A patrol of robot teams, where the robots are required to repeatedly visit a target area, is a useful tool in detecting an adversary trying to penetrate. In this work we examine the Closed Perimeter Patrol problem, in which the robots travel along a closed perimeter and the adversary is aware of the robots' patrol policy. The goal is to maximize the probability of penetration detection. Previous work dealt with symmetric tracks, in which all parts of the track have similar properties, and suggested non-deterministic patrol schemes, characterized by a uniform policy along the entire area. We consider more realistic scenarios of asymmetric tracks, with various parts of the track having different properties, and suggest a patrol policy with a non-uniform policy along different points of the track. We compare the achievements of both models and show the advantage of the non-uniform model. We further explore methods to efficiently calculate the attributes needed to maximize the probability of penetration detection and compare their implementation in various scenarios.
|Title of host publication||Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020|
|Editors||Bo An, Amal El Fallah Seghrouchni, Gita Sukthankar|
|Publisher||International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)|
|Number of pages||9|
|State||Published - 2020|
|Event||19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 - Virtual, Auckland, New Zealand|
Duration: 19 May 2020 → …
|Name||Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS|
|Conference||19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020|
|Period||19/05/20 → …|
Bibliographical noteFunding Information:
This work was partially supported by Ministry of Science and Technology, Israel and the Japan Science and Technology Agency (JST), Japan.
© 2020 International Foundation for Autonomous.
- Adversarial Patrol
- Multi-Robot Systems