The Persistent Robot Charging Problem for Long-Duration Autonomy

IF 5.3 2区 计算机科学 Q2 ROBOTICS IEEE Robotics and Automation Letters Pub Date : 2025-01-01 DOI:10.1109/LRA.2024.3524897
Nitesh Kumar;Jaekyung Jackie Lee;Sivakumar Rathinam;Swaroop Darbha;P. B. Sujit;Rajiv Raman
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Abstract

This paper introduces a novel formulation for finding the recharging schedule for a fleet of $n$ heterogeneous robots that minimizes utilization of recharging resources. 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 minimal utilization of charging stations. This formulation was further generalized to maximize the servicing time for robots when charging stations are limited. 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 corroborate 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.
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机器人长时间自治的持续充电问题
本文提出了一种求解$n$异构机器人车队充电计划的新公式,使充电资源的利用率达到最小。该研究提供了一个适用于多机器人任务规划的基础框架,特别是在需要长时间自治(LDA)的场景或其他需要多个机器人定期充电的环境中。提出了一种新的整数线性规划(ILP)模型,用于计算单个机器人的最优初始条件(部分充电),从而使充电站的利用率最小。将该公式进一步推广到充电站有限情况下机器人服务时间的最大化。通过比较分析来评估所提出的公式的有效性,将其性能与现有文献中记录的节俭价格调度算法进行比较。研究结果不仅证实了所提出方法的有效性,而且强调了其作为优化资源分配的有价值工具的潜力,用于一系列机器人和工程应用。
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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