Owing to advances in machine vision, it is now possible to study automatic gripping of moving parts. This complex task requires a precise knowledge of the displacements of objects in a camera field.

In this paper, a method to analyse the motion of parts is presented; it is based on the correlation of numerical images. The treatment of data provided by the image background makes this method quite original.

The utilization of this method, often considered as rather awkward, makes it possible, in this case, to develop a position feedback operation of the robot actuators controlled in an open loop (step by step motors).

This paper presents an active visual method for determining the 3-D pose of a flexible object with a hand-eye system. Some simple and effective on-line algorithms to overcome various exceptional situations in chaincoding or determine the object pose more precisely are developed. The pose of a flexible object can be easily changed because of the flexible nature and the prediction of the pose is almost impossible. A new sensing pose is computed by using the image coordinates of the points on the border line of an image window and the current pose of a hand-eye system for the cases that the flexible object is extended outside the window. In a case of exceptional overlapping, a new sensing pose is computed by using the image coordinates of four extreme image points and the current pose of the hand-eye system. Through a chaincoding process on the skeletonized images, the stereo matching problem of two images is transformed into the matching of the curvature representations of the two skeletonized images. The 3-D pose of a flexible object is computed by using the results of this matching and the camera and hand-eye parameters calibrated beforehand. The initial sensing results are used in computing a new sensing pose to determine the object more precisely.

The concept of the force ellipsoid and the manipulability ellipsoid of robotic mechanism is extended to two cooperating robot arms, and the equations of the dual arm force and manipulability ellipsoids are derived. The load distribution problem for two cooperating robots is studied using the concept of the force ellipsoid. The problem is usually underspecified mathematically and a variety of optimal solutions may exist. A new solution approach utilizing the force ellipsoid is proposed in this paper. The load distribution problem is formulated as a nonlinear optimization problem with a quadratic cost function and inequality constraints. The optimality criterion is the minimum energy, and two different cases are considered depending on the presence of the constraints on joint torques.

A vibratory sensor capable of locating parts by measuring their inertial properties is described in this paper. The sensor is designed to be fitted to the wrist of a robot and used to acquire parts from a stack or a tray. The initial coordinates of the parts need only be approximately known (say to ±25 mm and ±45°). The robot is thus said to operate in a “semi-ordered” environment which can be realised inexpensively as accurate pallets, fixtures or other parts presentation equipment are not required. Furthermore, the absence of complete disorder, as would exist with a “bin-picking” approach where parts are allowed to lie at random in a bin, reduces the degree of sophistication demanded of the sensor system. Consequently, the cost of the latter can also be kept low.

The proposed sensor has a circular platform mounted on the shaft of a motor such that the shaft is normal to the platform. The part whose location is to be determined is held on the platform by magnetic or other means. The assembly consisting of the part, platform and motor is constrained to vibrate about an axis parallel to the platform. Two methods for computing the location of the part relative to the platform are described. Both methods require the natural frequencies of vibration of the part-platform-motor assembly for various angular positions of the platform. A simulation study to examine the effect of various design and operational parameters (inertias of the platform and part, stiffness of the spring restraining the movements of the vibrating assembly, location of part on platform, accuracy of frequency measurements) on the accuracy of the location computed is presented. The simulation results clearly demonstrate the feasibility of the sensor.

There is a great deal to be done in machine tool and robot programming research. The major problems include the slowness of the introduction of machine controllers, which have similar operating system and interfacing capabilities as the current 16 bit and 32 bit microcomputers, and the lack of intelligent, high-level standard languages, providing access not only at a high level, but also at the robot system programming level. The introduced and illustrated “MARTI” off-line robot program generator, under development by the author, attempts to provide software in this area.

This paper presents a practical method for generating task strategies applicable to chamferless and high-precision assembly. The difficulties in devising reliable assembly strategies result from various forms of uncertainty such as imperfect knowledge on the parts being assembled and functional limitations of the assembly devices.

In order to cope with these problems, the robot is provided with the capability of learning the corrective motion in response to the force signal through iterative task execution. The strategy is realized by adopting a learning algorithm and is represented in a binary tree-type database. To verify the effectiveness of the proposed algorithm, a series of experiments are carried out under simulated real production conditions. The experimental results show that sensory signal-to-robot action mapping can be acquired effectively and, consequently, the assembly task can be performed successfully.

For the trajectory following problem of a robot manipulator, a robust estimation and control scheme which requires only position measurements is proposed to guarantee uniform ultimate bounded stability under significant uncertainties and disturbances in the robot dynamics. The scheme combines a class of robust control laws with a robust estimator where the robust control law can be chosen to be either a modification of the standard computed torque control law or simply a linear and decentralized “PD” control law. The proposed robust estimator is also linear and decentralized for easy implementation. Constructive choices of the gains in the control law and estimator are proposed which depend only on the coefficients of a polynomial bounding function of the unknown dynamics. The asymptotic stability of the tracking errors and the estimation error is also investigated. Experimentation results verify the theoretical analysis.

The flexible structure of a robot multi-links manipulator can be either a side effect or, on the contrary, an essential feature. We present a fairly general model to derive the corresponding dynamic equations in quite a systematic and simple way. To this end, we use the Lagrange formulation with strain energy potential and Raleigh (dissipation) functions. The approach can incorporate torsional deformation and aerodynamic friction, and it applies easily to robots working in the sea. The trajectory control appears to be one in the presence of model imprecision, and a slightly modified version of the classical sliding control technique is utilized to design the tracking control of the manipulator. Then we introduce the time-varying inertia link device (carried out by means of sliding masses) which we suggested in earlier work, and we show how it can be used to improve the tracking control scheme above. This paper contributes new ideas concerning flexible multi-links arms and active inertia links.

We consider the problem of moving a point robot through a two dimensional workspace containing polygonal obstacles moving on unknown trajectories. We propose to use sensor information to predict the trajectories of the obstacles, and interleave path planning and execution. In this paper, we present preliminary work in which we propose our basic algorithm and define a locally minimum velocity path as an optimal robot trajectory, given only local information about obstacle trajectories. In the sequel (part II) to this paper we will show that the complexity of a path planning problem can be characterized by how frequently the robot must change directions to approximate the locally minimum velocity path.