Searching for a mobile intruder in a polygonal region
SIAM Journal on Computing
Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Visibility-Based Pursuit-Evasion in a Polygonal Environment
WADS '97 Proceedings of the 5th International Workshop on Algorithms and Data Structures
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Randomized Pursuit-Evasion in Graphs
Combinatorics, Probability and Computing
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Visibility-based Pursuit-evasion with Limited Field of View
International Journal of Robotics Research
Deriving the Medial Axis with geometrical arguments for planar shapes
Pattern Recognition Letters
Pursuit-evasion on trees by robot teams
IEEE Transactions on Robotics
Toward simulating realistic pursuit-evasion using a roadmap-based approach
MIG'10 Proceedings of the Third international conference on Motion in games
Environment characterization for non-recontaminating frontier-based robotic exploration
PRIMA'11 Proceedings of the 14th international conference on Agents in Principle, Agents in Practice
Environment characterization for non-recontaminating frontier-based robotic exploration
PRIMA'11 Proceedings of the 14th international conference on Agents in Principle, Agents in Practice
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This paper addresses the problem of obtaining a concise description of a physical environment for robotic exploration. We aim to determine the number of robots required to clear an environment using non-recontaminating exploration. We introduce the medial axis as a configuration space and derive a mathematical representation of a continuous environment that captures its underlying topology and geometry. We show that this representation provides a concise description of arbitrary environments, and that reasoning about points in this representation is equivalent to reasoning about robots in physical space. We leverage this to derive a lower bound on the number of required pursuers. We provide a transformation from this continuous representation into a symbolic representation. Finally, we present a generalized pursuit-evasion algorithm. Given an environment we can compute how many pursuers we need, and generate an optimal pursuit strategy that will guarantee the evaders are detected with the minimum number of pursuers.