Abstract
This paper addresses the problem of Multi-robot Coverage Path Planning for unknown environments in the presence of robot failures. Unexpected robot failures can seriously degrade the performance of a robot team and in extreme cases jeopardize the overall operation. Therefore, this paper presents a distributed algorithm, called Cooperative Autonomy for Resilience and Efficiency, which not only provides resilience to the robot team against failures of individual robots, but also improves the overall efficiency of operation via event-driven replanning. The algorithm uses distributed Discrete Event Supervisors, which trigger games between a set of feasible players in the event of a robot failure or idling, to make collaborative decisions for task reallocations. The game-theoretic structure is built using Potential Games, where the utility of each player is aligned with a shared objective function for all players. The algorithm has been validated in various complex scenarios on a high-fidelity robotic simulator, and the results demonstrate that the team achieves complete coverage under failures, reduced coverage time, and faster target discovery as compared to three alternative methods.
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References
Acar, E., & Choset, H. (2002). Sensor-based coverage of unknown environments: Incremental construction of Morse decompositions. International Journal of Robotics Research, 21(4), 345–366.
Agmon, N., Hazon, N., Kaminka, G., & Group, M. (2008). The giving tree: Constructing trees for efficient offline and online multi-robot coverage. Annals of Mathematics and Artificial Intelligence, 52(2–4), 143–168.
Arslan, G., Marden, J., & Shamma, J. (2007). Autonomous vehicle-target assignment: A game-theoretical formulation. Journal of Dynamic Systems, Measurement, and Control, 129(5), 584–596.
Batalin, M. A., & Sukhatme, G. S. (2002). Spreading out: A local approach to multi-robot coverage. In H. Asama, T. Arai, T. Fukuda, & T. Hasegawa (Eds.), Distributed autonomous robotic systems 5 (pp. 373–382). Tokyo: Springer.
Bhattacharya, S., Ghrist, R., & Kumar, V. (2014). Multi-robot coverage and exploration on Riemannian manifolds with boundaries. The International Journal of Robotics Research, 33(1), 113–137.
Bircher, A., Kamel, M., Alexis, K., Burri, M., Oettershagen, P., Omari, S., et al. (2016). Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots. Autonomous Robots, 40(6), 1059–1078.
Broderick, J., Tilbury, D., & Atkins, E. (2014). Optimal coverage trajectories for a UGV with tradeoffs for energy and time. Autonomous Robots, 36(3), 257–271.
Carlson, J., & Murphy, R. R. (2005). How UGVs physically fail in the field. IEEE Transactions on Robotics, 21(3), 423–437.
Cassandras, C . G., & Lafortune, S. (2009). Introduction to discrete event systems. Berlin: Springer.
Chen, W., Toueg, S., & Aguilera, M. (2002). On the quality of service of failure detectors. IEEE Transactions on Computers, 51(5), 561–580.
Choset, H. (2001). Coverage for robotics—a survey of recent results. Annals of Mathematics and Artificial Intelligence, 31, 113–126.
Cloth, L., Jongerden, M., & Haverkort, B. (2007). Computing battery lifetime distributions. In 37th Annual IEEE/IFIP international conference on dependable systems and networks, Edinburgh (pp. 780–789).
Dai, H., Huang, Y., & Yang, L. (2015). Game theoretic max-logit learning approaches for joint base station selection and resource allocation in heterogeneous networks. IEEE Journal on Selected Areas in Communications, 33(6), 1068–1081.
Fernández, J. L., Watkins, C., Losada, D. P., & Medina, M. D. (2013). Evaluating different landmark positioning systems within the ride architecture. Journal of Physical Agents, 7(1), 3–11.
Ferranti, E., Trigoni, N., & Levene, M. (2007). Brick & mortar: An on-line multi-agent exploration algorithm. In IEEE international conference on robotics and automation (pp. 761–767).
Gabriely, Y., & Rimon, E. (2001). Spanning-tree based coverage of continous areas by a mobile robot. Annals of Mathematics and Artificial Intelligence, 31, 77–98.
