Harry A. Scott

A feature-based inspection and machining system

Abstract This paper 1 describes an architecture for a system for machining and inspecting mechanical piece parts and an implementation of it called the Feature-Based Inspection and Control System (FBICS). In FBICS, the controller of a machining center or coordinate measuring machine uses a standard feature-based description of the shape of the object to be made as a principal input for machining and/or inspection. FBICS is a hierarchical control system and performs automated hierarchical process planning. FBICS serves: (1) to demonstrate feature-based inspection and control in an open-architecture control system; (2) as a testbed for solving problems in feature-based manufacturing; and (3) to test the usability of STEP methods and models.

Repository of sensor data for autonomous driving research

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.

High-level mobility controller for a remotely operated unmanned land vehicle

The U.S. Army Laboratory Command, as part of the Department of Defense Robotics Testbed Program, is developing a testbed for cooperative, real-time control of unmanned land vehicles. The program entails the development and integration of many elements which allow the vehicles to perform both autonomous and teleoperated functions. The National Institute of Standards and Technology (NIST) is supporting this program by developing the vehicle control system using the Real-time Control System (RCS) architecture. RCS is a hierarchical, sensory-based control system, initially developed for the control of industrial robots and automated manufacturing systems. NIST is developing the portions of RCS that control all vehicle mobility functions, coordinate the operations of the other subsystems on the vehicle, and communicate between the vehicle and the remote operator control station. This paper reviews the overall control system architecture, the design and implementation of the mobility and communication functions, and results from recent testing.

Control system architecture for a remotely operated unmanned land vehicle

Techbase Enhancements for Autonomous Machines (TEAM) is a joint effort among several US Army organizations, national laboratories, and commercial contractors to develop a vehicle control system that can support a mix of capabilities ranging from master-slave teleoperation to autonomous control. The overall TEAM control system architecture and the design of the mobility and communication functions are described. The architecture is based on the real-time control system (RCS), a hierarchical, sensory-based control system. In this application, RCS controls all vehicle mobility functions, coordinates the operations of the other subsystems on the vehicle, and communicates between the vehicle and the remote operator control station. The functional modules of the control system and their responsibilities are described, with emphasis placed on the modules that support mobility functions. The design of the mobility and communication subsystems and the implementation of the mobility and communication control systems are outlined.<<ETX>>

Performance measurements for evaluating static and dynamic multiple human detection and tracking systems in unstructured environments

The Army Research Laboratory (ARL) Robotics Collaborative Technology Alliance (CTA) conducted an assessment and evaluation of multiple algorithms for real-time detection of pedestrians in Laser Detection and Ranging (LADAR) and video sensor data taken from a moving platform. The algorithms were developed by Robotics CTA members and then assessed in field experiments jointly conducted by the National Institute of Standards and Technology (NIST) and ARL. A robust, accurate and independent pedestrian tracking system was developed to provide ground truth. The ground truth was used to evaluate the CTA member algorithms for uncertainty and error in their results. A real-time display system was used to provide early detection of errors in data collection.

Performance analysis of unmanned vehicle positioning and obstacle mapping

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 Technologys (NIST) Intelligent Systems Division has been a part of the Defense Advanced Research Project Agency LAGR (Learning Applied to Ground Robots) Program. NISTs 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.

The Multi-Relationship Evaluation Design Framework: Designing Testing Plans to Comprehensively Assess Advanced and Intelligent Technologies

As new technologies develop and mature, it becomes critical to provide both formative and summative assessments on their performance. Performance assessment events range in form from a few simple tests of key elements of the technology to highly complex and extensive evaluation exercises targeting specific levels and capabilities of the system under scrutiny. Typically the more advanced the system, the more often performance evaluations are warranted, and the more complex the evaluation planning becomes. Numerous evaluation frameworks have been developed to generate evaluation designs intent on characterizing the performance of intelligent systems. Many of these frameworks enable the design of extensive evaluations, but each has its own focused objectives within an inherent set of known boundaries. This paper introduces the Multi-Relationship Evaluation Design (MRED) framework whose ultimate goal is to automatically generate an evaluation design based upon multiple inputs. The MRED framework takes input goal data and outputs an evaluation blueprint complete with specific evaluation elements including level of technology to be tested, metric type, user type, and, evaluation environment. Some of MRED’s unique features are that it characterizes these relationships and manages their uncertainties along with those associated with evaluation input. The authors will introduce MRED by first presenting relationships between four main evaluation design elements. These evaluation elements are defined and the relationships between them are established including the connections between evaluation personnel (not just the users), their level of knowledge, and decision-making authority. This will be further supported through the definition of key terms. An example will be presented in which these terms and relationships are applied to the evaluation design of an automobile technology. An initial validation step follows where MRED is applied to the speech translation technology whose evaluation design was inspired by the successful use of a pre-existing evaluation framework. It is important to note that MRED is still in its early stages of development where this paper presents numerous MRED outputs. Future publications will present the remaining outputs, the uncertain inputs, and MRED’s implementation steps that produce the detailed evaluation blueprints.© 2010 ASME

Collaborative tactical behaviors for autonomous ground and air vehicles

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.

Intelligent system control: A unified approach and applications

Publisher Summary This chapter describes the hierarchical real-time control systems reference model architecture aiming at designing and developing intelligent control for large and complex engineering systems. Real time control system (RCS) is based on several general and fundamental principles of engineering systems. This chapter presents the methodology, or the development process, to apply RCS to the system control. The RCS reference model architecture and methodology provide a simple and systematic mechanism to obtain, describe, and organize domain operational knowledge. The single-node concept improves human understanding of the design. The process-template-based implementation approach gives a systemwide consistent interfacing infrastructure, freeing the developers from the infrastructural issues and allowing them to concentrate on individual component technology. The RCS methodology prescribes modular real-time simulation and animation functions. This facilitates early concept visualization and rapid prototyping that, in turn, reduces development cost. This chapter describes three intelligent systems division (ISD) case study control systems. The case study descriptions demonstrate the richness of the RCS architecture, from a generic process-template-based system to real-time rich sensing and planning systems. The case studies also describe the evolution and the road map of the methodology, from base-class models to process and interface definitions and to the plan of fully exercising the methodology.

Integration of Material Buffering Devices in an Automated Factory

In a hierarchically structured automated factory, most components are part of a larger, higher-level module of the factory. For example, robots and machine tools may be considered part of machining workstations. Several machining workstations may then be considered to be components of a manufacturing cell. An automated shop could be composed of more than one manufacturing cell, and so on. However, some equipment components do not fit neatly into any one module of the factory. One such component is a material buffering device (MBD) that acts as an interface between a workstation and a facility-wide material transport system. This paper addresses integrating such devices into an automated factory.