Planning and acting in partially observable stochastic domains
Artificial Intelligence
Robot Motion Planning
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Hierarchical learning and planning in partially observable markov decision processes
Hierarchical learning and planning in partially observable markov decision processes
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A combined tactical and strategic hierarchical learning framework in multi-agent games
Sandbox '08 Proceedings of the 2008 ACM SIGGRAPH symposium on Video games
Efficient planning under uncertainty with macro-actions
Journal of Artificial Intelligence Research
A partially observable hybrid system model for bipedal locomotion for adapting to terrain variations
Proceedings of the 16th international conference on Hybrid systems: computation and control
Decentralized multi-robot cooperation with auctioned POMDPs
International Journal of Robotics Research
A Probabilistically Robust Path Planning Algorithm for UAVs Using Rapidly-Exploring Random Trees
Journal of Intelligent and Robotic Systems
Map partitioning to approximate an exploration strategy in mobile robotics
Multiagent and Grid Systems
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This paper proposes a new hierarchical formulation of POMDPs for autonomous robot navigation that can be solved in real-time, and is memory efficient. It will be referred to in this paper as the Robot Navigation-Hierarchical POMDP (RN-HPOMDP). The RN-HPOMDP is utilized as a unified framework for autonomous robot navigation in dynamic environments. As such, it is used for localization, planning and local obstacle avoidance. Hence, the RN-HPOMDP decides at each time step the actions the robot should execute, without the intervention of any other external module for obstacle avoidance or localization. Our approach employs state space and action space hierarchy, and can effectively model large environments at a fine resolution. Finally, the notion of the reference POMDP is introduced. The latter holds all the information regarding motion and sensor uncertainty, which makes the proposed hierarchical structure memory efficient and enables fast learning. The RN-HPOMDP has been experimentally validated in real dynamic environments.