Robotics: Science and Systems VII

Unmanned Aircraft Collision Avoidance using Continuous-State POMDPs

Haoyu Bai, David Hsu, Mykel Kochenderfer, Wee Sun Lee

Abstract:

An effective collision avoidance system for unmanned aircraft will enable them to fly in civil airspace and greatly expand their applications. One promising approach is to model the system as a partially observable Markov decision process (POMDP) and generate the threat resolution logic automatically by solving the model. However, existing discrete-state POMDP algorithms cannot cope with the high-dimensional state space in collision avoidance POMDPs. Using a recently-developed algorithm called Monte Carlo Value Iteration (MCVI), we constructed several continuous-state POMDP models and solved them directly without discretizing the state space. Simulation results show that our 3-D continuous-state models reduce the collision risk by up to 70 times, compared with earlier 2-D discrete-state POMDP models. The success demonstrates both the benefits of continuous-state POMDP models for collision avoidance systems and the latest algorithmic progress in solving these complex models.

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Bibtex:

  
@INPROCEEDINGS{Bai-RSS-11, 
    AUTHOR    = {Haoyu Bai AND David Hsu AND  Mykel Kochenderfer AND Wee Sun Lee}, 
    TITLE     = {Unmanned Aircraft Collision Avoidance using Continuous-State POMDPs}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2011}, 
    ADDRESS   = {Los Angeles, CA, USA}, 
    MONTH     = {June},
    DOI       = {10.15607/RSS.2011.VII.001} 
}