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Title: A point-based POMDP planner for target tracking
Authors: Hsu, D. 
Lee, W.S. 
Rong, N.
Issue Date: 2008
Citation: Hsu, D., Lee, W.S., Rong, N. (2008). A point-based POMDP planner for target tracking. Proceedings - IEEE International Conference on Robotics and Automation : 2644-2650. ScholarBank@NUS Repository.
Abstract: Target tracking has two variants that are often studied independently with different approaches: target searching requires a robot to find a target initially not visible, and target following requires a robot to maintain visibility on a target initially visible. In this work, we use a partially observable Markov decision process (POMDP) to build a single model that unifies target searching and target following. The POMDP solution exhibits interesting tracking behaviors, such as anticipatory moves that exploit target dynamics, information-gathering moves that reduce target position uncertainty, and energy-conserving actions that allow the target to get out of sight, but do not compromise long-term tracking performance. To overcome the high computational complexity of solving POMDPs, we have developed SARSOP, a new point-based POMDP algorithm based on successively approximating the space reachable under optimal policies. Experimental results show that SARSOP is competitive with the fastest existing point-based algorithm on many standard test problems and faster by many times on some. ©2008 IEEE.
Source Title: Proceedings - IEEE International Conference on Robotics and Automation
ISBN: 9781424416479
ISSN: 10504729
DOI: 10.1109/ROBOT.2008.4543611
Appears in Collections:Staff Publications

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