Multi-Agent Autonomous Surveillance: A Framework Based on Stochastic Reachability and Hierarchical Task Allocation
| Autor: | John Lygeros, Nikolaos Kariotoglou, Davide M. Raimondo, Sean Summers |
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| Rok vydání: | 2014 |
| Předmět: |
Computer science
business.industry Mechanical Engineering Distributed computing Mobile robot Machine learning computer.software_genre Target acquisition Computer Science Applications Task (project management) Dynamic programming Control and Systems Engineering Reachability State space Markov decision process Artificial intelligence business Instrumentation computer Information Systems Curse of dimensionality |
| Zdroj: | Journal of Dynamic Systems, Measurement, and Control. 137 |
| ISSN: | 1528-9028 0022-0434 |
| DOI: | 10.1115/1.4028589 |
| Popis: | We develop and implement a framework to address autonomous surveillance problems with a collection of pan-tilt (PT) cameras. Using tools from stochastic reachability with random sets, we formulate the problems of target acquisition, target tracking, and acquisition while tracking as reach-avoid dynamic programs for Markov decision processes (MDPs). It is well known that solution methods for MDP problems based on dynamic programming (DP), implemented by state space gridding, suffer from the curse of dimensionality. This becomes a major limitation when one considers a network of PT cameras. To deal with larger problems we propose a hierarchical task allocation mechanism that allows cameras to calculate reach-avoid objectives independently while achieving tasks collectively. We evaluate the proposed algorithms experimentally on a setup involving industrial PT cameras and mobile robots as targets. |
| Databáze: | OpenAIRE |
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