Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Robot motion planning with uncertainty in control and sensing
Artificial Intelligence
Elements of information theory
Elements of information theory
Searching for a mobile intruder in a polygonal region
SIAM Journal on Computing
Understanding action and sensing by designing action-based sensors
International Journal of Robotics Research - Special issue on integration among planning, sensing, and control
Mechanics of robotic manipulation
Mechanics of robotic manipulation
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Robot Motion Planning
Linear System Theory and Design
Linear System Theory and Design
Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Planning Algorithms
Visibility Algorithms in the Plane
Visibility Algorithms in the Plane
International Journal of Robotics Research
On the power of the compass (or, why mazes are easier to search than graphs)
SFCS '78 Proceedings of the 19th Annual Symposium on Foundations of Computer Science
On information invariants in robotics
Artificial Intelligence
I-bug: an intensity-based bug algorithm
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Counting targets with mobile sensors in an unknown environment
ALGOSENSORS'07 Proceedings of the 3rd international conference on Algorithmic aspects of wireless sensor networks
Distance-Optimal Navigation in an Unknown Environment Without Sensing Distances
IEEE Transactions on Robotics
IEEE Transactions on Information Theory
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This monograph presents an unusual perspective on sensing uncertainty and filtering with the intention of understanding what information is minimally needed to achieve a specified task. Information itself is modeled using information space concepts, which originated from dynamic game theory (rather than information theory, which was developed mainly for communication). The guiding principle in this monograph is avoid sensing, representing, and encoding more than is necessary. The concepts and tools are motivated by many tasks of current interest, such as tracking, monitoring, navigation, pursuit-evasion, exploration, and mapping. First, an overview of sensors that appear in numerous systems is presented. Following this, the notion of a virtual sensor is explained, which provides a mathematical way to model numerous sensors while abstracting away their particular physical implementation. Dozens of useful models are given, each as a mapping from the physical world to the set of possible sensor outputs. Preimages with respect to this mapping represent a fundamental source of uncertainty: These are equivalence classes of physical states that would produce the same sensor output. Pursuing this idea further, the powerful notion of a sensor lattice is introduced, in which all possible virtual sensors can be rigorously compared. The next part introduces filters that aggregate information from multiple sensor readings. The integration of information over space and time is considered. In the spatial setting, classical triangulation methods are expressed in terms of preimages. In the temporal setting, an information-space framework is introduced that encompasses familiar Kalman and Bayesian filters, but also introduces a novel family called combinatorial filters. Finally, the planning problem is presented in terms of filters and information spaces. The monograph concludes with some discussion about connections to many related research fields and numerous open problems and future research directions.