In a previous paper we presented a neural network called FRONTAL that attempted to capture the ability of infants to actively self-organize in a non-stationary environment. This paper describes the procedure whereby FRONTAL is interfaced to a robotic system called DART. DART comprises of a PUMA-562 arm and a real-time image processing system. The implementation procedure consisted of first connecting FRONTAL to a simulated DART robot. After successful operation, FRONTAL was connected to the real hardware DART. The ability of this robotic system to actively explore its surroundings and modify its behavior based on experience and environmental feedback confirms previous simulation results.

Planning appropriate trajectories can significantly increase the productivity of robot systems. To plan realistic time-optimal trajectories, the robot dynamics have to be described precisely. In this paper, a neural network based algorithm for tim e-optimal trajectory planning is introduced. This method utilises neural networks for representing the inverse dynamics of the robot. As the proposed neural networks can be trained with data obtained from exciting the robot with given torque inputs, they will capture the complete dynamics of the robot system. Threfore, the trajectories generated will be mo re realistic than those obtained by using nominal dynamic equations based on nominal parameters. Time-optimal trajectories are generated for a PUMA robot to demonstrate the proposed method.

One of the most fundamental problems in the development of an intelligent highway system or an autonomous mobile robot system for factory use is to find the necessary input control variables for smooth and safe movement of the vehicle, or robot, between any two configurations. In this paper it is demonstrated that this problem can be converted into one of finding a fitting function which satisfies the boundary conditions. Three curves, a quintic polynomial, a cubic polynomial and a triangular function are developed to perform the parallel transfer manoeuvre which forms the basis of several important manoeuvres such as reverse parking, moving off, negotiating a stationary obstacle, overtaking a moving vehicle, and changing lane. A detailed discussion of the effect of the vehicle's steering angle limit on the feasibility of these manoeuvres is presented. Simulation results using three typical vehicles, a long commercial vehicle, an ordinary car, and a small laboratory robot, travelling along three curves are also presented and discussed. Based on the comparative study, some suggestions for further work are made. Compared with other methods, this approach is simple and provides excellent simulation of human driver techniques. The paper concludes with a focused discussion about the integration of these techniques with satellite based GPS systems for automated vehicle guidance on highways.

In this paper, the global stability problem for constrained robot motions in the presence of constraint uncertainties is investigated. We focus on the uncertainties in the constraint functions and their effects on the global stability. PD type controllers are used and conditions for global stability are developed using Lyapunov's direct approach. In the presence of the constraint uncertainties under investigation, the desired position and constraint force can be guaranteed with global asymptotic convergence. The developed conditions for feedback gain selections clearly show the effects of the constraint uncertainties. For the case when the velocity measurements are not available, conditions for global stability regulation are also established and the robot controller uses only the measurements of the position angles. Finally, we consider the case where the robot joints are flexible and global stability conditions are given.

This paper presents a new adaptive controller as a solution to the problem of stabilizing nonholonomic mechanical systems in the presence of incomplete information concerning the system dynamic model. The proposed control system consists of two su bsystems: a slightly modified version of the kinematic stabilization strategy of M'Closkey and Murray which generates a desired velocity trajectory for the nonholonomic system, and an adaptive control scheme which ensures that this velocity trajector y is accurately tracked. This approach is shown to provide arbitrarily accurate stabilization to any desired configuration and can be implemented with no knowledge of the system dynamic model. The efficacy of the proposed stabilization strategy is illustr ated through extensive computer simulations with nonholonomic mechanical systems arising from explicit constraints on the system kinematics and from symmetries of the system dynamics.

The minimization of the joint torques based on the ∞-norm is proposed for the dynamic control of a kinematically redundant manipulator. The ∞-norm is preferred to the 2-norm in the minimization of the joint torques since the maximum torques of the actuators are limited. To obtain the minimum ∞-norm torque solution, we devised a new algorithm that uses the acceleration polyhedron representing the end-effector's acceleration capability. Usually the minimization of the joint torques has an instability problem for the long trajectories of the end-effector. To suppress this instability problem, an inequality constraint, named the feasibility constraint, is developed from the geometrical relation between the required end-effector acceleration and the acceleration polyhedron. The minimization of the °-norm of the joint torques subject to the feasibility constraint is shown to improve the performances through the simulations of a 3-link planar redundant manipulator.

This article deals with the kinematic calibration of the Delta robot. Two different calibration models are introduced: The first one takes into account deviations of all mechanical parts except the spherical joints, which are assumed to be perfect (“model 54”), the second model considers only deviations which affect the position of the end-effector, but not its orientation, assuming that the “spatial parallelogram” remains perfect (“model 24”). A measurement set-up is presented which allows to determine the end-effector's position and orientation with respect to the base. The measurement points are later be used to identify the parameters of the two calibration model resulting in an accuracy improvement of a factor of 12.3 for the position and a factor of 3.7 for the prediction of the orientation.

Current techniques which have been devised for motion planning and control of compliant multi-fingered grippers are based on the assumption of a constant compliance. The present investigation was undertaken to examine the validity of this assumption. To this end, the variational inequalities approach and the finite element method were used to formulate the general frictional contact problem. A two-step algorithm, which employs Quadratic Programming and non-differential optimization algorithms, is used to solve the resulting variational inequality and to provide the contact characteristics of compliant grippers.

The proposed method is applied to a case study involving a multi-fingered compliant gripper. The equivalent stress contours and the compliance of the system were obtained and analyzed. The proposed approach, which can be applied to any contact problem in robotics, overcomes most of the difficulties and approximations evident in current simplified contact analysis techniques.

We consider an optimization formulation that captures the quality of a grasp. In this model the object geometry, finger contact points and the magnitude of the external load are given. Perturbation of these parameters from their nominal values might lead to significant variations on the predicted grasping quality measures. In this paper we study the sensitivity of a class of grasping quality functions subject to these perturbations and introduce a global sensitivity measure together with a computational procedure to evaluate it. Our analysis is developed within the framework of sensitivity theory and dual methods of nonlinear programming. Numerical simulations supporting the theoretical analysis are presented.