As unmanned ground vehicles take on more and more intelligent tasks, determination of potential obstacles and accurate estimation of their position become critical for successful navigation and path planning. The performance analysis of obstacle mapping and unmanned vehicle positioning in outdoor environments is the subject of this paper. Recently, the National Institute of Standards and Technology's (NIST) Intelligent Systems Division has been a part of the Defense Advanced Research Project Agency LAGR (Learning Applied to Ground Robots) Program. NIST's objective for the LAGR Project is to insert learning algorithms into the modules that make up the NIST 4D/RCS (Four Dimensional/Real-Time Control System) standard reference model architecture which has been successfully applied to many intelligent systems. We detail world modeling techniques used in the 4D/RCS architecture and then analyze the high precision maps generated by the vehicle world modeling algorithms as compared to ground truth obtained from an independent differential GPS system operable throughout most of the NIST campus. This work has implications, not only for outdoor vehicles but also, for indoor automated guided vehicles where future systems will have more and more onboard intelligence requiring non-contact sensors to provide accurate vehicle and object positioning.
Tactical behaviors for autonomous ground and air vehicles are an area of high interest to the Army. They are critical for the inclusion of robots in the Future Combat System (FCS). Tactical behaviors can be defined at multiple levels: at the Company, Platoon, Section, and Vehicle echelons. They are currently being defined by the Army for the FCS Unit of Action. At all of these echelons, unmanned ground vehicles, unmanned air vehicles, and unattended ground sensors must collaborate with each other and with manned systems. Research being conducted at the National Institute of Standards and Technology (NIST) and sponsored by the Army Research Lab is focused on defining the Four Dimensional Real-time Controls System (4D/RCS) reference model architecture for intelligent systems and developing a software engineering methodology for system design, integration, test and evaluation. This methodology generates detailed design requirements for perception, knowledge representation, decision making, and behavior generation processes that enable complex military tactics to be planned and executed by unmanned ground and air vehicles working in collaboration with manned systems.
We describe a project to collect and disseminate sensor data for autonomous mobility research. Our goals are to provide data of known accuracy and precision to researchers and developers to enable algorithms to be developed using realistically difficult sensory data. This enables quantitative comparisons of algorithms by running them on the same data, allows groups that lack equipment to participate in mobility research, and speeds technology transfer by providing industry with metrics for comparing algorithm performance. Data are collected using the NIST High Mobility Multi-purpose Wheeled Vehicle (HMMWV), an instrumented vehicle that can be driven manually or autonomously both on roads and off. The vehicle can mount multiple sensors and provides highly accurate position and orientation information as data are collected. The sensors on the HMMWV include an imaging ladar, a color camera, color stereo, and inertial navigation (INS) and Global Positioning System (GPS). Also available are a high-resolution scanning ladar, a line-scan ladar, and a multi-camera panoramic sensor. The sensors are characterized by collecting data from calibrated courses containing known objects. For some of the data, ground truth will be collected from site surveys. Access to the data is through a web-based query interface. Additional information stored with the sensor data includes navigation and timing data, sensor to vehicle coordinate transformations for each sensor, and sensor calibration information. Several sets of data have already been collected and the web query interface has been developed. Data collection is an ongoing process, and where appropriate, NIST will work with other groups to collect data for specific applications using third-party sensors.