The authors describe the progress that has been made towards the development of a remotely controlled mobile robot that can be used by hazardous materials emergency response teams to perform a variety of tasks including incident localization and characterization, hazardous material identification/classification, site surveillance and monitoring, and ultimately incident mitigation. In its first end-to-end demonstration, the HAZBOT II vehicle navigated to the incident location from a distant (150-200 ft.) deployment site; entered a building through a door with thumb latch style handle and door closer; located and navigated to the suspected incident location (a chemical storeroom); unlocked and opened the storeroom's door; climbed over the storeroom's 12 in. high threshold to enter the storeroom; and located and identified a broken container of benzene.>

A program to develop planetary rover technology is underway at the Jet Propulsion Laboratory (JPL) under sponsorship of NASA. Developmental systems with the necessary sensing, computing, power, and mobility resources to demonstrate realistic forms of control for various missions have been developed, and initial testing has been completed. These testbed systems and the associated navigation techniques used are described. Particular emphasis is placed on three technologies: Computer-Aided Remote Driving (CARD), Semiautonomous Navigation (SAN), and behavior control. It is concluded that, through the development and evaluation of such technologies, research at JPL has expanded the set of viable planetary rover mission possibilities beyond the limits of remotely teleoperated systems such as Lunakhod. These are potentially applicable to exploration of all the solid planetary surfaces in the solar system, including Mars, Venus, and the moons of the gas giant planets.>

The authors apply Monte Carlo simulation techniques to gain further insight into the relationship between manufacturing errors and the performance of a robot using either the design model or arm signature model for control. In conventional design-model robot control, manufacturing errors contribute most to robot positioning errors. The authors relate the statistical parameters which characterize the manufacturing error probability distribution functions. In arm signature-based robot control (S-model), the correct arm signature model eliminates kinematic errors due to manufacturing. In this case, robot performance is limited by sensor errors which contribute to inaccuracy of the identified arm signature model. The relationship between the statistical parameters which characterize a robot's positioning accuracy to the statistical parameters which characterize the sensor performance is presented. The authors analyze and quantify the requirements of an arm signature identification system in terms of the underlying sensor performance.>

The S-Model identification algorithm described in [6,7] is a technique which can be used to accurately identify the actual kinematic parameters of serial link robotic manipulators. The actual kinematic parameters of a manipulator differ from the design parameters due to the presence of random manufacturing errors. The set of identified kinematic parameters is called the arm signature. Accurate arm signatures are needed to control and improve the end-effector positioning accuracy of robotic manipulators for a variety of important tasks. This paper describes the hardware and software implementation of a prototype arm signature identification system. This system uses an external ultrasonic range sensor to measure the Cartesian position of target points placed on the links of the robot. Algorithms to compensate the primary range measurements for spatial variations in air temperature and humidity are also incorporated. The relative Cartesian positioning accuracy of the sensor system is ± .02cm. The general characteristics of our sensor design and the overall system design which exploits averaging over many sensor readings offer numerous advantages for arm signature identification. The prototype system has been applied in [6] to improve the kinematic performance of seven Puma 560 robots. For these robots relative positioning accuracy was improved by a factor of 10 on straight'line positioning tasks. Analysis and simulation of systematic errors confirms that the resolution of our sensor system should provide kinematic performance close to the limitations of the joint encoders. Our experimental studies show that sensor bias ultimately limits kinematic performance using this arm signature system. Experience with this prototype system has demonstrated that the S-Model identification algorithm is a practical and viable method for improving the kinematic performance of robotic manipulators.

The positioning accuracy of commercially-available industrial robotic manipulators depends upon a kinematic model which describes the robot geometry in a parametric form. Manufacturing errors in machining and assembly of manipulators lead to discrepancies between the design parameters and the physical structure. Improving the kinematic performance thus requires identification of the actual kinematic parameters of each individual robot. This identification of the individual kinematic parameters is called the arm signature which is then incorporated into the manipulator's controller to improve positional accuracy. In this paper, an approach, based on a new parametric model of the kinematics, is introduced for arm signature identification. The S-Model utilizes 6.n parameters to describe the robot geometry and offers advantages for identification by decomposing the parameters into individually identified subsets. The S-Model parameters are then mapped into the equivalent Denavit-Hartenberg parameters for implementation into the controller. The S-Model arm signature identification algorithm can be implemented with relatively simple sensors and improves accuracy through statistical averaging. This algorithm has been implemented with an external ultrasonic range sensor to measure robot end-effector positions. Experimental results of arm signature identification of seven Unimation/Westinghouse Puma 560 robots demonstrated an average reduction in positioning error by a factor of 5-10 for a spectrum of representative test tasks.

Transfer function models that can provide physical insight into the dynamic behavior of manipulators and establish a foundation for control system analysis and design are identified. Emphasis is directed toward increasing the computational efficiency of the manipulator dynamic equations of motion. A conceptual three-degrees-of-freedom manipulator, which represents state-of-the-art configurations, is designed and simulated on a digital computer. The Lagrangian formulation is applied to develop the complete dynamic equations of motion of the manipulator. The simulation model includes the dynamic interactions of the centrifugal, Coriolis, inertial, and gravitational generalized forces along with the kinematic coupling, and it can accommodate load variations. The simulation model is interfaced to the Steiglitz-McBride identification algorithm to extract, from the complete nonlinear model, simplified linear models that characterize the arm in various regions of the work space. The simplified linear models are second-order discrete-time (input-output) transfer functions.