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Leader-follower strategies for robotic patrolling in environments with arbitrary topologies
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International Conference on Autonomous Agents archive
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1 table of contents
Budapest, Hungary
SESSION: Multi-robotics table of contents
Pages 57-64  
Year of Publication: 2009
ISBN:978-0-9817381-6-1
Authors
Nicola Basilico  Politecnico di Milano, Milano, Italy
Nicola Gatti  Politecnico di Milano, Milano, Italy
Francesco Amigoni  Politecnico di Milano, Milano, Italy
Sponsors
: The Foundation for Intelligent Physical Agents
Microsoft Research : Microsoft Research
: Wiley - Blackwell Ltd
: Whitestein Technologies
: European Office of Aerospace Research and Development, Air Force Office of Scientific Research, United States Air Force Research Laboratory
: Drexel University
Publisher
Bibliometrics
Downloads (6 Weeks): 19,   Downloads (12 Months): 52,   Citation Count: 0
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ABSTRACT

Game theoretic approaches to patrolling have become a topic of increasing interest in the very last years. They mainly refer to a patrolling mobile robot that preserves an environment from intrusions. These approaches allow for the development of patrolling strategies that consider the possible actions of the intruder in deciding where the robot should move. Usually, it is supposed that the intruder can hide and observe the actions of the patroller before intervening. This leads to the adoption of a leader-follower solution concept. In this paper, mostly theoretical in its nature, we propose an approach to determine optimal leader-follower strategies for a mobile robot patrolling an environment. Differently from previous works in literature, our approach can be applied to environments with arbitrary topologies.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Nicola Basilico: colleagues
Nicola Gatti: colleagues
Francesco Amigoni: colleagues