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This paper proposes an informative trajectory planning approach, namely, \textit{adaptive particle filter tree with sigma point-based mutual information reward approximation} (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view.
Oregon Health & Science University, 2004
R. Van Der Merwe, Sigma-point Kalman filters for probabilistic inference in dynamic state-space models · 2004
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MIT Press, 2005
S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics · 2005
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T. Furukawa, F. Bourgault, B. Lavis, and H. F. Durrant-Whyte, “Recursive bayesian search-and-tracking using coordinated uavs for lost targets,” in Proceedings of International Conference on Robotics and Automation (ICRA)
2006
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T. H. Chung, J. W. Burdick, and R. M. Murray, “A decentralized motion coordination strategy for dynamic target tracking,” in Proceedings of International Conference on Robotics and Automation (ICRA)
2006
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F. Bourgault, T. Furukawa, and H. F. Durrant-Whyte, “Optimal search for a lost target in a bayesian world,” Field and Service Robotics: Recent Advances in Reserch and Applications
2006
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L. Kocsis and C. Szepesvári, “Bandit based monte-carlo planning,” in European conference on machine learning
2006
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J. Tisdale, A. Ryan, Z. Kim, D. Tornqvist, and J. K. Hedrick, “A multiple uav system for vision-based search and localization,” in American Control Conference (ACC)
2008
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M. F. Huber, T. Bailey, H. Durrant-Whyte, and U. D. Hanebeck, “On entropy approximation for gaussian mixture random vectors,” in International Conference on Multisensor Fusion and Integration for Intelligent Systems
2008
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G. M. Hoffmann and C. J. Tomlin, “Mobile sensor network control using mutual information methods and particle filters,” IEEE Transactions on Automatic Control
2009
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G. Hollinger, S. Singh, J. Djugash, and A. Kehagias, “Efficient multi-robot search for a moving target,” International Journal of Robotics Research (IJRR)
2009
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J. Tisdale, Z. Kim, and J. K. Hedrick, “Autonomous uav path planning and estimation,” IEEE Robotics & Automation Magazine
2009
Cited alongside, same era.
A. Ryan and J. K. Hedrick, “Particle filter based information-theoretic active sensing,” Robotics and Autonomous Systems
2010
Cited alongside, same era.
2011
Cited alongside, same era.
B. J. Julian, M. Angermann, M. Schwager, and D. Rus, “Distributed robotic sensor networks: An information-theoretic approach,” International Journal of Robotics Research (IJRR)
2012
Cited alongside, same era.
B. Charrow, V. Kumar, and N. Michael, “Approximate representations for multi-robot control policies that maximize mutual information,” Autonomous Robots
F. Niroui, K. Zhang, Z. Kashino, and G. Nejat, “Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments,” IEEE Robotics and Automation Letters (RA-L)
2019
Later among the works it cites.
M. Ghaffari Jadidi, J. Valls Miro, and G. Dissanayake, “Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring,” International Journal of Robotics Research (IJRR)
2019
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A. Wandzel, Y. Oh, M. Fishman, N. Kumar, L. L. Wong, and S. Tellex, “Multi-object search using object-oriented pomdps,” in Proceedings of International Conference on Robotics and Automation (ICRA)
2019
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J. Fischer and Ö. S. Tas, “Information particle filter tree: An online algorithm for pomdps with belief-based rewards on continuous domains,” in International Conference on Machine Learning
2020
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2014
Cited alongside, same era.
F. Vanegas, D. Campbell, M. Eich, and F. Gonzalez, “Uav based target finding and tracking in gps-denied and cluttered environments,” in Proceedings of International Conference on Intelligent Robots and Systems (IROS)
2016
Cited alongside, same era.
C. Liu and J. K. Hedrick, “Model predictive control-based target search and tracking using autonomous mobile robot with limited sensing domain,” in American Control Conference (ACC)
2017
Cited alongside, same era.
A. Goldhoorn, A. Garrell, R. Alquézar, and A. Sanfeliu, “Searching and tracking people with cooperative mobile robots,” Autonomous Robots
2018
Cited alongside, same era.
Z. Sunberg and M. Kochenderfer, “Online algorithms for pomdps with continuous state, action, and observation spaces,” in Proceedings of the International Conference on Automated Planning and Scheduling
2018
Cited alongside, same era.
M. Aggravi, A. A. S. Elsherif, P. R. Giordano, and C. Pacchierotti, “Haptic-enabled decentralized control of a heterogeneous human-robot team for search and rescue in partially-known environments,” IEEE Robotics and Automation Letters (RA-L)
2021
Later among the works it cites.
E. Lozano, U. Ruiz, I. Becerra, and R. Murrieta-Cid, “Surveillance and collision-free tracking of an aggressive evader with an actuated sensor pursuer,” IEEE Robotics and Automation Letters (RA-L)
2022
Later among the works it cites.
A. Asgharivaskasi, S. Koga, and N. Atanasov, “Active mapping via gradient ascent optimization of shannon mutual information over continuous se (3) trajectories,” in Proceedings of International Conference on Intelligent Robots and Systems (IROS)
2022
Later among the works it cites.
P. Yang, Y. Liu, S. Koga, A. Asgharivaskasi, and N. Atanasov, “Learning continuous control policies for information-theoretic active perception,” in 2023 IEEE International Conference on Robotics and Automation (ICRA)
2023
Later among the works it cites.
H. Gao, P. Wu, Y. Su, K. Zhou, J. Ma, H. Liu, and C. Liu, “Probabilistic visibility-aware trajectory planning for target tracking in cluttered environments,” in American Control Conference (ACC)
2024
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