Unmanned ground vehicles (UGV) operating in outdoor environments must traverse unstructured terrain. This
terrain is diverse in nature and contains natural obstacles such as rocks, brushes, berms, and low lying wet areas.
Outdoor terrain is not static as it varies on a seasonal basis due to the life cycle associated with natural vegetation.
Additionally, outdoor terrain may change appearance due to variations in lighting conditions that result from the
Sun's relative position and from weather conditions such as clouds, fog or rain. This environmental diversity has
long caused researchers considerable grief, as developing a classical terrain classification algorithm has proven to
be a very difficult if not an impossible task. Researchers have skirted this problem by relying upon ranging sensors
and constructing 2 ½D or, more recently, 3D world representations. Although geometrical representations have
been used extensively, the low data rates associated with laser rangefinders, the unreliability of stereo vision, and
the interaction between geometry and orientation estimation errors have limited the lookahead distance, thereby
reducing the maximum attainable vehicle speeds. Learning from experience, in a more human like manner,
promises to reduce or alleviate many of the issues posed by unstructured outdoor terrain. Defence R&D Canada
(DRDC) "Learned Trafficability" program researches learning from experience. The paper presents DRDC's
progress in extending a 2 ½D world representation using vision and learning from experience.
Unmanned vehicles (UxV) operate in numerous environments, with air, ground and marine representing the
majority of the implementations. All unmanned vehicles, when traversing unknown space, have similar requirements.
They must sense their environment, create a world representation, and, then plan a path that safely
avoids obstacles and hazards. Traditionally, each unmanned vehicle class used environment specific assumptions
to create a unique world representation that was tailored to it operating environment. Thus, an unmanned aerial
vehicle (UAV) used the simplest possible world representation, where all space above the ground plane was free
of obstacles. Conversely, an unmanned ground vehicle (UGV) required a world representation that was suitable
to its complex and unstructured environment.
Such a clear cut differentiation between UAV and UGV environments is no longer valid as UAVs have migrated
down to elevations where terrestrial structures are located. Thus, the operating environment for a low flying
UAV contains similarities to the environments experienced by UGVs. As a result, the world representation
techniques and algorithms developed for UGVs are now applicable to UAVs, since low flying UAVs must sense
and represent its world in order to avoid obstacles.
Defence R&D Canada (DRDC) conducts research and development in both the UGV and UAV fields. Researchers
have developed a platform neutral world representation, based upon a uniform 2<sup>1</sup>/<sub>2</sub>-D elevation grid,
that is applicable to many UxV classes, including aerial and ground vehicles. This paper describes DRDC's
generic world representation, known as the Global Terrain map, and provides an example of unmanned ground
vehicle implementation, along with details of it applicability to aerial vehicles.
Unmanned systems simultaneously reduce risk and magnify the impact of soldier-operators. For example, in
Afghanistan UAVs routinely provide overwatch to manned units while UGVs support IED identification and
disposal roles. Expanding these roles requires greater autonomy with a coherent unmanned "system of systems"
approach that leverages one platform's strengths against the weakness of another. Specific collaborative
unmanned systems such as shared sensing, communication relay, and distributed computing to achieve greater
autonomy are often presented as possible solutions. By surveying currently deployed systems, this paper shows
that the spectrum of air and ground systems provide an important mixture of range, speed, payload, and endurance
with significant implications on mission structure. Rather than proposing UxV teams collaborating
towards specific autonomous capabilities, this paper proposes that basic physical and environmental constraints
will drive tactics towards a layered, unmanned battlespace that provides force protection and reconnaissance in
depth to a manned core.
In support of Canadian Forces transformation, Defence R&D Canada (DRDC) has established an ongoing program to develop machine intelligence for semi-autonomous vehicles and systems. Focussing on mine clearance and remote scouting for over a decade, DRDC Suffield has developed numerous UGVs controlled remotely over point-to-point radio links. Though this strategy removes personnel from potential danger, DRDC recognized that human factors and communications bandwidth limit teleoperation and that only networked, autonomous unmanned systems can conserve these valuable resources. This paper describes the outcome of the first autonomy project, Autonomous Land Systems (ALS), designed to demonstrate basic autonomous multivehicle land capabilities.
