For the Cartesian position controlled robot it is required to have an accurate mapping from the Cartesian space to the joint space in order to command the desired joint trajectories to achieve desired movements in the Cartesian space. That requires the correct kinematic Jacobian information. Since the actual mapping from the Cartesian space to the joint space is obtained at the joint coordinate not at the actuator coordinate, uncertainty in the Jacobian can be present. In the paper two feasible neural network schemes are proposed to compensate for the kinematic Jacobian uncertainty. Uncertainty in the Jacobian can be compensated by identifying either the actuator Jacobian matrix off-line or the inverse of that in online fashion. The case study of a stencilling robot is examined.

In this paper, accurate position control of a stencilling robot manipulator is designed. The stencilling robot mounted on a truck is required to draw lines and characters in the Cartesian space. One of the goals of the stencilling robot design is to make the controller as simple as possible such that a simple PID controller can be used. Since the robot is huge and heavy, the inertia of the robot is expected to play a major role in the tracking performance. The conventional PID controller works within certain accuracy, but does not guarantee the accurate trading performance as desired. Here we propose a neural network control scheme for a computed-torque like controller for the stencilling robot. The stability of the closed loop system is analyzed based on the Lyapunov theory. Simulation studies with the stencilling robot are carried out to test the performances.

We present an algorithm for generating a twice-differentiable curve on the rotation group SO(3) that interpolated a given ordered set of rotation matrices at their specified knot times. In our approach we regard SO(3) as a Lie group with a bi-invariant Riemannian metriac, and apply the coordinate-invariant methods of Riemannian geometry. The resulting rotation curve is easy to compute, invariant with respect to fixed and moving reference frames, and also approximately minimizes angular acceleration.

The calibration of spatial relationships among CAD models of objects used in offline robot programming is discussed. An approach based on a Clifford algebra is used, and simple algorithms for this kinematic localization problem are developed. The position and orientation of each object in a workcell are represented by multivectors of different ranks and the localization problem is converted into that of solving a set of multivector equations. Given locations of points in two frames, an averaging process which yields the best orientation as the root of four linear equations and the best position as a linear combination of the best rotation and the centroids of two point systems is developed. If normal vectors are also sensed, the Clifford algebra representation facilitates definition of a measure of orientation error compatible with the position error.>

An overview is given of the existing work on robot calibration, and some of the basic issues are identified in calibration and improvement of robot precision. Modeling, measurement, identification, and correction issues in robot calibration are discussed, and some of the unresolved questions are identified.

A data structure is presented for model driven robot programming and motion simulation. Handling of device parallelism and construction of data bases containing the description of one or more robots, the world in which they operate and parts that are being handled are also presented. The system structure of the model driven robot programming and simulation system STAR (Simulation Tool for Automation and Robotics) incorporating this data structure is described.

Computational kinematics and dynamics of robot manipulators are dealt with. A recursive method based on the vector form of Rodrigues' equation is presented for the computation of the associated coordinate transformations. The method allows for forward, backward, and two-way recursions and is applied to the computations of the Jacobian matrices and dynamic equations for mechanical manipulators. The computational complexities of the resulting equations are also evaluated and compared to some of the existing methods in each case. It is shown that the algorithms presented have certain computational advantages over most of the existing methods.