An efficient heuristic approach for security against multiple adversaries
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
The impact of adversarial knowledge on adversarial planning in perimeter patrol
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Uncertainties in adversarial patrol
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Developing a Deterministic Patrolling Strategy for Security Agents
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Collaborative multi agent physical search with Probabilistic knowledge
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Adversarial uncertainty in multi-robot patrol
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multi-robot area patrol under frequency constraints
Annals of Mathematics and Artificial Intelligence
Review: Of robot ants and elephants: A computational comparison
Theoretical Computer Science
Boundary patrolling by mobile agents with distinct maximal speeds
ESA'11 Proceedings of the 19th European conference on Algorithms
Multi-robot adversarial patrolling: facing a full-knowledge opponent
Journal of Artificial Intelligence Research
Physical search problems with probabilistic knowledge
Artificial Intelligence
Multi-robot repeated area coverage
Autonomous Robots
Optimal patrolling of fragmented boundaries
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
Stochastic surveillance strategies for spatial quickest detection
International Journal of Robotics Research
Journal of Intelligent and Robotic Systems
Hi-index | 0.00 |
There is growing interest in multi-robot frequency-based patrolling, in which a team of robots optimizes its frequency of point visits, for every point in a target work area. In particular, recent work on patrolling of open polygons (e.g., open-ended fences) has proposed a general cooperative patrolling algorithm, in which robots move back and forth along the polygon, in an synchronized manner, such that their assigned areas of movement overlap. If the overlap factor is carefully chosen---based on the motion models of the robots---specific performance criteria are optimized. Unfortunately, previous work has presented analysis of motion models in which there are no errors in the movement of the robots, and no velocity changes. We go a step beyond existing work, and develop a realistic model of robot motion, that considers velocity uncertainties. We mathematically analyze the model and show how to use it to find optimal patrolling parameters, given known bounds of uncertainty on the motion. We then use the model to analyze the independently-programmed patrolling movements of physical robots, in extensive experiments. We show that the model predicts the behavior of the robots much more accurately than previously-described models.