Artificial Intelligence Review

, Volume 37, Issue 3, pp 217–237 | Cite as

Autonomous battery management for mobile robots based on risk and gain assessment

  • Vincent BerenzEmail author
  • Fumihide Tanaka
  • Kenji Suzuki


Battery management of mobile robots is an issue that has not been a strong focus of attention and is usually addressed by the simple use of battery thresholds. One of the main causes is that no significant method of assessment of risk of battery depletion has yet been proposed. As a result decision of redirection to a charging station is fixed and takes into account neither a dynamic evaluation of the risk of battery depletion nor an evaluation of the gain, defined as the level of mission accomplishment that could be achieved. In this paper we propose a novel method for evaluation of risk of battery depletion for mobile robots. Uncertainties concerning effective battery capacity, current discharge rate and energy required for reaching the station are addressed by the use of probability density functions. This risk assessment will allow replacing the usage of battery threshold by a customizable risk-taking parameter that will be used to define what level of gain is required for balancing a given level of risk. This risk/gain management of battery will guarantee that decision of redirection to the station corresponds to a favorable compromise between risk and level of mission accomplishment. While the proposed approach has been tested using a simulated and real room cleaning robot, it could be applied on a wider range of mobile robots.


Battery management Mobile autonomous robots Risk assessment Energy aware operation 


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  1. Alighanbari M (2004) Task assignment algorithms for teams of uavs in dynamic environments. Master’s thesis, Dept. Aeronaut. Astronaut., Massachusetts Institute of Technology, CambridgeGoogle Scholar
  2. Aylor J, Thieme A, Johnso B (1992) A battery state-of-charge indicator for electric wheelchairs. IEEE Trans Ind Electron 39(5): 398–409. doi: 10.1109/41.161471 CrossRefGoogle Scholar
  3. Bethke B, Valenti M, How JP (2010) Experimental demonstration of uav task assignment with integrated health monitoring. IEEE Robotics automation magazine marchGoogle Scholar
  4. Cohen DA, Ozick D, Vu C, Lynch J, Mass PR (2008) Autonomous robot auto-docking and energy management systems and methods. Patent-7332890Google Scholar
  5. Dressler F, Fuchs G (2005) Energy-aware operation and task allocation of autonomous robots. In: Proceedings of 5th IEEE international workshop on robot motion and control (IEEE RoMoCo’05), pp 163–168Google Scholar
  6. Ferri G, Mondini A, Manzi A, Mazzolai B, Laschi C, Mattoli V, Reggente M, Stoyanov T, Lilienthal AJ, Lettere M, Dario P (2010) Dustcart, a mobile robot for urban environments: experiments of pollution monitoring and mapping during autonomous navigation in urban scenarios. In: Proceedings of ICRA workshop on networked and mobile robot olfaction in natural, dynamic environmentsGoogle Scholar
  7. Huet F (1998) A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries. J Power Sourc 70(1): 59–69MathSciNetCrossRefGoogle Scholar
  8. Kikuoka T, Yamamoto H, Sasaki N, Wakui K, Murakami K, Ohnishi K, Kawamura G, Noguchi H, Ukigaya F (1983) System for measuring state of charge of storage battery. Patent-4377787Google Scholar
  9. Lee M, Tarokh M, Cross M (2010) Fuzzy logic decision making for multi-robot security systems. Artif Intell Rev doi: 10.1007/s10462-010-9168-8
  10. Lucas N, Codrea C, Hirth T, Gutierrez J, Dressler F (2005) RoBM2: measurement of battery capacity in mobile robot systems. In: GI/ITG KuVS Fachgespräch Energiebewusste Systeme und Methoden, Erlangen, Germany, pp 13–18Google Scholar
  11. Pei F, Zhao K, Luo Y, Huang X (2006) Battery variable current-discharge resistance characteristics and state of charge estimation of electric vehicle. In: Intelligent control and automation, 2006. WCICA 2006. The Sixth World Congress on, vol 2, pp 8314–8318Google Scholar
  12. Piller S, Perrin M, Jossen A (2001) Methods for state-of-charge determination and their applications. In: Proceedings of the 22nd International power sources symposium, J Power Sourc 96(1):113–120Google Scholar
  13. Pop V, Bergveld H, Notten P, Regtien P (2005) State-of-the-art of battery state-of-charge determination. Meas Sci Technol 16(12): R93–R110CrossRefGoogle Scholar
  14. Reichard KM (2004) Integrating self-health awareness in autonomous systems. Rob Auton Syst 49(1–2): 105–112CrossRefGoogle Scholar
  15. Roberts JF, Zufferey JC, Floreano D (2008) Energy management for indoor hovering robots. In: IEEE/RSJ international conference on intelligent robots and systems (IROS’2008), Nice, FranceGoogle Scholar
  16. Seyfang GR (1990) Battery state of charge indicator. Patent-4949046Google Scholar
  17. Valenti M, Bethke B, How JP, de Farias DP, Vian J (2007) Embedding health management into mission tasking for UAV teams. In: American control conference, 2007. ACC ’07, pp 5777–5783Google Scholar
  18. Verbrugge MW (2002) Quasi-adaptive method for determining a battery’s state of charge. Patent-6359419Google Scholar
  19. Zhang F, Liu G, Fang L (2009) Battery state estimation using unscented kalman filter. In: Robotics and automation, 2009. ICRA ’09. IEEE International Conference on, pp 1863–1868Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Intelligent Interaction Technologies, Graduate School of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan

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