Christopher J. Scrapper

Robot simulation physics validation

Computer simulation of robot performance is an essential tool for the development of robot software. In order for simulation results to be valid for implementation on real hardware, the accuracy of the simulation model must be verified. If developers use a robot model that is not similar enough to the actual robot, then their results can be meaningless. To ensure the validity of the robot models, NIST proposes standardized test methods that can be easily replicated in both computer simulation and physical form. The actual robot can be tested, and the computer model can be finely tuned to replicate similar performances on equivalent tests. To illustrate this, we have accomplished this task with the Talon Robot on NIST standard test methods.

Using a priori data for prediction and object recognition in an autonomous mobile vehicle

A robotic vehicle needs to understand the terrain and features around it if it is to be able to navigate complex environments such as road systems. By taking advantage of the fact that such vehicles also need accurate knowledge of their own location and orientation, we have developed a sensing and object recognition system based on information about the area where the vehicle is expected to operate. The information is collected through aerial surveys, from maps, and by previous traverses of the terrain by the vehicle. It takes the form of terrain elevation information, feature information (roads, road signs, trees, ponds, fences, etc.) and constraint information (e.g., one-way streets). We have implemented such an a priori database using One Semi-Automated Forces (OneSAF), a military simulation environment. Using the Inertial Navigation System and Global Positioning System (GPS) on the NIST High Mobility Multi-purpose Wheeled Vehicle (HMMWV) to provide indexing into the database, we extract all the elevation and feature information for a region surrounding the vehicle as it moves about the NIST campus. This information has also been mapped into the sensor coordinate systems. For example, processing the information from an imaging Laser Detection And Ranging (LADAR) that scans a region in front of the vehicle has been greatly simplified by generating a prediction image by scanning the corresponding region in the a priori model. This allows the system to focus the search for a particular feature in a small region around where the a priori information predicts it will appear. It also permits immediate identification of features that match the expectations. Results indicate that this processing can be performed in real time.

Stable Navigation Solutions for Robots in Complex Environments

During the initial phase of a disaster response it is essential for responders to quickly and safely assess the overall situation. The use of rescue robots that can autonomously navigate and map these environments can help responders realize this goal while minimizing danger to them. In order for rescue robots to be of service to the responders, they must be able to sense the environment, create an internal representation that identifies victims and hazards to responders, and provide an estimate of where they are and where they have been. Methods for developing a stable navigation solution are based on sensors that can be broadly classified into two approaches, absolute (exteroception) and relative (proprioception). Commonly, two or more of these approaches are combined to develop a stable navigation solution that is insensitive to and robust in the presence of the errors that plague partial solutions by taking into account errors in the vehicles pose, thus bounding the uncertainty in the navigation solution. Since the capabilities and limitations of these approaches vary, it is essential for developers of robotic systems to understand the performance characteristics of methodologies employed to produce a stable navigation solution. This paper will provide quantitative analysis of two proprioceptive approaches, namely encoder-based odometry and inertial navigation system, and an exteroceptive approach namely visual odometry that uses scan matching techniques.

Mobility open architecture simulation and tools environment

This paper will describe the mobility open architecture tools and simulation (MOAST) environment. This environment conforms to the NIST 4D/RCS architecture (Albus et al., 1002) and allows simulated and real architectural components to function seamlessly in the same system. This permits not only the development of individual components, but also allows for component performance metrics to be developed and for the components to be evaluated under repeatable conditions. The environment is composed of high-fidelity and low-fidelity simulation systems, a detailed model of real-world terrain, actual hardware components, a central knowledge repository, and architectural glue to tie all of the components together. This paper describe the components in detail and provide an example of how the environment can be utilized to develop and evaluate a single architectural component through the use of repeatable trials and experimentation that includes both virtual and real components functioning together.

