Gregory S. Broten

Continuous motion, outdoor, 2 1/2D grid map generation using an inexpensive nodding 2-D laser rangefinder

This paper introduces a technique for creating 2 1/2D grid maps of unstructured, outdoor environments, while traveling at high speeds, using an inexpensive nodding 2-D laser rangefinder. The nodding mechanism allows the acquisition of multiple range data sets for terrain in front of the robot. While these multiple data sets alleviate some of the problems traditionally associated with laser rangefinders, they also introduce a new set of problems. The paper investigates and quantifies factors that determine the accuracy of a map generated using a nodding laser rangefinder and derives an optimal basis for minimizing these errors. This research has determined that the most significant source of errors, for a nodding laser rangefinder configuration, are the roll, pitch and yaw accuracy for the laser beam. A variance weighted statistical approach was implemented to optimally fuse the range data into the 2 1/2D grid map. Simulations and experiments were conducted, demonstrating the performance of the variance weighted technique as superior to classical statistical methods

Architecture for autonomy

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. DRDCs 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 DRDCs 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 DRDCs current needs. It also presents an argument for why this architecture should also satisfy DRDCs future requirements as well.

The robotics experience

The classical engineering fields have evolved standards and techniques for developing complex systems. For example, both mechanical and electrical engineers have a wide variety of standard components, with defined capabilities, that they can draw upon (e.g., gears, transistors) in the design of complex systems. On the other hand, software engineering has struggled with the basic idea of reusability. Software engineering approaches, such as the use of components that promote the concept of information hiding and the introduction of structured programming languages, offer a roadmap to an improved software reuse. Unfortunately, their adoption by robotics researchers has been slow, impeded by the tradition of individual research groups crafting independent and incompatible solutions to common problems.

Probabilistic Obstacle Detection Using 2 1/2 D Terrain Maps

Navigating unstructured environments requires reliable perception that generates an appropriate world representation. This representation must encompass all types of impediments to traversal, whether they be insurmountable obstacles, or mobility inhibitors such as soft soil. Traditionally, traversability and obstacle avoidance have represented separate capabilities with individual rangefinders dedicated to each task. This paper presents a statistical technique that, through the analysis of the underlying 21/2 D terrain map, determines the probability of an obstacle. This integrated approach eliminates the need for multiple data sources and is applicable to range data from various sources, including laser rangefinders and stereo vision. The proposed obstacle detection technique has been tested in simulated environments and under real world conditions, and these experiments revealed that it accurately identifies obstacles.

Frameworks and middleware for umanned ground vehicles

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 Miros 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.

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.

World representations for unmanned vehicles

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 21/2-D elevation grid, that is applicable to many UxV classes, including aerial and ground vehicles. This paper describes DRDCs 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.

Enhancing Software Modularity and Extensibility: A Case for using Generic Data Representations

Portable, modular and extensible software allows robotics researchers to pool their resources by sharing algorithms, thus advancing research in the field of robotics at a faster rate than is possible under a non-collaborative model. The development and use of frameworks and middleware, allowing researchers to encapsulate robotic capabilities within a component structure, has traditionally been the focus of robotics software engineering research. Although components greatly enhance the software mechanisms portability, modularity and extensibility, they do not directly address the algorithmic issues confronting developers of robotics software. Software algorithms, implementing specific robotic capabilities, require input data and produce output results. As a rule, these input/output data representations are closely tied to a given algorithmic implementation and hence impose limitations on modularity and extensibility. This paper investigates the use of generic data representations to enhance software modularity and extensibility. Experiments, conducted on the DRDC raptor unmanned ground vehicle, compared the performance of algorithms based upon both generic and algorithm specific data representations. This research has determined that the performance penalty, resulting from generic data representations usage, is manageable by robotic platforms using current off-the-shelf computing platforms.

Inferring geometry from imagery - Enabling high speed traversal

Robotic vehicles operating in outdoor environments, commonly referred to as unmanned ground vehicles (UGV), are confronted with unstructured/semi-structured environments that are variable in nature. The geographical location significantly influences the environments appearance, there are longer term seasonal cycles, as well as immediate affects such as the weather and lighting conditions. This environmental diversity has long caused researchers considerable grief, as developing a generalized terrain classification algorithm has proven to be very difficult. Researchers have skirted this problem by relying upon ranging sensors and constructing 2½D or, more recently, 3D world representations. Although geometric representations have been used extensively orientation errors limit the lookahead distance. An important UGV capability is high speed traversal, hence extending the lookahead distance that in turn increases the maximum attainable vehicle speed is an active area of research. This focus on high speed traversal in variable environments has pushed researchers to investigate techniques that allow learning from experience, in a more human like manner. This paper presents Defence R&D Canada - Suffields progress in extending a 2½D world representation using vision and learning to infer geometry.

Towards Framework-Based U×V Software Systems: An Applied Research Perspective

Defence R&D Canada changed research direction in 2002 from pure tele-operated land vehicles to general autonomy for land, air, and sea craft (U×V). 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.