Control and learning for intelligent mobility of unmanned ground vehicles in complex terrains

M. Trentini; B. Beckman; B. Digney

doi:10.1117/12.606483

27 May 2005 Control and learning for intelligent mobility of unmanned ground vehicles in complex terrains

M. Trentini, B. Beckman, B. Digney

Proceedings Volume 5804, Unmanned Ground Vehicle Technology VII; (2005) https://doi.org/10.1117/12.606483
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

The Autonomous Intelligent Systems program at Defence R&D Canada-Suffield envisions autonomous systems contributing to decisive operations in the urban battle space. Creating effective intelligence for these systems demands advances in perception, world representation, navigation, and learning. In the land environment, these scientific areas have garnered much attention, while largely ignoring the problem of locomotion in complex terrain. This is a gap in robotics research, where sophisticated algorithms are needed to coordinate and control robotic locomotion in unknown, highly complex environments. Unlike traditional control problems, intuitive and systematic control tools for robotic locomotion do not readily exist thus limiting their practical application. This paper addresses the mobility problem for unmanned ground vehicles, defined here as the autonomous maneuverability of unmanned ground vehicles in unknown, highly complex environments. It discusses the progress and future direction of intelligent mobility research at Defence R&D Canada-Suffield and presents the research tools, topics and plans to address this critical research gap.

Citation Download Citation

M. Trentini, B. Beckman, and B. Digney "Control and learning for intelligent mobility of unmanned ground vehicles in complex terrains", Proc. SPIE 5804, Unmanned Ground Vehicle Technology VII, (27 May 2005); https://doi.org/10.1117/12.606483

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