Knowledge representation and planning for on-road driving

Abstract This paper presents a cost-based adaptive planning agent and knowledge layers that is operating at one level of a deliberative hierarchical planning system for autonomous road driving. At this level, the planning agent is responsible for developing fundamental driving maneuvers that allow a vehicle to travel safely amongst moving and stationary objects. This is facilitated through the application of knowledge to the graph creation process and the use of dynamic cost function within the incrementally created planning graph. The cost function varies to comply with particular road, regional, or event driven situations, and when coupled with the incremental graph expansion allows for the agent to implement hard and soft system constraints. Further discussion will be provided that details one of the expert systems that is implemented to provide the planning system with this knowledge in real-time.

An Integrated Control and Simulation Environment for Mobile Robot Software Development

In order to expedite the research and development of robotic systems and foster development of novel robot configuration, it is essential to develop tools and standards that allow researchers to rapidly develop, communicate, and compare experimental results. This paper describes the Mobility Open Architecture Simulation and Tools Framework (MOAST). The MOAST framework is designed to aid in the development, testing, and analysis of robotic software by providing developers with a wide range of open source robotic algorithms and interfaces. The framework provides a physics-based virtual development environment for initial testing and allows for the seamless transition of algorithms to real hardware. This paper details the design approach, software architecture and specific module-to-module interfaces.

Planning for on-road driving through incrementally created graphs

A general-purpose hierarchical planning framework that allows for cost-optimal, logic-constrained plans is presented. This framework is applied to planning an on-road path for an autonomous vehicle traveling amongst moving and stationary objects. Through this application, the ability to implement both hard and soft constraints, as well as cope with dynamic environments and user objectives are demonstrated.

A Mobile Robot Control Framework: From Simulation to Reality

In order to expedite the research and development of robotic systems and foster development of novel robot configurations, it is essential to develop tools and standards that allow researchers to rapidly develop, communicate, and compare experimental results. This paper describes the Mobility Open Architecture Simulation and Tools Framework (MOAST). The MOAST framework is designed to aid in the development, testing, and analysis of robotic software by providing developers with a wide range of open source robotic algorithms and interfaces. The framework provides a physics-based virtual development environment for initial testing and allows for the seamless transition of algorithms to real hardware. This paper details the design approach, software architecture and module-to-module interfaces.

A Simulation Interface for Integrating Real-Time Vehicle Control With Game Engines

This paper describes the use of video game engines as simulation environments that aid the development and testing of real-time vehicle controllers. The use of game engines for simulation is surveyed, with relevant technologies noted. The need to switch between different vehicle controllers, game engines and real vehicles gave rise to an integration architecture. The features of the architecture are described, including the execution model, message set and knowledge base. Adaptation of existing controllers, simulations and vehicles to this architecture is discussed. Issues of performance and scalability are addressed. An example is provided to illustrate the concepts.© 2007 ASME

Ground truth and benchmarks for performance evaluation

Progress in algorithm development and transfer of results to practical applications such as military robotics requires the setup of standard tasks, of standard qualitative and quantitative measurements for performance evaluation and validation. Although the evaluation and validation of algorithms have been discussed for over a decade, the research community still faces a lack of well-defined and standardized methodology. The range of fundamental problems include a lack of quantifiable measures of performance, a lack of data from state-of-the-art sensors in calibrated real-world environments, and a lack of facilities for conducting realistic experiments. In this research, we propose three methods for creating ground truth databases and benchmarks using multiple sensors. The databases and benchmarks will provide researchers with high quality data from suites of sensors operating in complex environments representing real problems of great relevance to the development of autonomous driving systems. At NIST, we have prototyped a High Mobility Multi-purpose Wheeled Vehicle (HMMWV) system with a suite of sensors including a Riegl ladar, GDRS ladar, stereo CCD, several color cameras, Global Position System (GPS), Inertial Navigation System (INS), pan/tilt encoders, and odometry . All sensors are calibrated with respect to each other in space and time. This allows a database of features and terrain elevation to be built. Ground truth for each sensor can then be extracted from the database. The main goal of this research is to provide ground truth databases for researchers and engineers to evaluate algorithms for effectiveness, efficiency, reliability, and robustness, thus advancing the development of algorithms.