This paper describes an efficient and fast algorithm for finding the minimum distance between two convex polyhedrons in a three dimensional space. To obtain the minimal distance, the proposed computational scheme is based on a direct approach to minimizing the distance function which produces a succession of optimal search directions along object boundaries. This algorithm combines the gradient projection method'; and an additional optimal search direction when the gradient projection method leads to a zigzagging phenomenon. In this case, the additional optimal search direction accelerates significantly the convergence of the process. Extensive numerical experiments with convex polyhedra show the performance of this algorithm when compared with that of previous approaches. The proposed algorithm may be very helpful in solving the computation of minimal distance between a pair of convex sets, the collision detection problem or to track the closest points of moving convex objects.

In this paper, an adaptive learning (A-L) control scheme is proposed for cooperation of two manipulators handling a rigid object with model uncertainties. For robots performing repetitive cooperating tasks, their operations are decomposed into two modes: the single operational mode and the repetitive operational mode on which the A-L controller is based. In the single operational mode, the controller is a learning based adaptive controller in which the robotic parameters are updated by using the information of the previous operation. In the repetitive operational mode, the controller is a model-based iterative learning controller. The advantages of the A-L controller lie in the fact that it can improve the transient performance as robots repeat operations at a high speed of the learning convergence. Simulation results ascertain that the A-L algorithm is effective in controlling two cooperated robots with model uncertainties.

This paper presents a practical approach for the point-to-point control of elastic-jointed robot manipulators. With the proposed approach only position and velocity feedback are referenced, as opposed to most of the existing control schemes of elastic-jointed manipulators which require additional acceleration and/or jerk feedback. To guarantee the robustness of the controller, it is designed on extreme parameter uncertainties due to highly elastic joints of manipulators and energy motivated Lyapunov functions are used to derive the control law. Four pertinent controller gains are chosen in light of the on-line position and velocity feedback of the links and joint sensors. Through a simulated experimental verification, it is demonstrated that the designed simple position and velocity feedback controller, similar to that used for rigid-jointed robots, can globally stabilize the elastic-jointed robot for a bounded reference position. In addition, the tracking performance of the controller reveals that this simple control algorithm is robust in terms of joint flexibility. And the simplicity of the presented control algorithm, as compared to other model-based techniques for flexiblejoint robots, is particularly advantageous. Even though the simulated experiments are conducted on a single-link flexible joint robot, control law derived in this paper has general meaning for multi-link flexible joint robots.

The main objective of this work is to study the performance of a flexible single hub-arm system. The equations of motion are derived using the extended Hamilton principle. The Liapunov functional is used as a condition for the stability analysis. The Liapunov functional is considered as the sum of the internal energy of the flexible beam. The required drive torque was obtained directly through the solution of the inverse dynamic problem. Although the flexible link is nonminimum phase in nature, the use of Liapunov and the PD controller guarantee the causality for the stable case. The effects of tip mass as well as its inertia in the case of stable and asymptotic stable systems were investigated to ensure the validity of this procedure.

In this paper an algorithm is proposed for the problem of path planning of redundant manipulators among obstacles by using a suitable formulation for robot configurations and path strategy. In particular robotic manipulators have been modelled by using reference points on the kinematic chain and their Cartesian coordinates description. The path planning has been formulated as an optimization problem for the determination of adjacent configurations and the path among obstacles with minimum manipulator displacement. The fully Cartesian coordinates description has been useful for the economy of the numerical procedure and for the constraints formulation of link interference and obstacles avoidance constraints. Some examples are reported which prove the practical feasibility of the path planning procedure, and the numerical results have been tested as applicable to industrial robots through easy programming because of the concept of adjacent configurations.

In Part I a technique for the swing-up control of single inverted pendulum system is presented. The requirement is to swing-up a carriage mounted pendulum system from its natural pendent position to its inverted position. It works for all carriage balancing single inverted pendulum systems as the swing-up control algorithm does not require knowledge of the system parameters. Comparison with previous swing-up controls shows that the proposed swing-up control is simpler, eaiser. more efficient, and more robust.

In Part II the technique is extended to the case of the swing-up control of double inverted pendulum systems. Use is made of a novel selective partial-state feedback control law. The nonlinear, open-loop unstable, nonminimum-phase. and interactive MIMO pendulum system is actively linearised and decoupled about a neutrally stable equilibrium by the partial-state feedback control. This technique for swing-up control is not at all sensitive to uncertainties such as modelling error and sensor noise, and is both reliable and robust.