In 2002 Defence R&D Canada changed research direction from pure tele-operated land vehicles to general autonomy for land, air, and sea craft. The unique constraints of the military environment coupled with the complexity of autonomous systems drove DRDC to carefully plan a research and development infrastructure that would provide state of the art tools without restricting research scope. DRDC's long term objectives for its autonomy program address disparate unmanned ground vehicle (UGV), unattended ground sensor (UGS), air (UAV), and subsea and surface (UUV and USV) vehicles operating together with minimal human oversight. Individually, these systems will range in complexity from simple reconnaissance mini-UAVs streaming video to sophisticated autonomous combat UGVs exploiting embedded and remote sensing. Together, these systems can provide low risk, long endurance, battlefield services assuming they can communicate and cooperate with manned and unmanned systems. A key enabling technology for this new research is a software architecture capable of meeting both DRDC's current and future requirements. DRDC built upon recent advances in the computing science field while developing its software architecture know as the Architecture for Autonomy (AFA). Although a well established practice in computing science, frameworks have only recently entered common use by unmanned vehicles. For industry and government, the complexity, cost, and time to re-implement stable systems often exceeds the perceived benefits of adopting a modern software infrastructure. Thus, most persevere with legacy software, adapting and modifying software when and wherever possible or necessary -- adopting strategic software frameworks only when no justifiable legacy exists. Conversely, academic programs with short one or two year projects frequently exploit strategic software frameworks but with little enduring impact. The open-source movement radically changes this picture. Academic frameworks, open to public scrutiny and modification, now rival commercial frameworks in both quality and economic impact. Further, industry now realizes that open source frameworks can reduce cost and risk of systems engineering. This paper describes the Architecture for Autonomy implemented by DRDC and how this architecture meets DRDC's current needs. It also presents an argument for why this architecture should also satisfy DRDC's future requirements as well.
Modern unmanned vehicles (UV) are complex systems. The current generation of UVs have extensive capabilities allowing the UV to sense its environment, create an internal representation of the environment, navigate within this environment by commanding movement and accomplish this in real-time. This proliferation of UV capabilities has resulted in large and complex software systems that are often distributed across multiple processors. Such systems have a reputation for convoluted implementations that result in software that is difficult to understand, expand, debug and repair. In order for a UV to operate successfully this issue of complex distributed software systems must be mastered. The computing science field views a modular, component based design as the best approach for implementing complex distributed software systems. Methodologies and toolkits such as frameworks and middleware have been developed to enable and simplify the implementation of distributed software systems. DRDC and other research institutions are developing UVs frameworks using CORBA middleware. A CORBA interface enables location transparency, thus it does not matter whether the component is locally or remotely located. The UV autonomy framework developed at DRDC is based upon the Miro framework which was originally developed for soccer playing robots. The Miro framework implements many key features and methods required by autonomous systems and Miro's basis in CORBA allows it to be easily modified and extended to support the unique requirements of military UVs. DRDC has modified and extended Miro so that it now supports autonomous unmanned ground vehicles. The process of implementing these changes substantiated the advantages of frameworks and middleware since Miro proved to be highly flexible and easy to extend.
For unmanned combat forces, some research proposes multi-robot coordination through common analytical coordination algorithms using reliable, high bandwidth communications. Such coordination is capable of optimal or near-optimal distribution of unmanned forces, but requires reliable communications and frequent feedback and control to ensure predictable performance. Others propose local autonomy, reducing dependence on reliable communications through greater intelligence within each unmanned system, but at the cost of optimality, predictability, and dependency on rich, high rate sensing. Thus a fundamental problem of swarm control can be starkly drawn. If centralized control is not practical and the swarm must function at a level comparable to manned forces, swarm members must adhere to common goal direction semantics that permits each unit to dissect its contribution to the team objective with or without consultation and negotiation. How, then should missions be expressed, allocated, and monitored?
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.
Unmanned ground vehicles (UGV), traversing open terrain, require the capability of identifying non-geometric barriers or impediments to navigation, such as soft soil, fine sand, mud, snow, compliant vegetation, washboard, and ruts. Given the ever changing nature of these terrain characteristics, for an UVG to be able to consistently navigate such barriers, it must have the ability to learn from and to adapt to changes in these environmental conditions. As part of ongoing research co-operation with the Defense Research Establishment Suffield (DRES), Scientific Instrumentation Ltd. (SIL) has developed a Terrain Simulator that allows for the investigation of terrain perception and of learning techniques.