Robot intensiveness in the manufacturing industry is growing rapidly, but the construction industry has been slow to capitalise on the array of robotic technology now available. An attempt is made to indentify various robot ensembles that may prove economically viable in building construction, and to configure these in such a way that particular characteristics of robot work methods are used to maximum advantage within the constraints of the construction environment.

Cybernetic techniques already ensure that computing machines are produced efficiently. Consideration is now given to the case for automating the means of producing computer software. A formal notation to describe software production is outlined and some machine portable systems are introduced. New and powerful techniques for modelling machines and producing ‘program’ generators are described.

This paper describes the principles of the advanced programming techniques often dubbed Artificial Intelligence involved in decision making as may be of some value in matters related to production engineering. Automated decision making in the context of production can adopt many aspects. At the most obvious level, a robot may have to plan a sequence of actions on the basis of signals obtained from changing conditions in its environment. These signals may, indeed, be quite complex, for example the input of visual information from a television camera.

At another level, automated planning may be required to schedule the entire work cycle of a plant that includes many robots as well as other types of automated machinery. The often-quoted dark factory is an example of this, where not only some of the operations (such as welding) are done by robots, but also the transport of part-completed assemblies is automatically scheduled as a set of actions for autonomic transporters and cranes. It is common practice for this activity to be preprogrammed to the greatest detail. Automated decision making is aimed at adding flexibility to the process so that it can absolve the system designer from having to forsee every eventuality at the design stage.

Frequent reference is made in this context to artificial intelligence (AI), knowledge-based and expert systems. Although these topics are more readily associated with computer science, it is the automated factory, in general, and the robot, in particular, that will benefit from success in these fields. In this part of the paper we try to sharpen up this perspective, while in part II we aim to discuss the history of artificial intelligence in this context. In part III we discuss the industrial prospects for the field.

In disassembly tasks, due to the large variety of objects and the different positions and orientations in which they appear, the disassembly trajectories supplied on-line by a human operator or an automatic recognition system can contain large errors. The classical compliant control methods turn out to be insufficient to eliminate sticking which is due to these errors. This paper presents a compliant control method for disassembly of non-elastic parts in non-elastic environments which adopts the trajectories according to realised motion. In case of sticking a new direction of motion is searched for until the manipulated part is set into motion.

Structural and control flexibilities affect the absolute precision of serial manipulators. A semi-flexible kinematic model is developed, to improve the absolute static precision. It expands the solid body model by incorporating a spring effect for each joint and a beam effect for each link. Simulation results confirm the adequacy of the model. The dependencies existing between the articulate posture of the manipulator, the effects of the external efforts and the gravitational load on the global structure are properly described. The identifiability of the added parameters is explored on a RR planar robot. It requires efforts and pose errors to be known in the tool frame only.

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 describes the generation of profiles of jaws of robot grippers for holding parallel-sided workpieces. The profiles are effectively ruled surfaces. The line segments of the ruled surfaces are designed to contact workpieces of different thicknesses. The optimum design of these gripping surfaces is considered in this paper

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.

For ease of manufacture, axisymmetric components produced by processes such as forging, casting and moulding are often designed with a taper angle. This paper presents a family of devices for handling such components by their tapered portion. The devices are essentially finger tips, or jaws, to be fitted to standard scissor-type robot grippers. The jaws possess a three-dimensional profile constructed as a stack of v-shaped planar curves. The special jaw profile enables components of different diameters and taper angles to be gripped concentrically without calling for complex movements to reposition the gripper. The equations describing two categories of profile are derived and the optimum selection of profile parameters to yield compact jaws to grip components of a wide range of dimensions is discussed in the paper.

A prototype system utilizing a vision technique to monitor the parameters of adhesive beads or sealants is presented. The system uses a structured light technique to investigate adhesive beads and extract information about the shape of the bead from binary images. The geometrical model of the bead, the relationship between this model and the shape of a line of light on the image are investigated. The models for calculating the parameters of the bead and the height of the camera are analyzed. Control strategies are suggested.

Actual positions of industrial robot end-effectors differ from those commanded off-line. Consequently, it is then difficult for robots to fulfill certain tasks, such as automated assembly sequences or tasks where high performance accuracy is required. This paper shows that the accuracy of robot performance can be improved by introducing deviation matrices which are functions of many possible error sources. As a first approach, an experiment was carried out where structural elastic deflections, one of the many error sources, of a robot ASEA Irb 6/2 were taken into account. The experiment showed that using the improved model, the positioning accuracy of an ASEA Irb 6/2 robot carrying a weight of 5.6kg was improved from 2.5mm to 0.25mm and the orientation accuracy was improved from 0·45° to 0·3°.

Position estimation is a key issue for an ALV Autonomous Land Vehicle) in navigating a mountainous area. The unevenness of the terrain makes mechanical velocity sensors inaccurate (due to wheel slippage), and the lack of appropriate landmarks complicates the problem. In this paper, we present a solution method using features of the skyline. The skyline from the vision system is assumed given, and compared with a computer map, called the CAD-MAP. The algorithm is composed of: a) Identification of the peak points in the camera skyline, b) Computing the ALV position for the identified peak points, and c) Searching for the corresponding peak point in the CAD-MAP. Heuristics for computational efficiency and solution accuracy are also included in the algorithm. To test the validity and effectiveness of the algorithm, numerous simulations were performed and analyzed.

It is shown that if one comes back to the formulation of the Hamilton's variational principle, it is then possible to obtain new viewpoints on the tracking control of robot manipulators. First, the Lagrange multiplier associated to the sliding surface can be interpretated in terms of control effort and/or forces of reaction of the mechanical system. Secondly, one can use the Taylor expansion of the mechanical Lagrangian, combined with a neighbour- ing Hamilton's principle, to obtain control schemes via sliding surfaces. Thirdly, a perturbation approach combined with the neighbouring Hamilton's principle provides results on the robustness of the control.

In this paper, the problem of real-time computation of a dynamic model of manipulator is considered. In order to decrease the number of operations for dynamic model computation, an approximate model is introduced. Also, a relative error criterion is proposed, which enables one to determine the computing periods of various parts of a dynamic model of manipulator.