Pattern recognition and object localization, using various sensors such as vision and tactile sensors, are two important areas of research in the application of robotic systems. This paper demonstrates the feasibility of using some relatively inexpensive array of pressure sensors and a neural network approach to achieve object localization and pattern recognition. The sensors that are used are force sensing resistors (FSRs), more specifically, a 16 x 16 array of FSRs. Because of the nonlinearity associated with a FSR, three possible approaches for gathering output from the sensor array are used. The neural network that is used consists of two 2-layer counterpropagation networks (CPNs). One of the CPNs is trained to recognize contact signatures of different objects placed on a fixed reference position on the sensor array.

We present a method for kinematic calibration of open chain mechanisms based on the product of exponentials (POE) formula. The POE formula represents the forward kinematics of an open chain as a product of matrix exponentials, and is based on a modern geometric interpretation of classical screw theory. Unlike the kinematic representations based on the Denavit- Hartenberg (D-H) parameters, the kinematic parameters in the POE formula vary smoothly with changes in the joint axes, ad hoc methods designed to address the inherent singularities in the D-H parameters are therefore unnecessary. Another important advantage is that simple closed-form expressions can be obtained for the derivatives of the forward kinematic equations with respect to the kinematic parameters. After introducing the POE formula, we derive a least-squares kinematic calibration algorithm for general open chain mechanisms. Simulation results with a 6-axis open chain are presented.

This paper presents a mathematical model of a robot with one degree of freedom and numerical investigation of its dynamics in a particular parameter scan which is close to the upper boundary of the estimates for the parameters of rigidity and friction, while the length parameter L is treated as a free control parameter. In this L-scan the quasiperiodic and frequency locked solutions, their pattern and order of appearance are studied in the interval from the parameter range of immediate engineering significance to the point of appearance of transient chaos. In particular, a fractaltype multiple splitting of Arnold tongues is found in the parameter region bordering the range of engineering significance.

Motor torque constant is an important parameter in modeling and controlling a robot axis. In practice this parameter can vary considerably from the manufacturer's specification, if available, and this makes it desirable to characterise individual motors. Traditional techniques require that the motor can be removed from the robot for testing, or that an elaborate technique involving weights and pulleys be employed. This paper describes a novel method for measuring the torque constant of robot servo motors in situ and is based on the equivalence of motor torque and back EMF constants. It requires a very simple experimental procedure, utilizes existing axis position sensors, and eliminates effects due to static friction and joint cross coupling. A straightforward extension to this approach can provide a measurement of motor armature impedance. Experimental results obtained for a Puma 560 are discussed and compared with other published results.

This paper investigates the feasibility of constructing a humanoid robot using existing technology. Firstly, the adoption of the humanoid form is justified. The structure, strength and power capabilities of a human are analysed in engineering terms, and taken to represent the requirements specification for a humanoid robot. Technological alternatives to the biological components are reviewed and compared to this specification. The feasibility of matching human performance is considered, and it is concluded that the necessary power and energy requirements can be fitted within the mass and volume of the human body.

The dynamic model of an articulated vehicle consisting of one tractor and two semi-trailers is derived according to a systematic approach available in the literature. The model is produced on the assumption of no slippage, which enforces nonholonomic constraints. The resulting equations encompass a number of significant special cases.

This paper deals with the problems of the education of the staff in robotics and flexible automation at the secondary school level. It deals with an experiment which aims at training technicians for robotics and flexible manufacturing systems. The experiment began in 1989. and now the second generation of students that studies according to the curricula of this experiment has completed the education. Previous experience, especially from the standpoint of the result of the students and problems that arise during the realization of the experiment, are analyzed in this text.

To remove humans from hazardous environments “good” teleoperated robotic systems are needed. “Good” is defined here to mean a system which is task centred and which is transparent to the user-in other words, a system you can see through. A teleoperated robotic system which strives towards this goal is described by way of illustration. The fundamentals that have helped to achieve this are: design and build from a task centred viewpoint, careful design of the Human Computer Interface(HCI) and special consideration for the systems integration task.

The Mihailo Pupin Institute is among the largest and most eminent research organizations in technical sciences in Yugoslavia. Founded in 1946 as the Telecommunications Institute of the Serbian Academy of Sciences and Arts, it was given its present name 13 years later. In the Institute's early days the personnel, about one hundred employees, just a few of them researchers, has grown to more than 850 employees, 300 of them researchers, working in a 15,000 sq. meters modern research center.