Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ISROBOTNET: a testbed for sensor and robot network systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
ISROBOTNET: a testbed for sensor and robot network systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Vector-Value Markov Decision Process for multi-objective stochastic path planning
International Journal of Hybrid Intelligent Systems
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We consider the problem of sensor-aware path planning for a robot in a Networked Robot System, in particular in urban environments equipped with a network of surveillance cameras. A robot can use observations from the camera network to improve its own localization performance, but also needs to take into account the specifics of its local sensors. We model our problem in the Markov Decision Process framework, which forms a natural way to express concurrent and possibly conflicting objectives - such as reaching a goal quickly, keeping the robot localized, keeping the target in sight - each with their own priority. We show how we can successfully prioritize the different objectives in a flexible way by changing the reward function, based on the sensory needs of the system.