Charles P. Neuman

Dynamic sensor-based control of robots with visual feedback

Sensor-based robot control may be viewed as a hierarchical structure with multiple observers. Actuator, feature-based, and recognition observers provide the basis for multilevel feedback control at the actuator, sensor, and world coordinate frame levels, respectively. The analysis and design of feature-based control strategies to achieve consistent dynamic performance is addressed. For vision sensors, such an image-based visual servo control is shown to provide stable and consistent dynamic control within local regimes of the recognition observer. Simulation studies of two- and three-degree-of-freedom systems show the application of an adaptive control algorithm to overcome unknown and nonlinear relations in the feature to world space mapping.

Kinematic modeling of wheeled mobile robots

We formulate the kinematic equations of motion of wheeled mobile robots incorporating conventional, omnidirectional, and ball wheels.1 We extend the kinematic modeling of stationary manipulators to accommodate such special characteristics of wheeled mobile robots as multiple closed-link chains, higher-pair contact points between a wheel and a surface, and unactuated and unsensed wheel degrees of freedom. We apply the Sheth-Uicker convention to assign coordinate axes and develop a matrix coordinate transformation algebra to derive the equations of motion. We introduce a wheel Jacobian matrix to relate the motions of each wheel to the motions of the robot. We then combine the individual wheel equations to obtain the composite robot equation of motion. We interpret the properties of the composite robot equation to characterize the mobility of a wheeled mobile robot according to a mobility characterization tree. Similarly, we apply actuation and sensing characterization trees to delineate the robot motions producible by the wheel actuators and discernible by the wheel sensors, respectively. We calculate the sensed forward and actuated inverse solutions and interpret the physical conditions which guarantee their existence. To illustrate the development, we formulate and interpret the kinematic equations of motion of Uranus, a wheeled mobile robot being constructed in the CMU Mobile Robot Laboratory.

Kinematic modeling for feedback control of an omnidirectional wheeled mobile robot

We have introduced a methodology for the kinematic modeling of wheeled mobile robots. In this paper, we apply our methodology to Uranus, an omnidirectional wheeled mobile robot which is being developed in the Robotics Institute of Carnegie Mellon University. We assign coordinate systems to specify the transformation matrices and write the kinematic equations-of-motion. We illustrate the actuated inverse and sensed forward solutions; i.e., the calculation of actuator velocities from robot velocities and robot velocities from sensed wheel velocities. We apply the actuated inverse and sensed forward solutions to the kinematic control of Uranus by: calculating in real-time the robot position from shaft encoder readings (i.e., dead reckoning); formulating an algorithm to detect wheel slippage; and developing an algorithm for feedback control.

Arm signature identification

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 manipulators 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.

Dynamic visual servo control of robots: An adaptive image-based approach

Sensory systems, such as computer vision, can be used to measure relative robot end-effector positions to derive feedback signals for control of end-effector positioning. The role of vision as the feedback transducer affects closed-loop dynamics, and a visual feedback control strategy is required. Vision-based robot control research has focused on vision processing issues, while control system design has been limited to ad-hoc strategies. We formalize an analytical approach to dynamic robot visual servo control systems by first casting position-based and image-based strategies into classical feedback control structures. The image-based structure represents a new approach to visual servo control, which uses image features (e.g., image areas, and centroids) as feedback control signals, thus eliminating a complex interpretation step (i.e., interpretation of image features to derive world-space coordinates). Image-based control presents formidable engineering problems for controller design, including coupled and nonlinear dynamics, kinematics, and feedback gains, unknown parameters, and measurement noise and delays. A model reference adaptive controller (MRAC) is designed to satisfy these requirements.

Discrete dynamic robot models

An inherently discrete-time dynamic model is introduced for robotic manipulators. Although robot dynamics are highly coupled and nonlinear, the model is compact and suitable for control engineering applications. The model is designed to guarantee conservation of energy (and momentum, if appropriate) at each sampling instant. Initial numerical experiments with cylindrical robots confirm the feasibility and applicability of the discrete dynamic robot model.

Computational robot dynamics: Foundations and applications

In 1984, the authors unveiled the computer program Algebraic Robot Modeler (ARM) for the symbolic generation of complete closed-form dynamic robot models. In this paper, we introduce computational robot dynamics as the synthesis of classical mechanics and computer software for the symbolic and numeric modeling of robotic mechanisms, and branch-out in three directions. First, we outline the foundations of computational robot dynamics. From its inception (in 1973), we review chronologically the contributions of prominent roboticists, tracing the parallel development of robot dynamics formulations and computational robot dynamics. We then highlight our research activities, the current capabilities of ARM, and our plans for the continuing development and application of ARM and computational robot dynamics. Finally, we focus on practical applications of computational robot dynamics. We apply ARM to produce examples illustrating the comparative computational requirements of robot dynamics formulations for symbolic processing and customized algorithms for numeric processing.

Properties and structure of dynamic robot models for control engineering applications

Abstract Controller design for robotic manipulators requires a fundamental physical understanding of the properties and structure of dynamic robot models. This paper focuses on the Lagrangian formulation which is attractive from both the dynamic modeling and control engineering points-of-view. Physical and mathematical properties and structural characteristics of the complete dynamic robot model are demonstrated. Implications of the model for control system analysis and design are then indicated. Physical interpretation leads naturally to the decomposition of the model into the positioning arm and end-effector subsystems and motivates the application of decentralized control to robotic manipulators. The authors then propose the application of control the positioning arm and artificial intelligence and intelligent sensors to control the end-effector.

ARM: An algebraic robot dynamic modeling program

The computer program ARM (Algebraic Robot Modeler) has been implemented to generate symbolically the forward solution and complete Lagrangian dynamic robot model for control engineering applications. Development and application of this versatile dynamic modeling and control engineering tool are highlighted in this paper. The Q matrix formulation is employed to develop nested iterative algorithms for the symbolic computation of the inertial, centrifugal and Coriolis, and gravitational components of the Lagrangian dynamic robot model. The computational requirements are enumerated as functions of the number of degrees-of-freedom of the manipulator. Automatic generation of the centrifugal and Coriolis force vector dominates the computational load for both state-of-the-art and futuristic robots. The forward solution and complete dynamic model for the three degree-of-freedom positioning system of the Puma robot are exhibited to illustrate the capabilities of Arm. On-going enhancements to ARM are then summarized.

Frequency-multiplexed and pipelined iterative optical systolic array processors.

Optical matrix processors using acoustooptic transducers are described with emphasis on new systolic array architectures using frequency multiplexing in addition to space and time multiplexing. A Kalman filtering application is considered as our case study from which the operations required on such a system can be defined. This also serves as a new and powerful application for iterative optical processors. The importance of pipelining the data flow and the ordering of the operations performed in a specific application of such a system are also noted. Several examples of how to effectively achieve this are included. A new technique for handling bipolar data on such architectures is also described.