A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Motion Planning and Control
Robot Motion Planning and Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Elastic Strips: A Framework for Integrated Planning and Execution
The Sixth International Symposium on Experimental Robotics VI
Dynamic Programming
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Genetic algorithms for positioning and utilizing sensors in synthetically generated landscapes
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Global estimation in constrained environments
International Journal of Robotics Research
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This work presents a comparison of decision making criteria and optimization methods for active sensing in robotics. Active sensing incorporates the following aspects: (i) where to position sensors, and (ii) how to make decisions for next actions, in order to maximize information gain and minimize costs. We concentrate on the second aspect: "Where should the robot move at the next time step?". Pros and cons of the most often used statistical decision making strategies are discussed. Simulation results from a new multisine approach for active sensing of a nonholonomic mobile robot are given.