Autonomic Management for Mobile Robot Battery Degradation
The majority of today’s mobile robots are very dependent on battery power. Mobile robots can operate untethered for a number of hours but eventually they will need to recharge their batteries in-order to continue to function. While computer processing and sensors have become cheaper and more powerful each year, battery development has progress very little. They are slow to re-charge, inefficient and lagging behind in the general progression of robotic development we see today. However, batteries are relatively cheap and when fully charged, can supply high power output necessary for operating heavy mobile robots. As there are no cheap alternatives to batteries, we need to find efficient ways to manage the power that batteries provide during their operational lifetime. This paper proposes the use of autonomic principles of self-adaption to address the behavioral changes a battery experiences as it gets older. In life, as we get older, we cannot perform tasks in the same way as we did in our youth; these tasks generally take longer to perform and require more of our energy to complete. Batteries also suffer from a form of degradation. As a battery gets older, it loses the ability to retain the same charge capacity it would have when brand new. This paper investigates how we can adapt the current state of a battery charge and cycle count, to the requirements of a mobile robot to perform its tasks.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316440Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 702
 D. M. Chess, A. Segal, I. Whalley, and S. R. White, “An architectural blueprint for autonomic computing,” IBM Corporation, 2004.
 S. Jha, M. Parashar, O. Rana, "Self-Adaptive Architectures for Autonomic Computational Science" in Self-Organizing Architectures, Springer, pp. 177-197, 2010.
 PowerThruu: Lead Acid Battery working – lifetime study. (Online). Available from: http://www.power-thru.com (Accessed 17 May 2017)
 D. C. C. Freitas, M. B. Ketzer, M. R. A. Morais and A. M. N. Lima, "Lifetime estimation technique for lead-acid batteries," IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, 2016, pp. 2076-2081.
 Microsoft. Microsoft Robotics Developer Studio. (Online). Available from: http://www.microsoft.com/robotics/2017/02/04.
 Yuasa Batteries – NP serires NP7.5-12 data sheet. (Online). Available from: http://www.yuasabatteries.com (Accessed 06 May 2017).
 A. Hernando, R. Sanz, R. Calinescu, "A Model-Based Approach to the Autonomic Management of Mobile Robot Resources", in proc. Int. Conf. on Adaptive and Self-Adaptive Systems and Applications, Lisbon, Portugal, 2010.
 P. Arcaini, E. Riccobene and P. Scandurra, "Modeling and Analyzing MAPE-K Feedback Loops for Self-Adaptation," 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, Florence, 2015, pp. 13-23.
 Progreesive Dynamcis Inc. (Online). Available from: http://www.progressivedyn.com/battery_basics.html/2017/05/20
 Stem Education (Microsoft Developer Studio). (Online). Available from: http://www.helloapps.com/index.html/ (Accessed 23 May 2017)
 Adept Mobile Robots. Pioneer 3 Operations Manual, Version 6, 2010.
 J. O. Kephart and D. M. Chess, “The vision of autonomic computing,” Computer, vol. 36, no. 1, pp. 41–50, 2003.
 A. G. Ganek and T. A. Corbi, “The dawning of the autonomic computing era,” IBM Syst. J., vol. 42, no. 1, pp. 5–18, 2003.
 American Scientific: Could Battery Advances Mean Better Robots?. (Online). Available from: https://www.scientificamerican.com/article/robot-battery-technology-life-spa/ (Accessed 2 June 2017).
 Lucas, Nestor, Cosmin Codrea, Thomas Hirth, Javier Gutierrez and Falko Dressler. “RoBM2: Measurement of Battery Capacity in Mobile Robot Systems.” (2005).
 Sweet, A., Gorospe, G., Daigle, M., Celaya, J. R., Bal-aban, E., Roychoudhury, I., and Narasimhan, S., “Demonstration of prognostics-enabled decision making algorithms on a hardware mobile robot test platform”. In Annual Conf. of the Prognostics and Health Management Society, pp. 142–150, 2014.
 Deshmukh, A., Vargas, P. A., Aylett, R., Brown, K.: Towards Socially Constrained Power Management for Long-Term Operation of Mobile Robots. In Towards Autono-mous Robotic Systems, (2010).
 Jae-O Kim and Chanwoo Moon, “A Vision-Based Wireless Charging System for Robot Trophallaxis”, International Journal of Advanced Robotic Systems, Vol 12, Issue 12, 2015.
 Y. Mei, Y. Lu, Y. C. Hu and C. S. G. Lee, “A case study of mobile robot's energy consumption and conservation techniques”, ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005., Seattle, WA, 2005, pp. 492-49.