonstrate improved resource allocation in long-duration autonomy scenarios.

Abstract

This paper introduces a novel formulation aimed at determining the optimal schedule for recharging a fleet of $n$ heterogeneous robots, with the primary objective of minimizing resource utilization. This study provides a foundational framework applicable to Multi-Robot Mission Planning, particularly in scenarios demanding Long-Duration Autonomy (LDA) or other contexts that necessitate periodic recharging of multiple robots. A novel Integer Linear Programming (ILP) model is proposed to calculate the optimal initial conditions (partial charge) for individual robots, leading to the minimal utilization of charging stations. This formulation was further generalized to maximize the servicing time for robots given adequate charging stations. The efficacy of the proposed formulation is evaluated through a comparative analysis, measuring its performance against the thrift price scheduling algorithm documented in the existing literature. The findings not only validate the effectiveness of the proposed approach but also underscore its potential as a valuable tool in optimizing resource allocation for a range of robotic and engineering applications.