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
An Incremental Self-Deployment Algorithm for Mobile Sensor Networks
Autonomous Robots
Randomized Pursuit-Evasion in Graphs
Combinatorics, Probability and Computing
Visibility-based Pursuit-evasion with Limited Field of View
International Journal of Robotics Research
Mutual localization in a multi-robot system with anonymous relative position measures
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Improving the Efficiency of Clearing with Multi-agent Teams
International Journal of Robotics Research
On Discrete-Time Pursuit-Evasion Games With Sensing Limitations
IEEE Transactions on Robotics
Multi-UAV motion planning for guaranteed search
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
A passivity-based decentralized strategy for generalized connectivity maintenance
International Journal of Robotics Research
Mutual localization in multi-robot systems using anonymous relative measurements
International Journal of Robotics Research
Active tracking and pursuit under different levels of occlusion: a two-layer approach
Machine Vision and Applications
Hi-index | 0.00 |
This paper addresses a visibility-based pursuit-evasion problem in which a team of mobile robots with limited sensing and communication capabilities must coordinate to detect any evaders in an unknown, multiply-connected planar environment. Our distributed algorithm to guarantee evader detection is built around maintaining complete coverage of the frontier between cleared and contaminated regions while expanding the cleared region. We detail a novel distributed method for storing and updating this frontier without building a map of the environment or requiring global localization. We demonstrate the functionality of the algorithm through simulations in realistic environments and through hardware experiments. We also compare Monte Carlo results for our algorithm to the theoretical optimum area cleared as a function of the number of robots available.