One of the applications where the use of robots can be beneficial is Wilderness Search and Rescue (WiSAR), which involves the search for a possibly mobile but non-trackable lost person (i.e., the target) in wilderness environments. A mobile target implies that the search area grows continuously and potentially without bound. This fact, combined with the presence of typically rugged, varying terrain and the possibility of inclement weather, poses a considerable challenge to human Search and Rescue (SAR) personnel with respect to the time and effort required to perform the search and the danger entailed to the searchers. Mobile robots can be advantageous in WiSAR due to their ability to provide consistent performance without getting tired and their lower susceptibility to harsh weather conditions compared to humans. Thus, a coordinated team of robots that can assist human SAR personnel by autonomously performing searches in WiSAR scenarios would be of great value. However, to date, a suitable multi-robot coordination methodology for autonomous search that can satisfactorily address the issues relevant to WiSAR is lacking.
The objective of this Dissertation is, thus, to develop a methodology that can autonomously coordinate the search strategy of a multi-robot team in wilderness environments to locate a moving target that is neither continuously nor intermittently observed during the search process. Three issues in particular are addressed: (i) target-location prediction, (ii) robot deployment, and (iii) robot-path planning. The corresponding solution approaches devised to address these issues incorporate the influence of varying terrain that may contain a priori known and unknown obstacles, and deal with unique target physiology and psychology as well as found clues left behind by the target. The solution methods for these three tasks work seamlessly together resulting in a tractable MRC methodology for autonomous robotic WiSAR.
Comprehensive simulations have been performed that validate the overall proposed methodology. Moreover, the tangible benefits provided by this methodology were further revealed through its comparison with an alternative search method.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/43655 |
| Date | 13 January 2014 |
| Creators | Macwan, Ashish |
| Contributors | Benhabib, Beno, Nejat, Goldie |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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