This work focuses on the autonomous exploration of unknown areas by a swarm of mobile robots, referred to as agents. When an emergency happens within a building, it is dangerous to send human responders to search the area for hazards and victims. This motivates the need for autonomous agents that are able to coordinate with each other to explore the area as fast as possible. We investigate this problem from an algorithmic, rather than a robotics point of view, and thus abstract away from practical problems, such as obstacle detection and navigation over rough terrain. Our focus is on distributed algorithms that can cope with the following challenges: the topology of the area is typically unknown, communication between agents is intermittent and unreliable, and agents are not aware of their location in indoor environments. In order to address these challenges, we adopt the stigmergy approach, that is, we assume that the area is instrumented with small inexpensive sensors (called tags) and agents coordinate indirectly with each other by reading and updating the state of local tags. We propose three novel distributed algorithms that allow agents to explore unknown areas by coordinating indirectly through a tag-instrumented environment. In addition, we propose two mechanisms for discovering evacuation routes from critical points in the area to emergency exits. Agents are able to combine the tasks of area exploration and evacuation route discovery in a seamless manner. We study the proposed algorithms analytically, and evaluate them empirically in a custom-built simulation environment in a variety of scenarios. We then build a real testbed of agents and tags, and investigate practical mechanisms that allow agents to detect and localise nearby tags, and navigate toward them. Using the real testbed, we derive realistic models of detection, localisation and navigation errors, and investigate how they impact the performance of the proposed exploration algorithms. Finally, we design fault-tolerant exploration algorithms that are robust to these errors and evaluate them extensively in a simulation environment.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:531814 |
| Date | January 2010 |
| Creators | Ferranti, Ettore |
| Contributors | Trigoni, Niki |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:80f73481-1370-498e-ab43-d86f9af11a62 |
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