Intelligent mobility research for robotic locomotion in complex terrain

Abstract

The objective of the Autonomous Intelligent Systems Section of Defence R&D Canada - Suffield is best described by its mission statement, which is augment soldiers and combat systems by developing and demonstrating practical, cost effective, autonomous intelligent systems capable of completing military missions in complex operating environments. The mobility requirement for ground-based mobile systems operating in urban settings must increase significantly if robotic technology is to augment human efforts in these roles and environments. The intelligence required for autonomous systems to operate in complex environments demands advances in many fields of robotics. This has resulted in large bodies of research in areas of perception, world representation, and navigation, but the problem of locomotion in complex terrain has largely been ignored. In order to achieve its objective, the Autonomous Intelligent Systems Section is pursuing research that explores the use of intelligent mobility algorithms designed to improve robot mobility. Intelligent mobility uses sensing, control, and learning algorithms to extract measured variables from the world, control vehicle dynamics, and learn by experience. These algorithms seek to exploit available world representations of the environment and the inherent dexterity of the robot to allow the vehicle to interact with its surroundings and produce locomotion in complex terrain. The primary focus of the paper is to present the intelligent mobility research within the framework of the research methodology, plan and direction defined at Defence R&D Canada - Suffield. It discusses the progress and future direction of intelligent mobility research and presents the research tools, topics, and plans to address this critical research gap. This research will create effective intelligence to improve the mobility of ground-based mobile systems operating in urban settings to assist the Canadian Forces in their future urban operations.