| Type of Document |
Master's Thesis |
| Author |
Van Covern, David Burns
|
| URN |
etd-12052007-104008 |
| Title |
Collaborative Tarrget Localization and Inspection Using a Heterogeneous Team of Autonomous Vehicles |
| Degree |
Master of Engineering |
| Department |
Mechanical Engineering |
| Advisory Committee |
| Advisor Name |
Title |
| Reinholtz, Charles F. |
Committee Chair |
| Wicks, Alfred L. |
Committee Co-Chair |
| Woolsey, Craig A. |
Committee Member |
|
| Keywords |
- Autonomous Vehicles
- Air-Ground Team
- Unmanned
|
| Date of Defense |
2007-12-04 |
| Availability |
unrestricted |
Abstract
Autonomous vehicle development is a rapidly growing field that has vast possibilities for both military and commercial applications. Removing people from dangerous tasks will save lives. Continued research is necessary in order to build these new technologies and mature those already established. One area of potential in the unmanned vehicle community is that of fully autonomous cooperation. This area of research will allow multiple unmanned platforms to perform new functions on a larger scale by combining their capabilities in a coordinated manner.
This thesis addresses the emerging need of research related to fully autonomous cooperation between a heterogeneous team of vehicles, by taking a system level approach and integrating the necessary technologies. Software was developed and then tested that combines an unmanned ground vehicle and an unmanned aerial vehicle in order to perform a task that utilizes the strengths of each platform. The ground vehicle is programmed with a route for which it sends look-ahead waypoints to the aircraft. As it traverses the route, the aircraft searches for possible targets. If a target is detected, the approximate coordinates are sent over the network and the ground vehicle then further localizes and inspects the target. Once the inspection is completed, the ground vehicle continues on its previous route. This thesis demonstrates that pairing ground and aerial vehicles in a fully autonomous target localization problem can indeed provide a team functioning more efficiently than either alone.
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