Abstract
This paper presents a novel, sparse sensing motion planning algorithm for autonomous mobile robots in resource limited coverage problems. Optimizing usage of limited resources while effectively exploring an area is vital in scenarios where sensing is expensive, has adverse effects, or is exhaustive. We approach this problem using ergodic search techniques, which optimize how long a robot spends in a region based on the likelihood of obtaining informative measurements which guarantee coverage of a space. We recast the ergodic search problem to take into account when to take sensing measurements. This amounts to a mixed-integer program that optimizes when and where a sensor measurement should be taken while optimizing the agent’s paths for coverage. Using a continuous relaxation, we show that our formulation performs comparably to dense sampling methods, collecting information-rich measurements while adhering to limited sensing measurements. Multi-agent examples demonstrate the capability of our approach to automatically distribute sensor resources across the team. Further comparisons show comparable performance with the continuous relaxation of the mixed-integer program while reducing computational resources.
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References
Marín, L., Vallés, M., Soriano, A., Valera, A., Albertos, P.: Multi sensor fusion framework for indoor-outdoor localization of limited resource mobile robots. Sensors 13(10), 14133–14160 (2013). https://doi.org/10.3390/s131014133. https://www.mdpi.com/1424-8220/13/10/14133
Ma, F., Carlone, L., Ayaz, U., Karaman, S.: Sparse sensing for resource-constrained depth reconstruction. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 96–103 (2016). https://doi.org/10.1109/IROS.2016.7759040
Ablavsky, V., Snorrason, M.: Optimal search for a moving target - a geometric approach. In: AIAA Guidance, Navigation, and Control Conference and Exhibit. AIAA (2000)
Choset, H.: Coverage for robotics-a survey of recent results. Ann. Math. Artif. Intell. 31(1), 113–126 (2001)
Lanillos, P., Gan, S.K., Besada-Portas, E., Pajares, G., Sukkarieh, S.: Multi-UAV target search using decentralized gradient-based negotiation with expected observation. Inf. Sci. 282, 92–110 (2014)
Baxter, J.L., Burke, E.K., Garibaldi, J.M., Norman, M.: Multi-robot search and rescue: a potential field based approach. In: Mukhopadhyay, S.C., Gupta, G.S. (eds.) Autonomous Robots and Agents. Studies in Computational Intelligence, vol. 76, pp. 9–16. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73424-6_2
Wong, E.M., Bourgault, F., Furukawa, T.: Multi-vehicle bayesian search for multiple lost targets. In: International Conference on Robotics and Automation, pp. 3169–3174. IEEE (2005)
Mathew, G., Mezić, I.: Metrics for ergodicity and design of ergodic dynamics for multi-agent systems. Physica D 240(4), 432–442 (2011)
Wu, G.D., Zhu, Z.W., Chien, C.W.: Sparse-sensing-based wall-following control design for a mobile-robot. In: 2016 IEEE International Conference on Control and Robotics Engineering (ICCRE), pp. 1–5 (2016). https://doi.org/10.1109/ICCRE.2016.7476144
Miller, L.M., Silverman, Y., MacIver, M.A., Murphey, T.D.: Ergodic exploration of distributed information. IEEE Trans. Rob. 32(1), 36–52 (2015)
Abraham, I., Mavrommati, A., Murphey, T.: Data-driven measurement models for active localization in sparse environments. In: Robotics: Science and Systems XIV. Robotics: Science and Systems Foundation (2018). https://doi.org/10.15607/RSS.2018.XIV.045
Abraham, I., Prabhakar, A., Murphey, T.D.: An ergodic measure for active learning from equilibrium. IEEE Trans. Autom. Sci. Eng. 18(3), 917–931 (2021). https://doi.org/10.1109/TASE.2020.3043636
Ayvali, E., Salman, H., Choset, H.: Ergodic coverage in constrained environments using stochastic trajectory optimization. In: International Conference on Intelligent Robots and Systems, pp. 5204–5210. IEEE (2017)
Miller, L.M., Murphey, T.D.: Trajectory optimization for continuous ergodic exploration. In: American Control Conference (ACC) 2013, pp. 4196–4201. IEEE (2013)
Kobilarov, M.: Cross-entropy motion planning. The Int. J. Robot. Res. 31(7), 855–871 (2012)
Schmidt, M., Niculescu-Mizil, A., Murphy, K., et al.: Learning graphical model structure using l1-regularization paths. In: AAAI, vol. 7, pp. 1278–1283 (2007)
Wolsey, L.A.: Mixed Integer Programming. Wiley Encyclopedia of Computer Science and Engineering, pp. 1–10. Wiley, Hoboken (2007)
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Rao, A., Abraham, I., Sartoretti, G., Choset, H. (2024). Sparse Sensing in Ergodic Optimization. In: Bourgeois, J., et al. Distributed Autonomous Robotic Systems. DARS 2022. Springer Proceedings in Advanced Robotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-031-51497-5_9
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DOI: https://doi.org/10.1007/978-3-031-51497-5_9
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