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Forecasting battery state of charge for robot missions

Authors:

Nora AyanianAuthors Info & Claims

SAC '17: Proceedings of the Symposium on Applied Computing

Pages 249 - 255

https://doi.org/10.1145/3019612.3019705

Published: 03 April 2017 Publication History

Abstract

Due to limited power onboard, a significant factor for success of distributed teams of robots is energy-awareness. The ability to predict when power will be depleted beyond a certain point is necessary for recharging or returning to a base station. This paper presents a framework for forecasting state of charge (SOC) of a robot's battery for a given mission. A generalized and customizable mission description is formulated as a sequence of parametrized tasks defined for the robot; the missions are then mapped to expected change in SOC by training neural networks on experimental data. We present results from experiments on the Turtlebot 2 to establish the efficacy of this framework. The performance of the proposed framework is demonstrated for three distinct mission representations and compared to an existing method in the literature. Finally, we discuss the strengths and weaknesses of feedforward and recurrent neural network models in the context of this work.

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Cited By

Peng FLiu HZheng L(2023)A Sarsa reinforcement learning hybrid ensemble method for robotic battery power forecasting一种用于机器人电池电量预测的 Sarsa 强化学习混合集成方法Journal of Central South University10.1007/s11771-023-5451-030:11(3867-3880)Online publication date: 21-Dec-2023
https://doi.org/10.1007/s11771-023-5451-0
Thurow KChen CJunginger SStoll NLiu H(2020)Transportation robot battery power forecasting based on bidirectional deep-learning methodTransportation Safety and Environment10.1093/tse/tdz0161:3(205-211)Online publication date: 22-Feb-2020
https://doi.org/10.1093/tse/tdz016
Thompson FGaleazzi RGuihen D(2020)Field trials of an energy‐aware mission planner implemented on an autonomous surface vehicleJournal of Field Robotics10.1002/rob.2194237:6(1040-1062)Online publication date: 11-Feb-2020
https://doi.org/10.1002/rob.21942
Show More Cited By

Index Terms

Forecasting battery state of charge for robot missions
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
    2. Sensor networks
2. Hardware
  1. Power and energy
    1. Energy generation and storage
      1. Batteries
    2. Power estimation and optimization

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Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '17: Proceedings of the Symposium on Applied Computing

April 2017

2004 pages

ISBN:9781450344869

DOI:10.1145/3019612

Conference Chair:
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Maria Lencastre
University of Pernambuco, Brazil

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 April 2017

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Office of Naval Research

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SAC 2017

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SIGAPP

SAC 2017: Symposium on Applied Computing

April 3 - 7, 2017

Marrakech, Morocco

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Peng FLiu HZheng L(2023)A Sarsa reinforcement learning hybrid ensemble method for robotic battery power forecasting一种用于机器人电池电量预测的 Sarsa 强化学习混合集成方法Journal of Central South University10.1007/s11771-023-5451-030:11(3867-3880)Online publication date: 21-Dec-2023
https://doi.org/10.1007/s11771-023-5451-0
Thurow KChen CJunginger SStoll NLiu H(2020)Transportation robot battery power forecasting based on bidirectional deep-learning methodTransportation Safety and Environment10.1093/tse/tdz0161:3(205-211)Online publication date: 22-Feb-2020
https://doi.org/10.1093/tse/tdz016
Thompson FGaleazzi RGuihen D(2020)Field trials of an energy‐aware mission planner implemented on an autonomous surface vehicleJournal of Field Robotics10.1002/rob.2194237:6(1040-1062)Online publication date: 11-Feb-2020
https://doi.org/10.1002/rob.21942
Ho DBen Chehida KMiramond BAuguin M(2019)Learning-Based Adaptive Management of QoS and Energy for Mobile Robotic MissionsInternational Journal of Semantic Computing10.1142/S1793351X1940022113:04(513-539)Online publication date: 26-Dec-2019
https://doi.org/10.1142/S1793351X19400221
Tiwari KXiao XChong N(2018)Estimating Achievable Range of Ground Robots Operating on Single Battery Discharge for Operational Efficacy Amelioration2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2018.8593845(3991-3998)Online publication date: Oct-2018
https://doi.org/10.1109/IROS.2018.8593845

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