Galceran, E., & Carreras, M. (2013). A survey on coverage path planning for robotics. Robotics and Autonomous Systems, 61(12), 1258–1276.
Garcia, E., & de Santos, P. G. (2004). Mobile-robot navigation with complete coverage of unstructured environments. Robotics and Autonomous Systems, 46, 195–204.
Gerkey, B., Vaughan, R., & Howard, A. (2003). The player/stage project: Tools for multi-robot and distributed sensor systems. In Proceedings of the 11th international conference on advanced robotics (Vol. 1, pp. 317–323).
Gupta, S., Ray, A., & Phoha, S. (2009). Generalized Ising model for dynamic adaptation in autonomous systems. Europhysics Letters, 87(1), 10009.
Hameed, I. (2014). Intelligent coverage path planning for agricultural robots and autonomous machines on three-dimensional terrain. Journal of Intelligent & Robotic Systems, 74(3–4), 965–983.
Hare, J., Gupta, S., & Wettergren, T. (2018). POSE: Prediction-based opportunistic sensing for energy efficiency in sensor networks using distributed supervisors. IEEE Transactions on Cybernetics, 48(7), 2114–2127.
Hazon, N., & Kaminka, G. (2008). On redundancy, efficiency, and robustness in coverage for multiple robots. Robotics and Autonomous Systems, 56(12), 1102–1114.
Islam, A., Alim, A., Hyder, C., & Zubaer, K. (2015). Digging the innate reliability of wireless networked systems. In IEEE international conference on networking systems and security, Bangladesh (pp. 1–10).
Jongerden, M., & Haverkort, B. (2009). Which battery model to use? IET Software, 3(6), 445–457.
Karapetyan, N., Benson, K., McKinney, C., Taslakian, P., & Rekleitis, I. (2017). Efficient multi-robot coverage of a known environment. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 1846–1852).
Karapetyan, N., Moulton, J., Lewis, J. S., Li, A. Q., O’Kane, J. M., & Rekleitis, I. (2018). Multi-robot dubins coverage with autonomous surface vehicles. In Proceedings of the IEEE international conference on robotics and automation (pp. 2373–2379).
Kim, A., & Eustice, R. (2015). Active visual slam for robotic area coverage: Theory and experiment. The International Journal of Robotics Research, 34(4–5), 457–475.
Koos, S., Cully, A., & Mouret, J. (2013). Fast damage recovery in robotics with the t-resilience algorithm. The International Journal of Robotics Research, 32(14), 1700–1723.
Latimer, D., Srinivasa, S., Lee-Shue, V., Sonne, S., Choset, H., & Hurst, A. (2002). Towards sensor based coverage with robot teams. In Proceedings of the IEEE international conference on robotics and automation (Vol. 1, pp. 961–967).
Monderer, D., & Shapley, L. (1996). Potential games. Games and Economic Behavior, 14(1), 124–143.
Mukherjee, K., Gupta, S., Ray, A., & Phoha, S. (2011). Symbolic analysis of sonar data for underwater target detection. IEEE Journal of Oceanic Engineering, 36(2), 219–230.
Myerson, R. (2013). Game theory. Cambridge: Harvard University Press.
Palacin, J., Salse, J. A., Valganon, I., & Clua, X. (2004). Building a mobile robot for a floor-cleaning operation in domestic environments. IEEE Transactions on Instrumentation and Measurement, 53(5), 1418–1424.
Paull, L., Saeedi, S., Seto, M., & Li, H. (2014). AUV navigation and localization: A review. IEEE Journal of Oceanic Engineering, 39(1), 131–149.
Rekleitis, I., New, A., Rankin, E., & Choset, H. (2008). Efficient boustrophedon multi-robot coverage: An algorithmic approach. Annals of Mathematics and Artificial Intelligence, 52(2–4), 109–142.
Rieger, C., Gertman, D., & McQueen, M. (2009). Resilient control systems: next generation design research. In IEEE 2nd conference on human system interactions (pp. 632–636).
