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.
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.
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.
Unmanned vehicle systems is an attractive technology for the military, but whose promises have remained
largely undelivered. There currently exist fielded remote controlled UGVs and high altitude
UAV whose benefits are based on standoff in low complexity environments with sufficiently low control
reaction time requirements to allow for teleoperation. While effective within there limited operational
niche such systems do not meet with the vision of future military UxV scenarios. Such scenarios envision
unmanned vehicles operating effectively in complex environments and situations with high levels of independence
and effective coordination with other machines and humans pursing high level, changing and
sometimes conflicting goals. While these aims are clearly ambitious they do provide necessary targets
and inspiration with hopes of fielding near term useful semi-autonomous unmanned systems. Autonomy
involves many fields of research including machine vision, artificial intelligence, control theory, machine
learning and distributed systems all of which are intertwined and have goals of creating more versatile
broadly applicable algorithms. Cohort is a major Applied Research Program (ARP) led by Defence R&D
Canada (DRDC) Suffield and its aim is to develop coordinated teams of unmanned vehicles (UxVs) for
urban environments. This paper will discuss the critical science being addressed by DRDC developing
semi-autonomous systems.
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.
The Defence R&D Canada (DRDC) has been given strategic direction to pursue research to increase the independence and effectiveness of unmanned military vehicles and systems. This led to the creation of the Autonomous Land Systems(ALS) project that was completed in 2005 with a successful demonstration of semi-autonomous UGVs in open partially vegetated environments. Cohort is a newly funded project that will work to develop effective UxV teams for urban and complex environments. This paper will briefly discuss the state of UGV research at the completion of ALS and other research projects supporting Cohort. The goals and challenges of Cohort will be outlined as well as the research plan that involves many of DRDC's laboratories from across Canada.
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 Defence R&D Canada (DRDC) has been given strategic direction to pursue research to increase the independence and effectiveness of military vehicles and systems. This led to the creation of the Autonomous Land Systems (ALS) project that was completed in 2005 with a successful demonstration of semi-autonomous UGVs in open partially vegetated environments. Cohort is a newly funded project that will work to devleop effective UxV teams for urban and complex environments. This paper will briefly discuss the state of the UGV research at the completion of ALS and other research projects supporting Cohort. The goals and challenges of Cohort will be outlined as well as the research plan that is involving many of DRDC's laboratories from across Canada.
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.
The Defence R&D Canada (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) program which is notionally divided into air, land and marine vehicles. This paper presents an overarching description of DRDC's concluding, starting and pending future programs in vehicle intelligence and autonomy.
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.
KEYWORDS: Unmanned aerial vehicles, Intelligence systems, Control systems, Sensors, Defense and security, Artificial intelligence, Algorithm development, Robotics, 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.
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.
KEYWORDS: Algorithm development, Robotics, Defense and security, Vehicle control, Sensors, Unmanned ground vehicles, Actuators, Modeling, Chemical elements, Control systems
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.
The Defence Research and Development Canada's (DRDC) Autonomous Intelligent System's program conducts research to increase the independence and effectiveness of military vehicles and systems. DRDC-Suffield's 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.
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.
KEYWORDS: Sensors, Line width roughness, Data fusion, Databases, Cameras, Sensor fusion, Data modeling, Neural networks, Image segmentation, Principal component analysis
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.
KEYWORDS: 3D modeling, Sensors, Vegetation, Data modeling, Image sensors, Image segmentation, Navigation systems, 3D image processing, Line width roughness, Defense and security
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.
KEYWORDS: Robots, Data modeling, Robotics, Line width roughness, Control systems, Information operations, Data processing, Sensors, Spatial resolution, Buildings
It is accepted that the ability to learn and adapt is key to prosperity and survival in both individuals and societies. The same is true of populations of robots. Those robots within a population that are able to learn will outperform, survive longer and perhaps exploit their non-learning co- workers. This paper describes the ongoing results of Communal Learning in the Cognitive Colonies Project (CMU/Robotics and DRES), funded jointly by DARPA ITO- Software for Distributed Robotics and DRDC-DRES. Discussed will be how communal learning fits into the free market architecture for distributed control. Techniques for representing experiences, learned behaviors, maps and computational resources as commodities within the market economy will be presented. Once in a commodity structure, the cycle of speculate, act, receive profits or sustain losses and then learn of the market economy. This allows successful control strategies to emerge and the individuals who discovered them to become established as successful. This paper will discuss: learning to predict costs and make better deals, learning transition confidences, learning causes of death, learning with robot sacrifice and learning model parameters.
In the extreme environments posed by war fighting, fire fighting, and nuclear accident response, the cost of direct human exposure is levied in terms of injury and death. Robotic alternatives must address effective operations while removing humans from danger. This is profoundly challenging, as extreme environments inflict cumulative performance damage on exposed robotic agents. Sensing and perception are among the most vulnerable components. We present a distributed robotic system that enables autonomous reconnaissance and mapping in urban structures using teams of robots. Robot teams scout remote sites, maintain operational tempos, and successfully execute tasks, principally the construction of 3-D Maps, despite multiple agent failures. Using an economic model of agent interaction based on a free market architecture, a virtual platform (a robot colony) is synthesized where task execution does not directly depend on individual agents within the colony.
In this paper, the ability of the retina to detect motion in the retinal peripheral visual field is emulated. The process of emulation utilizes a special receptive field defined in the spatial and temporal domains. The simulation studies on this emulated peripheral visual field show that it is possible to acquire certain robust abilities in artificial systems which are inherent in their corresponding biological processes. Abilities such as noise suppression were evident in the emulated motion detection system presented in this paper.
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