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Market-Based Coordination of Recharging Robots

Market-Based Coordination of Recharging Robots. Victor Marmol School of Computer Science Senior Thesis. Advisor: M. Bernardine Dias, Ph.D. Robotics Institute. Mentor: Balajee Kannan , Ph.D. Robotics Institute. Autonomous Recharging.

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Market-Based Coordination of Recharging Robots

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  1. Market-Based Coordination of Recharging Robots Victor Marmol School of Computer Science Senior Thesis Advisor: M. Bernardine Dias, Ph.D. Robotics Institute Mentor: BalajeeKannan, Ph.D. Robotics Institute

  2. Autonomous Recharging • Necessary for any group of mobile robots that are to be effective beyond a short amount of time • Robots can run for weeks, months, years • Mobile and static recharging units allow a group of worker robots to recharge when necessary Mobile recharger docking with a worker. Real system (left) CAD model (right)

  3. Related Work • Most existing systems don’t implement recharging • Most existing approaches are threshold-based and make decisions utilizing only the current state • Battery voltage threshold [2, Silverman et al. 2002][8, Silverman et al. 2003][12, Munoz et al. 2002][13, Munoz et al. 2002] • Time threshold [5, Austin et al. 2001] • Distance threshold [4, Waverla at al. 2008][7, Waverlaet al. 2007] • Most current systems aren’t charge-aware • No existing strategy for coordinating multiple worker robots and a recharging unit

  4. Our Approach • Design and develop a market-based distributed system for planning and coordination • Give each robot charge-awareness • Enhance system to include mobile rechargers Mobile recharging agent’s docking arm GUI integrating map and robot control

  5. Market-Based Systems • Uses a simulated economy to trade tasks between robots based on their costs • Cost is defined by a set of cost functions • Advantages: • Distributed • Fault tolerant • Scalable Task An auction for a task with two bidding robots. Arrows are bids, green arrows are winning bids. Cost metric is distance.

  6. Charge-Awareness • Robots estimate their remaining operational time • Workers bid on work tasks to insert into their schedules • Recharging tasks inserted to create balanced schedules • Schedules are optimized to minimize distance traveled • Workers assume no mobile rechargers for initial estimate Task Task Task Task Charge-Aware Home Task Task Existing schedule Charge-aware schedule

  7. Mobile Rechargers • Goal: maximize work done by worker robots • Workers auction off recharging tasks • Mobile rechargers bid on recharging tasks with rendezvous points along the worker’s path Task Recharge Task Task Schedule with mobile recharging

  8. Evaluation: Distance • Ran all strategies on a schedule of 50 tasks

  9. Evaluation: Time • Ran all strategies on a schedule of 50 tasks • Two methods for calculating recharging time • Method 1: Constant time to recharge • Method 2: Proportional to amount of charge required

  10. Evaluation: Scalability (Distance) • Ran all strategies on schedules of increasing size Our strategies consistently outperform current approaches

  11. Conclusion & Future work • Our strategies represent an advancement in the state of the art for autonomous recharging • Planning and coordination in autonomous recharging greatly enhances the amount of work performed by mobile robots • Future Work • Extend to larger teams • More workers • More mobile rechargers • Make mobile rechargers charge-aware

  12. Acknowledgements • M. Bernardine Dias, Ph.D. and BalajeeKannan, Ph.D. • Jimmy Bourne, SairamYamanoor, M. Freddie Dias, and Nisarg Kothari • Everyone in the rCommerce group Part of the rCommerce group

  13. References • Seungjun Oh, A. Z. & K. Taylor (2000). Autonomous battery recharging for indoor mobile robots, in the proceedings of Australian Conference on Robotics and Automation (ACRA2000). • Silverman, M.C ; Nies, D ; Jung, B & Sukhatme, G.S (2002). Staying alive: A docking station for autonomous robot recharging, in IEEE Intl. Conf. on Robotics and Automation, 2002 • Kottas, A., Drenner, A., and Papanikolopoulos, N. 2009. Intelligent power management: promoting power-consciousness in teams of mobile robots. In Proceedings of the 2009 IEEE international Conference on Robotics and Automation (Kobe, Japan, May 12 - 17, 2009). IEEE Press, Piscataway, NJ, 2459-2464. • J. Wawerla and R. T. Vaughan. Optimal robot recharging strategies for time discounted labour. In Proc. of the 11th Int. Conf. on the Simulation and Synthesis of Living Systems, 2008. • D. J. Austin, L. Fletcher, and A. Zelinsky, .Mobile robotics in the long term,. in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2001. • Litus, Y., Vaughan, R. T., and Zebrowski, P. (2007). The frugal feeding problem: Energy-efficient, multi-robot, multi-place rendezvous. In Proceedings of the IEEE International Conference on Robotics and Automation. • Wawerla, J. and Vaughan, R. T. (2007). Near-optimal mobile robot recharging with the rate-maximizing forager. In Proceedings of the European Conference on Artificial Life. • M. Silverman, B. Jung, D. Nies, G. Sukhatme. “Staying Alive Longer: Autonomous Robot Recharging Put to the Test.” Center for Robotics and Embedded Systems (CRES) Technical Report CRES-03-015. University of Southern California, 2003. • Alex Couture-Beil and Richard T. Vaughan. Adaptive mobile charging stations for multi-robot systems. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS'09). • St. Loius, MO, October 2009.Zebrowski, P ; Vaughan, R (2005). Recharging Robot Teams: A Tanker Approach, International Conference on Advanced Robotics (ICAR'05), Seattle, Washington, July 18th-20th, 2005.

  14. References • YaroslavLitus, PawelZebrowski, and Richard T. Vaughan. A distributed heuristic for energy-efficient multi-robot multi-place rendezvous. IEEE Transactions on Robotics, 25(1):130-135, 2009. • Munoz A., Sempe F., and Drogoul A. (2002). Sharing a Charging Station in Collective Robotics. • SempéF., Muñoz A., Drogoul A. “Autonomous Robots Sharing a Charging Station with no Communication: a Case Study.” Proceedings of the 6th International Symposium on Distributed Autonomous Robotic Systems (DARS'02). June 2002. • M. B. Dias, “Traderbots: A new paradigm for robust and efficient multirobot coordination in dynamic environments,” Ph.D. dissertation, Robotics Institute, Carnegie Mellon University, January 2004. • TraderBots User’s Guide. Carnegie Mellon University, National Robotics Engineering Center. August 1, 2008. • Flinn, J., Satyanarayanan, M. Energy-aware Adaptation for Mobile Applications. In Proceedings of the 17th ACM Symposium on Operating Systems and Principles. Kiawah Island, SC, December, 1999. • McFarland, D. & Spier, E. (1997). Basic cycles, utility and opportunism in self-sufficient robots. Robotics and Autonomous Systems, 20, 179-90. • BirkA. (1997) Autonomous Recharging of Mobile Robots. In: Proceedings of the 30th International Sysposium on Automative Technology and Automation. Isata Press • Ngo, T. D., Raposo, H., Schioler, H., Being Sociable: Multirobots with Self-sustained Energy, Proceedings of the 15th IEEE Mediterranean Conference on Control and Automation, Athens, Greece, 27-29 July, 2007

  15. Questions? Pioneer P3DX and LAGR robots

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