Memory-Based In Situ Learning for Unmanned Vehicles
Abstract
Researchers are using a sensor-input-based metric to develop a team of robots that would have the capability to learn their roles and improve strategies so that they can meet their overall goals in dynamic unstructured environments such as underwater or urban settings in which communications and monitoring are difficult. For a robot to operate autonomously in a dynamic environment, it must be capable of adapting itself without the help of humans. The ultimate goal of our research is to provide teams of unmanned underwater vehicles (UUVs) some of the abilities of animals to adapt to their environment using their memories, without requiring exhaustive trial-and-error testing or complex modeling of the environment. We focus on UUVs because they offer the promise of making dangerous tasks such as searching for underwater hazards or surveying the ocean bottom more safe and economical for government and commercial operations. We adopt a team concept to reduce overall mission cost using several low-cost subordinate UUVs to augment the sensor capabilities of a higher-capability lead UUV. Our goal is to develop a team of robots that would have the capability to learn their roles and improve team strategies so that the team can meet its overall goals in dynamic unstructured environments such as underwater or urban settings in which communications and monitoring are difficult.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2006
- Accession Number
- ADA519469
Entities
People
- Brian S. Bourgeois
- Donald Sofge
- Patrick Mcdowell
- Sundaraja Sitharama Iyengar
Organizations
- Louisiana State University