Elements of information theory
Elements of information theory
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Convex Optimization
Theoretical Analysis of the Multi-agent Patrolling Problem
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Fastest Mixing Markov Chain on a Graph
SIAM Review
Planning Algorithms
A realistic model of frequency-based multi-robot polyline patrolling
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Probabilistic Multiagent Patrolling
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
Survey paper: Research on probabilistic methods for control system design
Automatica (Journal of IFAC)
Decentralized Perimeter Surveillance Using a Team of UAVs
IEEE Transactions on Robotics
Automatica (Journal of IFAC)
Minimax Robust Quickest Change Detection
IEEE Transactions on Information Theory
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Randomized Sensor Selection in Sequential Hypothesis Testing
IEEE Transactions on Signal Processing
Analysis of Search Decision Making Using Probabilistic Search Strategies
IEEE Transactions on Robotics
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We design persistent surveillance strategies for the quickest detection of anomalies taking place in an environment of interest. From a set of predefined regions in the environment, a team of autonomous vehicles collects noisy observations, which a control center processes. The overall objective is to minimize detection delay while maintaining the false-alarm rate below a desired threshold. We present joint (i) anomaly detection algorithms for the control center and (ii) vehicle routing policies. For the control center, we propose parallel cumulative sum (CUSUM) algorithms (one for each region) to detect anomalies from noisy observations. For the vehicles, we propose a stochastic routing policy, in which the regions to be visited are chosen according to a probability vector. We study stationary routing policy (the probability vector is constant) as well as adaptive routing policies (the probability vector varies in time as a function of the likelihood of regional anomalies). In the context of stationary policies, we design a performance metric and minimize it to design an efficient stationary routing policy. Our adaptive policy improves upon the stationary counterpart by adaptively increasing the selection probability of regions with high likelihood of anomaly. Finally, we show the effectiveness of the proposed algorithms through numerical simulations and a persistent surveillance experiment.