A simple, quantitative measure for encapsulating the autonomous capabilities of unmanned systems (UMS) has
yet to be established. Current models for measuring a UMS’s autonomy level require extensive, operational
level testing, and provide a means for assessing the autonomy level for a specific mission/task and operational
environment. A more elegant technique for quantifying autonomy using component level testing of the robot
platform alone, outside of mission and environment contexts, is desirable. Using a high level framework for
UMS architectures, such a model for determining a level of autonomy has been developed. The model uses a
combination of developmental and component level testing for each aspect of the UMS architecture to define a
non-contextual autonomous potential (NCAP). The NCAP provides an autonomy level, ranging from fully non-
autonomous to fully autonomous, in the form of a single numeric parameter describing the UMS’s performance
capabilities when operating at that level of autonomy.
Dismounted soldiers are clearly at the centre of modern asymmetric conflicts and unmanned systems of the future will
play important roles in their support. Moreover, the nature of modern asymmetric conflicts requires dismounted soldiers
to operate in urban environments with challenges of communication and limited situational awareness. To improve the
situational awareness of dismounted soldiers in complex urban environments, Defence R&D Canada - Suffield (DRDC
Suffield) envision Unmanned Air Vehicles (UAV) rotorcraft and Unmanned Ground Vehicles (UGV) cooperating in the
battlespace. The capabilities provided to the UAV rotorcraft will include high speed maneuvers through urban terrain, overthe-
horizon and loss of communications operations, and/or low altitude over-watch of dismounted units. This information
is shared with both the dismounted soldiers and UGV. The man-sized, man-mobile UGV operates in close support to
dismounted soldiers to provide a payload carrying capacity. Some of the possible payloads include chemical, biological,
radiological and nuclear (CBRN) detection, intelligence, surveillance and reconnaissance (ISR), weapons, supplies, etc..
These unmanned systems are intended to increase situational awareness in urban environments and can be used to call
upon nearby forces to react swiftly by providing acquired information to concentrate impact where required.
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.
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.
Proc. SPIE. 5422, Unmanned Ground Vehicle Technology VI
KEYWORDS: Defense and security, Unmanned aerial vehicles, Sensors, Robotics, Control systems, Artificial intelligence, Intelligence systems, Algorithm development, Systems modeling, Decision support systems
The Defence Research and Development Canada's (DRDC has been given strategic direction to pursue research to increase the independence and effectiveness of military vehicles and systems. This has led to the creation of the Autonomous Intelligent Systems (AIS) prgram and is notionally divide into air, land and marine vehicle systems as well as command, control and decision support systems. This paper presents an overarching description of AIS research issues, challenges and directions as well as a nominal path that vehicle intelligence will take. The AIS program requires a very close coordination between research and implementation on real vehicles. This paper briefly discusses the symbiotic relationship between intelligence algorithms and implementation mechanisms. Also presented are representative work from two vehicle specific research program programs. Work from the Autonomous Air Systems program discusses the development of effective cooperate control for multiple air vehicle. The Autonomous Land Systems program discusses its developments in platform and ground vehicle intelligence.
The mobility requirement for Unmanned Ground Vehicles (UGVs) is expected to increase significantly as the number of conflicts shift from open terrain operations to the increased complexity of urban settings. In preparation for this role Defence R&D Canada-Suffield is exploring novel mobility platforms utilizing intelligent mobility algorithms that will each contribute to improved UGV mobility. The design of a mobility platform significantly influences its ability to maneuver in the world. Highly configurable and mobile platforms are typically best suited for unstructured terrain. Intelligent mobility algorithms seek to exploit the inherent dexterity of the platform and available world representation of the environment to allow the vehicle to engage extremely irregular and cluttered environments. As a result, the capabilities of vehicles designed with novel platforms utilizing intelligent mobility algorithms will outperform larger vehicles without these capabilities. However, there exist many challenges in the development of UGV systems to satisfy the increased mobility requirement for future military operations. This paper discusses a research methodology proposed to overcome these challenges, which primarily involves the definition and development of novel mobility platforms for intelligent mobility research. It addresses intelligent mobility algorithms and the incorporation of world representation and perception research in the creation of necessary synergistic systems. In addition, it presents an overview of the novel mobility platforms and research activities at Defence R&D Canada-Suffield aimed at advancing UGV mobility capabilities in difficult and relevant military environments.
In order for an Unmanned Ground Vehicle (UGV) to operate effectively it must be able to perceive its environment in an accurate, robust and effective manner. This is done by creating a world representation which encompasses all the perceptual information necessary for the UGV to understand its surroundings. These perceptual needs are a function of the robots mobility characteristics, the complexity of the environment in which it operates, and the mission with which the UGV has been tasked. Most perceptual systems are designed with predefined vehicle, environmental, and mission complexity in mind. This can lead the robot to fail when it encounters a situation which it was not designed for since its internal representation is insufficient for effective navigation. This paper presents a research framework currently being investigated by Defence R&D Canada (DRDC), which will ultimately relieve robotic vehicles of this problem by allowing the UGV to recognize representational deficiencies, and change its perceptual strategy to alleviate these deficiencies. This will allow the UGV to move in and out of a wide variety of environments, such as outdoor rural to indoor urban, at run time without reprogramming. We present sensor and perception work currently being done and outline our research in this area for the future.