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Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects (2203.04551v4)

Published 9 Mar 2022 in cs.MA

Abstract: We consider the online planning problem for a team of agents to discover and track an unknown and time-varying number of moving objects from onboard sensor measurements with uncertain measurement-object origins. Since the onboard sensors have limited field-of-views, the usual planning strategy based solely on either tracking detected objects or discovering unseen objects is inadequate. To address this, we formulate a new information-based multi-objective multi-agent control problem, cast as a partially observable Markov decision process (POMDP). The resulting multi-agent planning problem is exponentially complex due to the unknown data association between objects and multi-sensor measurements; hence, computing an optimal control action is intractable. We prove that the proposed multi-objective value function is a monotone submodular set function, which admits low-cost suboptimal solutions via greedy search with a tight optimality bound. The resulting planning algorithm has a linear complexity in the number of objects and measurements across the sensors, and quadratic in the number of agents. We demonstrate the proposed solution via a series of numerical experiments with a real-world dataset.

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References (49)
  1. A solution for large-scale multi-object tracking. Trans. on Sig. Proc., 2020.
  2. The complexity of decentralized control of Markov decision processes. Math. of Op. Research, 27(4):819–840, 2002.
  3. Observer and control design in partially observable finite Markov chains. Automatica, 110:108587, 2019.
  4. Evolutionary algorithms for solving multi-objective problems, volume 5. Springer, 2007.
  5. M. Corah and N. Michael. Distributed submodular maximization on partition matroids for planning on large sensor networks. In Proc. of CDC, pages 6792–6799, 2018.
  6. Elements of information theory. John Wiley & Sons, 2006.
  7. Detecting, localizing, and tracking an unknown number of moving targets using a team of mobile robots. The Inter. J. of Rob. Res., 36(13-14):1540–1553, 2017.
  8. {CRAWDAD} dataset epfl/mobility (v. 2009-02-24). Downloaded from https://crawdad.org/epfl/mobility/20090224, Feb. 2009.
  9. Detection and tracking meet drones challenge. Trans. on Pat. Ana. and Mach. Intel., pages 1–20, 2021.
  10. An analysis of approximations for maximizing submodular set functions—II. In Poly. Comb., pages 73–87. Springer, 1978.
  11. Model predictive control: Theory and practice—A survey. Automatica, 1989.
  12. Evaluating the accuracy of vehicle tracking data obtained from unmanned aerial vehicles. Inter. J. of Transport. Sci. and Tech., 5(3):136–151, 2016.
  13. Sensor management for multi-target tracking via multi-Bernoulli filtering. Automatica, 50(4):1135–1142, 2014.
  14. Submodularity and greedy algorithms in sensor scheduling for linear dynamical systems. Automatica, 61:282–288, 2015.
  15. J. Koski. Multicriterion structural optimization. In Optimization of Large Structural Systems, pages 793–809. Springer Netherlands, Dordrecht, 1993.
  16. Robust submodular observation selection. J. of Mac. Learn. Res., 9(Dec):2761–2801, 2008.
  17. Structure modeling and estimation of multiple resolvable group targets via graph theory and multi-Bernoulli filter. Automatica, 89, 2018.
  18. R. Mahler. A GLMB filter for unified multitarget multisensor management. In Sig. Proc., Sens./Info. Fusion, and Target Rec. XXVIII, volume 11018, page 110180D, 2019.
  19. R. P. Mahler. Statistical multisource-multitarget information fusion. Artech House, 2007.
  20. R. P. Mahler. Advances in statistical multisource-multitarget information fusion. Artech House, 2014.
  21. Efficient offline communication policies for factored multiagent POMDPs. In Proc. of 24th NeurIPS, pages 1917–1925, 2011.
  22. An analysis of approximations for maximizing submodular set functions—I. Math. Prog., 14(1):265–294, 1978.
  23. TrackerBots: Autonomous unmanned aerial vehicle for real-time localization and tracking of multiple radio-tagged animals. J. of F. Rob., 36(3), 2019.
