The use of autonomous robots for surveillance is one of the most interesting applications of graph-patrolling algorithms. In recent years, considerable effort has been devoted to tackling the problem of efficiently computing effective patrolling strategies. One of the mainstream approaches is adversarial patrolling, where a model of a strategic attacker is explicitly taken into account. A common assumption made by these techniques is to consider a worst-case attacker, characterized by ubiquitous and perfect observation capabilities. Motivated by the domain of robotic applications, we instead consider a more realistic and limited attacker model capable of gathering noisy observations in a locally limited range of the environment. We assume that the modeled attacker follows a behavior induced by its observations. Thus, we devise a randomized patrolling strategy based on Markov chains that makes observations reveal very little information, while still maintaining a reasonable level of protection in the environment. Our experimental results obtained in simulation confirm time-variance as a practical approach for our objective.
Time-Varying Graph Patrolling Against Attackers with Locally Limited and Imperfect Observation Models / C.D. Alvarenga, N. Basilico, S. Carpin (PROCEEDINGS OF THE ... IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS). - In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)[s.l] : IEEE, 2019. - ISBN 978-1-7281-4003-2. - pp. 4869-4876 (( convegno IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS tenutosi a Macau nel 2019 [10.1109/IROS40897.2019.8967770].
Time-Varying Graph Patrolling Against Attackers with Locally Limited and Imperfect Observation Models
N. Basilico;
2019
Abstract
The use of autonomous robots for surveillance is one of the most interesting applications of graph-patrolling algorithms. In recent years, considerable effort has been devoted to tackling the problem of efficiently computing effective patrolling strategies. One of the mainstream approaches is adversarial patrolling, where a model of a strategic attacker is explicitly taken into account. A common assumption made by these techniques is to consider a worst-case attacker, characterized by ubiquitous and perfect observation capabilities. Motivated by the domain of robotic applications, we instead consider a more realistic and limited attacker model capable of gathering noisy observations in a locally limited range of the environment. We assume that the modeled attacker follows a behavior induced by its observations. Thus, we devise a randomized patrolling strategy based on Markov chains that makes observations reveal very little information, while still maintaining a reasonable level of protection in the environment. Our experimental results obtained in simulation confirm time-variance as a practical approach for our objective.File | Dimensione | Formato | |
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