Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Theoretical Analysis of the Multi-agent Patrolling Problem
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Swarm Approaches for the Patrolling Problem, Information Propagation vs. Pheromone Evaporation
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Multi-agent patrolling: an empirical analysis of alternative architectures
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
A fault-tolerant modular control approach to multi-robot perimeter patrol
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Boundary patrolling by mobile agents with distinct maximal speeds
ESA'11 Proceedings of the 19th European conference on Algorithms
Multilevel-based topology design and shape control of robot swarms
Automatica (Journal of IFAC)
Optimal patrolling of fragmented boundaries
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
Distributed multi-robot patrol: A scalable and fault-tolerant framework
Robotics and Autonomous Systems
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This paper proposes a multiple robot control algorithm to approach the problem of patrolling an open or closed line. The algorithm is fully decentralized, i.e., no communication occurs between robots or with a central station. Robots behave according only to their sensing and computing capabilities to ensure high scalability and robustness towards robots' fault. The patrolling algorithm is designed in the framework of behavioral control and it is based on the concept of Action: an higher level of abstraction with respect to the behaviors. Each Action is obtained by combining more elementary behaviors in the Null-Space-Behavioral framework. A Finite-State-Automata is designed as supervisor in charge of selecting the appropriate action. The approach has been validated in simulation as well as experimentally with a patrol of 3 Pioneer robots available at the Distributed Intelligence Laboratory of the University of Tennessee.