2000 character limit reached
Stein Coverage: a Variational Inference Approach to Distribution-matching Multisensor Deployment (2312.07001v1)
Published 12 Dec 2023 in cs.MA
Abstract: This paper examines the spatial coverage optimization problem for multiple sensors in a known convex environment, where the coverage service of each sensor is heterogeneous and anisotropic. We introduce the Stein Coverage algorithm, a distribution-matching coverage approach that aims to place sensors at positions and orientations such that their collective coverage distribution is as close as possible to the event distribution. To select the most important representative points from the coverage event distribution, Stein Coverage utilizes the Stein Variational Gradient Descent (SVGD), a deterministic sampling method from the variational inference literature. An innovation in our work is the introduction of a repulsive force between the samples in the SVGD algorithm to spread the samples and avoid footprint overlap for the deployed sensors. After pinpointing the points of interest for deployment, Stein Coverage solves the multisensor assignment problem using a bipartite optimal matching process. Simulations demonstrate the advantages of the Stein Coverage method compared to conventional Voronoi partitioning multisensor deployment methods.
- O. Arslan, “Statistical coverage control of mobile sensor networks,” IEEE Transactions on Robotics, vol. 35, no. 4, pp. 889–908, 2019.
- G. Notomista, S. Mayya, Y. Emam, C. Kroninger, A. Bohannon, S. Hutchinson, and M. Egerstedt, “A resilient and energy-aware task allocation framework for heterogeneous multirobot systems,” IEEE Transactions on Robotics, vol. 38, no. 1, pp. 159–179, 2022.
- A. Farinha, R. Zufferey, P. Zheng, S. F. Armanini, and M. Kovac, “Unmanned aerial sensor placement for cluttered environments,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6623–6630, 2020.
- J. Cortes, S. Martinez, T. Karatas, and F. Bullo, “Coverage control for mobile sensing networks,” IEEE Transactions on Robotics and Automation, vol. 20, no. 2, pp. 243–255, 2004.
- J. Aponte-Luis, J. A. Gómez-Galán, F. Gómez-Bravo, M. Sánchez-Raya, J. Alcina-Espigado, and P. M. Teixido-Rovira, “An efficient wireless sensor network for industrial monitoring and control,” Sensors, vol. 18, no. 1, 2018.
- N. Megiddo and K. J. Supowit, “On the complexity of some common geometric location problems,” SIAM Journal on Computing, vol. 13, no. 1, pp. 182–196, 1984.
- M. Schwager, D. Rus, and J.-J. Slotine, “Decentralized, adaptive coverage control for networked robots,” The International Journal of Robotics Research, vol. 28, no. 3, pp. 357–375, 2009.
- O. Arslan and D. E. Koditschek, “Voronoi-based coverage control of heterogeneous disk-shaped robots,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 4259–4266, 2016.
- A. Kwok and S. Martinez, “Deployment algorithms for a power-constrained mobile sensor network,” in 2008 IEEE International Conference on Robotics and Automation, pp. 140–145, 2008.
- M. Santos and M. Egerstedt, “Coverage control for multi-robot teams with heterogeneous sensing capabilities using limited communications,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5313–5319, 2018.
- A. Krause, A. Singh, and C. Guestrin, “Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies.,” Journal of Machine Learning Research, vol. 9, no. 2, 2008.
- A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clustering algorithm,” Pattern Recognition, vol. 36, no. 2, pp. 451–461, 2003. Biometrics.
- D. Inoue, Y. Ito, and H. Yoshida, “Optimal transport-based coverage control for swarm robot systems: Generalization of the voronoi tessellation-based method,” IEEE Control Systems Letters, vol. 5, no. 4, pp. 1483–1488, 2021.
- V. Krishnan and S. Martínez, “A multiscale analysis of multi-agent coverage control algorithms,” Automatica, vol. 145, p. 110516, 2022.
- Y.-f. Chung and S. S. Kia, “A distributed service-matching coverage via heterogeneous agents,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4400–4407, 2022.
- D. Ghimire and S. S. Kia, “Optimal multi-sensor deployment via sample-based quality-of-service distribution matching,” in 2023 European Control Conference (ECC), pp. 1–6, 2023.
- Q. Liu, J. Lee, and M. Jordan, “A kernelized stein discrepancy for goodness-of-fit tests,” in International Conference on Machine Learning, pp. 276–284, PMLR, 2016.
- S.-i. Amari and A. Cichocki, “Information geometry of divergence functions,” Bulletin of the polish academy of sciences. Technical sciences, vol. 58, no. 1, pp. 183–195, 2010.
- Q. Liu, “Stein variational gradient descent as gradient flow,” Advances in neural information processing systems, vol. 30, 2017.
- C. Stein, “Approximate computation of expectations,” in Lecture Notes-Monograph Series, IMS, 1986.
- Q. Liu and D. Wang, “Stein variational gradient descent: A general purpose bayesian inference algorithm,” Advances in neural information processing systems, vol. 29, 2016.
- G. Detommaso, T. Cui, Y. Marzouk, A. Spantini, and R. Scheichl, “A stein variational newton method,” in Advances in Neural Information Processing Systems (S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, eds.), vol. 31, Curran Associates, Inc., 2018.
- H. W. Kuhn, “The hungarian method for the assignment problem,” Naval Research Logistics Quarterly, vol. 2, no. 1-2, pp. 83–97, 1955.
- S. Wilson, P. Glotfelter, L. Wang, S. Mayya, G. Notomista, M. Mote, and M. Egerstedt, “The robotarium: Globally impactful opportunities, challenges, and lessons learned in remote-access, distributed control of multirobot systems,” IEEE Control Systems Magazine, vol. 40, no. 1, pp. 26–44, 2020.