{"references": ["S. Dhillon, K. Chakrabarty, S. Iyengar, \"Sensor Placement for\nGrid Coverage under Imprecise Detections\", in Proc. Intl. Conf. on\nInformation Fusion, pp. 1571-1587, 2002.", "D. Ucinski, \"Measurement Optimization for Parameter Estimation in\nDistributed Systems\", CRC Press, 1999.", "Li, W., Cassandras, C.G., \"Distributed Cooperative coverage Control of\nSensor Networks\", 44th IEEE Conference on Decision and Control and\nEuropean Control Conference (CDC-ECC '05), pp. 2542-2547, 2005.", "J. R. Spletzer and C. J. Taylor, \"Dynamic Sensor Planning and Control\nfor Optimally Tracking Targets\", International Journal of Robotics\nResearch, vol. 22, no. 1, pp. 7-20, 2003.", "T. Nakamoto, H. Ishida, and T. Moriizumi, \"Active Odor Sensing\nSystem\", in Proc. Intl. Symposium on Industrial Electronics, vol. 1,\npp. SS128-SS133, 1997.", "J.R. Frost and L.D. Stone, \"Review of Search Theory: Advances and\nApplications to Search and Rescue Decision Support\", Technical Report\nCG-D-15-01, U.S. Coast Guard Research and Development Center,\nGronton, CT, 2001.", "T. Heng, Y. Kuno, and Y. Shirai, \"Active Sensor Fusion for Collision\nAvoidance\", in Proc of the IEEE/RSJ Int. Conf. on Intelligent Robots\nand Systems, vol. 3, pp. 1244-1249, 1997.", "Rahili, S., Ren, W., \"Game Theory Control Solution for Sensor Coverage\nProblem in Unknown Environment\", 53rd IEEE Conference on Decision\nand Control (CDC), pp.1173-1178, 2014.", "W. Yatao and L. Pavel,\"A Modified Q-Learning Algorithm for Potential\nGames\", Proceedings of the 19th IFAC World Congress, vol.19, pp.\n8710-8718, 2014.\n[10] J. Marden and A. Wierman, \"Distributed Welfare Games with\nApplications to Sensor Coverage\", 47th IEEE Conference on Decision\nand Control, pp. 1708-1713, 2008.\n[11] Pavel, L.,\"Game Theory and Evolutionary Games\", ECE1657,\nUniversity of Toronto, Toronto, 2014.\n[12] H. Bayram and H. Bozma, \"Multi-robot Communication Network\nTopology via Centralized Pairwise Games\", 2013 IEEE International\nConference on Robotics and Automation, 2013.\n[13] Chung, T.H., Gupta, V., Burdick, J.W., Murray, R.M.,\"On a\nDecentralized Active Sensing Strategy using Mobile Sensor Platforms\nin a Network\", 43rd IEEE Conference on Decision and Control (CDC),\npp.1914-1919, Vol. 2, 2004.\n[14] J. Cortes, S. Martinez, T. Karatas, and F. Bullo, \"Coverage Control\nfor Mobile Sensing Networks\", IEEE Transactions on Robotics and\nAutomation, vol. 20, no. 2, pp. 243-255, 2004.\n[15] J. Cortes, S. Martinez and F. Bullo, \"Spatially-distributed Coverage\nOptimization and Control with Limited-range Interactions\", ESAIM:\nControl, Optimisation and Calculus of Variations, vol. 11, no. 4, pp.\n691-719, 2005.\n[16] A. Kwok and S. Martinez, \"Deployment Algorithms for A\nPower-constrained Mobile Sensor Network\", IEEE International\nConference on Robotics and Automation, 2008.\n[17] J. R. Marden and J. S. Shamma, \"Revisiting Log-linear Learning:\nAsynchrony, Completeness and Payoff-based Implementation\", Games\nand Economic Behavior, vol. 75, no. 2, pp. 788-808, 2012.\n[18] A. P. Dempster, N. M. Laird, and D. B. Rubin, \"Maximum Likelihood\nfrom Incomplete Data via The EM Algorithm\", Journal of the Royal\nStatistical Society Series B, vol. 39, no. 1, pp. 1-38, 1977.\n[19] V. Lakshmanan and J. S. Kain, \"A Gaussian Mixture Model Approach\nto Forecast Verification\", Weather and Forecasting, vol. 25, pp. 908-920,\n2010.\n[20] Y. Lim and J. S. Shamma, \"Robustness of Stochastic Stability in Game\nTheoretic Learning\", ACC, pp. 6145-6150, 2013.\n[21] Akaike, H., \"A New Look at The Statistical Model Identification\", IEEE\nTransactions on Automatic Control, vol.19, no.6, pp. 716-723, 1974\n[22] N. Ueda, R. Nakano, Z. Ghahramani and G. Hinton, \"Split and Merge\nEM Algorithm for Improving Gaussian Mixture Density Estimates\", The\nJournal of VLSI Signal Processing, vol. 26, no. 12, pp. 133-140, 2000."]}

In this paper, we study a distributed control algorithm for the problem of unknown area coverage by a network of robots. The coverage objective is to locate a set of targets in the area and to minimize the robots’ energy consumption. The robots have no prior knowledge about the location and also about the number of the targets in the area. One efficient approach that can be used to relax the robots’ lack of knowledge is to incorporate an auxiliary learning algorithm into the control scheme. A learning algorithm actually allows the robots to explore and study the unknown environment and to eventually overcome their lack of knowledge. The control algorithm itself is modeled based on game theory where the network of the robots use their collective information to play a non-cooperative potential game. The algorithm is tested via simulations to verify its performance and adaptability.