Rutishauser, S., Correll, N., & Martinoli, A. (2009). Collaborative coverage using a swarm of networked miniature robots. Robotics and Autonomous Systems, 57(5), 517–525.
Sadat, S. A., Wawerla, J., & Vaughan, R. (2015). Fractal trajectories for online non-uniform aerial coverage. In Proceedings of the IEEE international conference on robotics and automation (pp. 2971–2976).
Saulnier, K., Saldana, D., Prorok, A., Pappas, G., & Kumar, V. (2017). Resilient flocking for mobile robot teams. IEEE Robotics and Automation Letters, 2(2), 1039–1046.
Sheng, W., Yang, Q., Tan, J., & Xi, N. (2006). Distributed multi-robot coordination in area exploration. Robotics and Autonomous Systems, 54(12), 945–955.
Song, J., & Gupta, S. (2015). SLAM based shape adaptive coverage control using autonomous vehicles. In Proceedings of the IEEE system of systems engineering conference (pp. 268–273). San Antonio, TX.
Song, J., & Gupta, S. (2018). \(\varepsilon ^\star \): An online coverage path planning algorithm. IEEE Transactions on Robotics, 34, 526–533.
Song, J., Gupta, S., & Hare, J. (2014). Game-theoretic cooperative coverage using autonomous vehicles. In IEEE/MTS OCEANS’14, St. John’s (pp. 1–6).
Song, J., Gupta, S., Hare, J., & Zhou, S. (2013). Adaptive cleaning of oil spills by autonomous vehicles under partial information. In Proceedings of the MTS/IEEE OCEANS’13 (pp. 1–5). San Diego, CA.
Song, J., Gupta, S., & Wettergren, T. (2017). Time-optimal path planning for underwater vehicles in obstacle constrained environments. In Proceedings of the MTS/IEEE OCEANS’17 (pp. 1–6). Anchorage, AK.
Song, J., Gupta, S., & Wettergren, T. (2019). \(T^{\ast }\): Time-optimal risk-aware motion planning for curvature-constrained vehicles. IEEE Robotics and Automation Letters, 4(1), 33–40.
Song, Y., Wong, S., & Lee, K. (2011). Optimal gateway selection in multi-domain wireless networks: A potential game perspective. In Proceedings of the 17th ACM annual international conference on mobile computing and networking (pp. 325–336).
Sun, H., Peng, C., Yang, T., Zhang, H., & He, W. (2017). Resilient control of networked control systems with stochastic denial of service attacks. Neurocomputing, 270, 170–177.
Tolley, M., Shepherd, R., Mosadegh, B., Galloway, K., Wehner, M., Karpelson, M., et al. (2014). A resilient, untethered soft robot. Soft Robotics, 1(3), 213–223.
Xu, A., Viriyasuthee, C., & Rekleitis, I. (2014). Efficient complete coverage of a known arbitrary environment with applications to aerial operations. Autonomous Robots, 36(4), 365–381.
Xu, L., & Stentz, A. (2011). An efficient algorithm for environmental coverage with multiple robots. In Proceedings of the IEEE international conference on robotics and automation (pp. 4950–4955).
Yang, S., & Luo, C. (2004). A neural network approach to complete coverage path planning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), 718–724.
Zelinsky, A., Jarvis, R., Byrne, J., & Yuta, S. (1993). Planning paths of complete coverage of an unstructured environment by a mobile robot. In Proceedings of the International Conference on Advanced Robotics, Tokyo (pp. 533–538).
Zheng, X., Koenig, S., Kempe, D., & Jain, S. (2010). Multirobot forest coverage for weighted and unweighted terrain. IEEE Transactions on Robotics, 26(6), 1018–1031.
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This is one of the several papers published in Autonomous Robots comprising the Special Issue on Multi-Robot and Multi-Agent Systems.
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Song, J., Gupta, S. CARE: Cooperative Autonomy for Resilience and Efficiency of robot teams for complete coverage of unknown environments under robot failures. Auton Robot 44, 647–671 (2020). https://doi.org/10.1007/s10514-019-09870-3
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DOI: https://doi.org/10.1007/s10514-019-09870-3