  24. Multi-objective multi-agent planning for jointly discovering and tracking mobile objects. In Proc. of 34th AAAI, pages 7227–7235, Feb 2020.
  25. Distributed Multi-object Tracking under Limited Field of View Sensors. Trans. on Sig. Proc., 2021.
  26. Distributed greedy algorithm for multi-agent task assignment problem with submodular utility functions. Automatica, 105:206–215, 2019.
  27. The labeled multi-Bernoulli filter. Trans. on Sig. Proc., 62(12):3246–3260, 2014.
  28. B. Ristic and B.-N. Vo. Sensor control for multi-object state-space estimation using random finite sets. Automatica, 46(11):1812–1818, 2010.
  29. Probabilistic robotics. MIT Press, 2005.
  30. Multi-sensor multi-object tracking with the generalized labeled multi-bernoulli filter. Trans. on Sig. Proc., 67(23):5952–5967, Dec 2019.
  31. Multi-sensor joint detection and tracking with the {B}ernoulli filter. Trans. on Aero. and Elect. Sys., 48(2):1385–1402, 2012.
  32. Labeled random finite sets and multi-object conjugate priors. Trans. on Sig. Proc., 2013.
  33. Multi-sensor control for multi-object Bayes filters. Sig. Proc., 142:260–270, 2018.
  34. A new performance bound for submodular maximization problems and its application to multi-agent optimal coverage problems. Automatica, 144:110493, 2022.
  35. Urban traffic density estimation based on ultrahigh-resolution UAV video and deep neural network. J. of Sel. Top. in App. E. Obs. and Re. Sens., 11(12):4968–4981, 2018.
  36. Multi-objective optimization based multi-Bernoulli sensor selection for multi-target tracking. Sensors, 19(4):980 – 997, 2019.
  37. Informative path planning for an autonomous underwater vehicle. In 2010 IEEE International Conference on Robotics and Automation, pages 4791–4796. IEEE, 2010.
  38. Online localization of radio-tagged wildlife with an autonomous aerial robot system. In Robotics: Science and Systems, 2015.
  39. Mobile sensor network control using mutual information methods and particle filters. IEEE Transactions on Automatic Control, 55(1):32–47, 2009.
  40. Active sensing for motion planning in uncertain environments via mutual information policies. The International Journal of Robotics Research, 38(2-3):146–161, 2019.
  41. Sensor-driven online coverage planning for autonomous underwater vehicles. IEEE/ASME Transactions on Mechatronics, 18(6):1827–1838, 2012.
  42. Optimal UAV sensor management and path planning for tracking multiple mobile targets. In Dynamic Systems and Control Conference, volume 46193, page V002T25A003. American Society of Mechanical Engineers, 2014.
  43. Centralized cooperative sensor fusion for dynamic sensor network with limited field-of-view via labeled multi-Bernoulli filter. IEEE Transactions on Signal Processing, 69:878–891, 2020.
  44. A Bayesian filter for multi-view 3D multi-object tracking with occlusion handling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2246–2263, 2020.
  45. Multi-agent fusion with different limited fields-of-view. IEEE Transactions on Signal Processing, 70:1560–1575, 2022.
  46. S. Panicker, A. K. Gostar, A. Bab-Hadiashar, and R. Hoseinnezhad, “Tracking of targets of interest using labeled multi-bernoulli filter with multi-sensor control,” Signal Processing, vol. 171, p. 107451, 2020.
  47. B.-N. Vo, S. Singh, and A. Doucet, “Sequential monte carlo methods for multitarget filtering with random finite sets,” IEEE Transactions on Aerospace and electronic systems, vol. 41, no. 4, pp. 1224–1245, 2005.
  48. V. Roberge, M.~Tarbouchi, and G.~Labonte, “Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning,” IEEE Transactions on industrial informatics, vol. 9, no. 1, pp. 132–141, 2012.
  49. D.S. Bernstein, R.~Givan, N.~Immerman, and S.~Zilberstein, “The complexity of decentralized control of Markov decision processes,” Mathematics of operations research, vol. 27, no. 4, pp. 819–840, 2002.
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Authors (5)
  1. Hoa Van Nguyen (14 papers)
  2. Ba-Ngu Vo (24 papers)
  3. Ba-Tuong Vo (17 papers)
  4. Hamid Rezatofighi (61 papers)
  5. Damith C. Ranasinghe (53 papers)
Citations (8)
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