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|Title:||A point-based POMDP planner for target tracking|
|Authors:||Hsu, D. |
|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. https://doi.org/10.1109/ROBOT.2008.4543611|
|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|
|Appears in Collections:||Staff Publications|
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