Multi-Robot Cooperated Sensor Networks Hierarchical Expanding Deployment and Perception Architecture
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
Collaborative coverage using a swarm of networked miniature robots
Robotics and Autonomous Systems
A framework for multi-robot node coverage in sensor networks
Annals of Mathematics and Artificial Intelligence
Rapid exploration of unknown areas through dynamic deployment of mobile and stationary sensor nodes
Autonomous Agents and Multi-Agent Systems
On fast exploration in 2D and 3D terrains with multiple robots
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multi-robot tree and graph exploration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Ad-hoc wireless network coverage with networked robots that cannot localize
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Swarm dispersion via potential fields, leader election, and counting hops
SIMPAR'10 Proceedings of the Second international conference on Simulation, modeling, and programming for autonomous robots
Adaptive multi-robot team reconfiguration using a policy-reuse reinforcement learning approach
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
Navigation for indoor mobile robot based on wireless sensor network
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
Rolling dispersion for robot teams
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present the design and theoretical analysis of a novel algorithm termed least recently visited (LRV). LRV efficiently and simultaneously solves the problems of coverage, exploration, and sensor network deployment. The basic premise behind the algorithm is that a robot carries network nodes as a payload, and in the process of moving around, emplaces the nodes into the environment based on certain local criteria. In turn, the nodes emit navigation directions for the robot as it goes by. Nodes recommend directions least recently visited by the robot, hence, the name LRV. We formally establish the following two properties: 1) LRV is complete on graphs and 2) LRV is optimal on trees. We present experimental conjectures for LRV on regular square and cube lattice graphs and compare its performance empirically to other graph exploration algorithms. We study the effects of the order of the exploration and show on a square lattice that with an appropriately chosen order, LRV performs optimally. Finally, we discuss the implementation of LRV in simulation and in real hardware.