Research in mobile robot navigation has demonstrated some success in navigating flat indoor environments while
avoiding obstacles. However, the challenge of analyzing complex environments to climb obstacles autonomously
has had very little success due to the complexity of the task. Unmanned ground vehicles currently exhibit
simple autonomous behaviours compared to the human ability to move in the world. This paper presents the
control algorithms designed for a tracked mobile robot to autonomously climb obstacles by varying its tracks
configuration. Two control algorithms are proposed to solve the autonomous locomotion problem for climbing
obstacles. First, a reactive controller evaluates the appropriate geometric configuration based on terrain and
vehicle geometric considerations. Then, a reinforcement learning algorithm finds alternative solutions when the
reactive controller gets stuck while climbing an obstacle. The methodology combines reactivity to learning.
The controllers have been demonstrated in box and stair climbing simulations. The experiments illustrate the
effectiveness of the proposed approach for crossing obstacles.
The Autonomous Intelligent Systems Section at Defence R&D Canada - Suffield envisions autonomous systems
contributing to decisive operations in the urban battle space. In this vision, teams of unmanned ground, air, and
marine vehicles, and unattended ground sensors will gather and coordinate information, formulate plans, and
complete tasks. The mobility requirement for ground-based mobile systems operating in urban settings must
increase significantly if robotic technology is to augment human efforts in military relevant roles and environments.
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 and perception, 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. However, a disconnect exists between the current state-of-the-art
in perception systems and the information required for novel platforms to interact with their environment to
improve mobility in complex terrain. The primary focus of the paper is to present the research tools, topics, and
plans to address this gap in perception and control research. 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.
The objective of the Autonomous Intelligent Systems Section of Defence R&D Canada - Suffield is best described
by its mission statement, which is "to 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.
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