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Balancing Priorities in Patrolling with Rabbit Walks (2312.16564v1)

Published 27 Dec 2023 in cs.RO and cs.MA

Abstract: In an environment with certain locations of higher priority, it is required to patrol these locations as frequently as possible due to their importance. However, the Non-Priority locations are often neglected during the task. It is necessary to balance the patrols on both kinds of sites to avoid breaches in security. We present a distributed online algorithm that assigns the routes to agents that ensures a finite time visit to the Non-Priority locations along with Priority Patrolling. The proposed algorithm generates offline patrol routes (Rabbit Walks) with three segments (Hops) to explore non-priority locations. The generated number of offline walks depends exponentially on a parameter introduced in the proposed algorithm, thereby facilitating the scalable implementation based on the onboard resources available on each patrolling robot. A systematic performance evaluation through simulations and experimental results validates the proportionately balanced visits and suggests the proposed algorithm's versatile applicability in the implementation of deterministic and non-deterministic scenarios.

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References (33)
  1. On optimal cooperative patrolling. In 49th IEEE Conference on Decision and Control (CDC), pages 7153–7158, Atlanta, GA, USA, December 2010. IEEE.
  2. Oscar Morales-Ponce. Optimal patrolling of high priority segments while visiting the unit interval with a set of mobile robots. Theoretical Computer Science, 911:1–18, April 2022.
  3. MSP algorithm: multi-robot patrolling based on territory allocation using balanced graph partitioning. In Proceedings of the 2010 ACM Symposium on Applied Computing, SAC ’10, pages 1271–1276, New York, NY, USA, March 2010. Association for Computing Machinery.
  4. A multi-robot deep Q-learning framework for priority-based sanitization of railway stations. Applied Intelligence, 53(17):20595–20613, September 2023.
  5. Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2563–2570, September 2021. ISSN: 2153-0866.
  6. On the Power and Limitations of Deception in Multi-Robot Adversarial Patrolling. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pages 430–436, Melbourne, Australia, August 2017. International Joint Conferences on Artificial Intelligence Organization.
  7. Cooperative patrol planning of multi-robot systems by a competitive auction system. In 2009 ICCAS-SICE, pages 4359–4363, August 2009.
  8. Impact of CCTV surveillance on Crime. Center of Criminal Justice Research, 2020.
  9. The Philadelphia foot Patrol Experiment: A Ramdomized controlled Trial of Police Patrol Effiectiveness in Violent Crime Hotspots. Criminology, 49(3):795–831, August 2011.
  10. Robots in Inspection and Monitoring of Buildings and Infrastructure: A Systematic Review. Applied Sciences, 13(4):2304, January 2023.
  11. Research and Markets ltd. Inspection Robots - Global Strategic Business Report, 2023.
  12. Recent Advances on Multi-agent Patrolling. In Advances in Artificial Intelligence – SBIA 2004, Lecture Notes in Computer Science, pages 474–483, Berlin, Heidelberg, 2004. Springer.
  13. A Survey on Multi-robot Patrolling Algorithms. In Luis M. Camarinha-Matos, editor, Technological Innovation for Sustainability, IFIP Advances in Information and Communication Technology, pages 139–146, Berlin, Heidelberg, 2011. Springer.
  14. A survey of multi-robot regular and adversarial patrolling. IEEE/CAA Journal of Automatica Sinica, 6(4):894–903, July 2019.
  15. Nicola Basilico. Recent Trends in Robotic Patrolling. Current Robotics Reports, 3(2):65–76, June 2022.
  16. Performance based task assignment in multi-robot patrolling. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC ’13, pages 70–76, New York, NY, USA, March 2013. Association for Computing Machinery.
  17. Distributed on-line dynamic task assignment for multi-robot patrolling. Autonomous Robots, 41(6):1321–1345, August 2017.
  18. Xu Chen. Fast Patrol Route Planning in Dynamic Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 42(4):894–904, July 2012.
  19. Frequency-Based Multi-agent Patrolling Model and Its Area Partitioning Solution Method for Balanced Workload. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research, volume 10848, pages 530–545, Cham, 2018. Springer International Publishing.
  20. Multi-agent Patrolling: An Empirical Analysis of Alternative Architectures. In Multi-Agent-Based Simulation II, Lecture Notes in Computer Science, pages 155–170, Berlin, Heidelberg, 2003. Springer.
  21. Approximation Algorithms for Multi-Robot Patrol-Scheduling with Min-Max Latency. In Springer Proceedings in Advanced Robotics, volume 17, pages 107–123. Springer International Publishing, 2021.
  22. A two-step evolutionary and ACO approach for solving the multi-agent patrolling problem. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pages 861–868, June 2008. ISSN: 1941-0026.
  23. Alessandro De Luna Almeida. Combining Idleness and Distance to Design Heuristic Agents for the Patrolling Task. pages 33–40, 2003.
  24. Multi-agent patrolling with reinforcement learning. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004., pages 1122–1129, July 2004.
  25. Graph Attention Networks. International Conference on Learning Representations, February 2018. arXiv:1710.10903 [cs, stat].
  26. Cooperative multi-robot patrol with Bayesian learning. Autonomous Robots, 40(5):929–953, June 2016.
  27. Applying Bayesian learning to multi-robot patrol. In 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages 1–6, October 2013. ISSN: 2374-3247.
  28. Persistent monitoring in discrete environments: Minimizing the maximum weighted latency between observations. The International Journal of Robotics Research, 33(1):138–154, January 2014.
  29. Priority Patrolling using Multiple Agents. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 8692–8698, Xi’an, China, May 2021. IEEE.
  30. Microscopic Traffic Simulation using SUMO. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2575–2582, Maui, HI, November 2018. IEEE.
  31. The Hybrid Reciprocal Velocity Obstacle. IEEE Transactions on Robotics, 27(4):696–706, August 2011. Conference Name: IEEE Transactions on Robotics.
  32. Markus Achtelik. Vicon Bridge, September 2023. original-date: 2013-08-06T09:17:01Z.
  33. Min-Max Latency Walks: Approximation Algorithms for Monitoring Vertex-Weighted Graphs. In Algorithmic Foundations of Robotics X, Springer Tracts in Advanced Robotics, pages 139–155, Berlin, Heidelberg, 2013. Springer.
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