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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: Balajee Kannan, 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, Waverla et 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-aware schedule
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 Schedule with mobile recharging
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 Task Recharge Task Schedule with mobile recharging

8 Evaluation: Distance Ran all strategies on a schedule of 50 tasks
Strategy Distance Gains Charge-Aware Mobile Recharger Infinite battery -1.37 (-0.08%) -0.96 (-0.56%) Battery threshold 15.89 (9.29%) 16.30 (9.55%) Distance threshold 15.76 (9.21%) 16.17 (9.48%) Charge-aware - 0.41 (0.24%) Mobile recharger -0.41 (-0.24%

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 Time Gains Charge-Aware Mobile Recharger Strategy Method #1 Method #2 Infinite battery (-5.24%) (-65.73%) (-3.74%) (-62.43%) Battery threshold 52.00 (5.13%) (4.77%) 67.79 (6.80%) (14.49%) Distance threshold 102 (10.07%) 9.50 (0.34%) (11.81%) (9.99%) Charge-aware - 15.79 (1.59%) (9.62%) Mobile recharger (-1.56%) (-8.77%)

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 Balajee Kannan, Ph.D.
Jimmy Bourne, Sairam Yamanoor, 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 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 , 2009). IEEE Press, Piscataway, NJ, 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 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 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 Yaroslav Litus, Pawel Zebrowski, and Richard T. Vaughan. A distributed heuristic for energy-efficient multi-robot multi-place rendezvous. IEEE Transactions on Robotics, 25(1): , 2009. Munoz A., Sempe F., and Drogoul A. (2002). Sharing a Charging Station in Collective Robotics. Sempé F., 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, Birk A. (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, July, 2007

15 Questions? Pioneer P3DX and LAGR robots

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