The previous paper by the same authors (Robotica 2, Part 2, pp. 93–103 1984) was concerned with the application of logic programming to the generation of paths for robots. This paper is dealing with the PIGS system for robot problem-solving in generating plans for robots. The system avoids the overhead of generating a new set of facts for each state considered. A computer-type control is adopted not only to generate a simple sequence of actions to be performed by the robot, but also to enable the robot to follow an accurately defined path. A suitable plan of action of a robot problem-solving system is generated. The system is written in the PROLOG language and acts as a robot plan generator that is integrated with a path finding procedure in order to avoid collisions whilst plans are being implemented.

This paper deals with the motivation behind robotics. It surveys the different goals of robotics research and analyses the ways in which robotics could help in various domains. After discussing the task specifications, emphasis is placed on three research sub-cultures that form separate robot “worlds”, viz. the Model World, the Industrial World and the Human World. The paper concludes with a case study to illustrate the need to reduce rash predictions and stimulate better science.

Computer Vision is essentially concerned with emulating the process of seeing, naturally manifested in various higher biological systems,1–4 on a computational apparatus, and is consequently part of the Artificial Intelligence field within the sub-category of Machine perception. Seeing has to do with making sense of image data acquired through an optical system and subsequently dealt with at increasing levels of abstraction and association with known facts about the world. The spectrum of interest in Computer Vision ranges from attempting to answer basic questions concerning the functionality of biological vision systems, particularly human, at one end, all the way to enhancing the reliability, speed and cost effectiveness of specific industrial operations, particularly component inspection and vision driven robotic manipulation. The main bulk of interest is in the middle, where the quest for generality pushes interest towards biological vision systems with their demonstrated effectiveness in a wide range of environments, some hostile, whilst the need for economic viability and timeliness in relation to particular application pushes interest towards finding workable algorithms which function reliably at high speed on affordable apparatus.

This paper is addressed, in somewhat tutorial style, at clarifying, by examples of work in the area, the issues surrounding application oriented robotic vision systems, their assumptions, strengths, weaknesses and degree of generality, and at the same time putting them in the context of the overall field of Computer Vision. In addition, the paper points to directions of development which promise to provide powerful industrial vision tools at an acceptable price.

This paper deals with the control technique of a computer-controlled manipulator with high nonlinearity. To overcome the nonlinearity, a linearization of the system by nonlinear feedback has been employed. Because of the difficulty of the parameter identification under the variation of load, it is not easy to make correct nonlinear compensation for its linearization. In this paper, to solve this problem a robust servo controller based on a model is designed for the linearized manipulator, and a control system is constructed taking account of input nonlinearity. The method is applied to the three-joint manipulator endowed with a software servo using a minicomputer, and the effect of the proposed method is investigated.

A hierarchical robot sensory system being developed for industrial robotics is described. At each level of the hierarchy, sensory interpretative processes are guided by expectancy-generating modeling processes. The modeling processes are driven by a priori knowledge (object prototypes), by knowledge of the robot's movements (feed-forward from the control system), and by feedback from the interpretative processes (prior state of the sensory world). At the lowest level, the senses (vision, proximity, tactile, force, joint angle, etc.) are handled separately; above this level, they are integrated into a multi-model world model. At successively higher levels, the interpretative and modeling processes describe the world with successively higher order constructs, and over successively longer time periods. Each level of the modeling hierarchy provides output, in parallel, to guide the corresponding levels of a hierarchical robot control system.

This paper describes an algorithm which recognizes the position and the orientation of a structural industrial part, such as a crankshaft, utilizing the relationships between its elementary blobs. Crankshafts are arranged tightly and piled up in multiple layers and their image from above includes regions (i.e. pictures) of crankshafts not only of the current top layer but also of the lower ones; it thus becomes complicated. First, the algorithm carries out the connectivity analysis for an input binary image, and then extracts elementary blobs by applying a line fitting procedure on every sequence of boundary pixels of connected regions. Next, each blob is judged to determine to which component of a part it corresponds, using the size model. Then the relationships (distances and orientations) between blobs are examined, using their relational models, and a group of blobs of one part is recognized. Its position and orientation are calculated simultaneously. This model matching algorithm is implicitly included in the procedures.

An automated manipulating system, which performs the task of loading, positioning and holding of parts in the working area of an industrial robot is discussed in this paper. The system feeds component parts for the lifting mechanisms of electric trucks to be arc-welded by the robot and then removes them, thus completing a predetermined cycle. It is a part of an actual assembly line in a standard workshop for manufacturing electric trucks. The system's operation is described in detail, and the basic technological characteristics of the welding and peripheral equipment are given. Also, some features of an original welding torch cleaning device are described. The principal approach of planning, designing and implementing robotized cells for automatic arc-welding using a robot is also discussed.

With the recent advances of VLSI technology, it has become possible to realize parallel architectures for image processing which are more efficient than the customary sequential processors. The paper discusses general architectural requirements for parallel processing and then the existing systems architectures. This is followed by a description of VLSI devices for image processing in three examples. The paper emphasizes VLSI architectures for image feature extraction, classification and structure (syntax) analysis.

This paper deals with the simulation of a control of an industrial arm using a prediction filter to track an object. It also deals with a developed range-sensing subsystem (range sensor), which, making use of trigonometric principles, can sense the two-dimensional location of the object even if it is moving. First, the hardware and the software of the 2-D range sensor are described. Next, processes to give the arm successive locations to arrive at, using the data obtained by the range sensor are illustrated. Finally some resultant simulation follows.

A robot-based automatic system for adjusting energy regulators in electric cookers is described in this paper. It is claimed that this system improves the quality of the regulators and increases productivity. First, the operator's intuitive judgement and decision-making are simulated on a microcomputer; the structure and performance variables of the regulator are then described. A discussion of computer modelling of the regulator then follows, leading to the development of an algorithm for the adjustment procedure and overall strategy of the system. Experiments on 2,000 regulators showed that this automated operation was superior to the manual procedure as regards consistency and accuracy. This technique based on a robot may be applied to quality control and manufacture of a variety of similar products.

The control of a robot manipulator with force sensors in the gripping tips is considered. The sensors measure three components of the force. Complicated motions of the manipulator are complied from basic movements, and the paper describes how the operation of grasping an arbitrarily positioned object is constructed from the basic movements. A mathematical model of the basic procedure of keeping contact with the object is considered.

Machine vision systems incorporating highly parallel processor architectures are reviewed. A new processor architecture, the image flow computer, is presented in detail. An interactive image processing programming language based on mathematical morphology is then presented. A detailed example of the use of the system for the inspection of a particular industrial part concludes the presentation.

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.

The increased demand on the performance and efficiency of industrial robots, has led to the design of sophisticated control systems. Such control systems require an accurate dynamic model of the system. A commonly used method of modeling an industrial robot, involves the description of a set of dynamic equations, relating actuator torques to loads and accelerations. These equations are generally quite complex and inconvenient for implementation on digital computers.

Another method often used for identification, is the ‘indirect method’, in which the transfer function is obtained in two steps. The discrete time model is first derived from samples of the input and output measurements, which is then transformed to the continuous-time model. A limitation of this method is that it requires the excitation to be of the ‘persistently exciting’ type, thus precluding the application of simple inputs like the step signal.

This paper describes a ‘direct’ method for identification of an ‘industrial robot’ from samples of input and output observations. Results of modeling an industrial robot and two simulations are presented. One of the simulations, and the industrial robot uses the step input as excitation. The other example was excited with an exponential input.