Robotics: Science and Systems VII
Unmanned Aircraft Collision Avoidance using Continuous-State POMDPs
Haoyu Bai, David Hsu, Mykel Kochenderfer, Wee Sun LeeAbstract:
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.
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} }