The complexity of searching a graph
Journal of the ACM (JACM)
Searching for a mobile intruder in a polygonal region
SIAM Journal on Computing
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Sweeping simple polygons with a chain of guards
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Pursuit-evasion on trees by robot teams
IEEE Transactions on Robotics
Multi-UAV motion planning for guaranteed search
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In this paper we present a method to extract surveillance graphs from occupancy grid maps. Surveillance graphs are part of the Graph-Clear framework and model the problem of detecting targets using multiple robots with limited range sensors. Robots can only execute basic actions called sweep and block on vertices and edges, respectively. Sweep detects targets in vertices and block prevents them from crossing edges. The extracted graphs accurately model the complexity of the planar environment to be searched, and are constructed as duals of the Voronoi Diagram. We give a geometric embedding for blocking and sweeping actions of the graph into the environment by directly associating them to sweep lines that robots cover with their sensors. This paper solves two open problems, namely the generation of surveillance graphs and the implementation of actions on a robot team. Sweep lines can then be directly translated into control inputs to the robot team. The new method is superior to previous heuristics for the extraction of graphs not only through its direct geometric relationship to the environment, but also due to its increased performance in direct experimental comparisons. Additionally, it provides a basis for possible theoretical results regarding the optimal coordination of multiple robots to detect targets in an arbitrary planar environment.