Bruce Leonard Digney

Robotic concepts for urban operations

While land vehicles in open terrains is currently the primary military operation, it is expected that an increasing number of conflicts will occur in urban setting. Urban robots must operate under mobility, communication, perception and control conditions far more demanding than their open terrain counterparts. The Defense Research Establishment Suffield (DRES) is being tasked to develop robots, unmanned vehicles and supports system to aid the Canadian Forces in urban operations. In preparation for this role DRES personnel were invited to participate in operation Urban Ram, a large urban war game held on the grounds of CFB Griesbach in Edmonton. This paper presents the lessons learned at Urban Ram as to what roles robots could fulfill and the challenges of urban environments that must be overcome. Also presented will be robotic concepts inspired by Urban Ram, specifically discussed will be High Utility Robotics (HUR), which combines geometric shape shifting with function morphing to provide the general purpose, high mobility and broad application robots required for urban environments.

Adaptive representation for dynamic environment, vehicle, and mission complexity

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.

Perception and mobility research at Defence R&D Canada for UGVs in complex terrain

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.

Novel mobility platforms utilizing intelligent algorithms

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.

Perception for learned trafficability models

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.

Learned trafficability models

While clearly necessary, geometric information is not sufficient to insure successful navigation in outdoor environments. Many barriers to navigation cannot be represented in a three dimensional geometric model alone. Barriers such as soft ground, snow, mud, loose sand, compliant vegetation, debris hidden in vegetation and annoyances such as small ruts and washboard effects do not appear in geometric representations. The difficulty of offline specification and changing nature of terrain characteristics requires that solutions be capable of learning without prior information and able to adapt as environmental conditions change. This paper will discuss the ongoing and proposed work the Learned Trafficability Models (LTMs) program at the Defence Research Establishment Suffield (DRES) of the Canadian Department of National Defence.

The ALS project: lessons learned

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.

Little Dog learning of tractive and compressive terrain characteristics

In recent years research into legged locomotion across extreme terrains has increased. Much of this work was done under the DARPA Learning Legged Locomotion program that utilized a standard Little Dog robot platform and prepared terrain test boards with known geometric data. While path planing using geometric information is necessary, acquiring and utilizing tractive and compressive terrain characteristics is equally important. This paper describes methods and results for learning tractive and compressive terrain characteristics with the Little Dog robot. The estimation of terrain traction and compressive/support capabilities using the mechanisms and movements of the robot rather than dedicated instruments is the goal of this research. The resulting characteristics may differ from those of standard tests, however they will be directly usable to the locomotion controllers given that they are obtained in the physical context of the actual robot and its actual movements. This paper elaborates on the methods used and presents results. Future work will develop better suited probabilistic models and interwave these methods with other purposeful actions of the robot to lessen the need for direct terrain probing actions.

Defence R&D Canada's autonomous intelligent systems program

The Defence Research and Development Canadas (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.

Theseus: tethered distributed robotics (TDR)

The Defence Research and Development Canadas (DRDC) Autonomous Intelligent Systems program conducts research to increase the independence and effectiveness of military vehicles and systems. DRDC-Suffields Autonomous Land Systems (ALS) is creating new concept vehicles and autonomous control systems for use in outdoor areas, urban streets, urban interiors and urban subspaces. This paper will first give an overview of the ALS program and then give a specific description of the work being done for mobility in urban subspaces. Discussed will be the Theseus: Thethered Distributed Robotics (TDR) system, which will not only manage an unavoidable tether but exploit it for mobility and navigation. Also discussed will be the prototype robot called the Hedgehog, which uses conformal 3D mobility in ducts, sewer pipes, collapsed rubble voids and chimneys.