The complex model presented in this paper consists of three main parts: (a) a model of the robot; (b) tools for building and displaying models of workpieces to be painted; (c) a model of the spatial distribution of the paint particles. The simulation process is preceded by the creation of the surface to be coated. A colour graphic display is used for this purpose. The surface appears on the screen together with the robot (hidden line elimination is included). The “spray gun” is installed at the robot's tip and its orientation, performance and other characteristics are to be set. The paint particles sprayed are supposed to form a cone, and by considering its intersection with the surface, the amount of paint delivered to any point can be evaluated and visualized in different colours. The operator simulates the process of painting by moving the spray gun, changing the parameters (speed, distance, etc.), erasing and repeating any step of the process. He can also work in an automatic path generation mode. The result is a near-to-optimal path of the robot's tip (minimizing paint loss and providing a good evenness of coating).

John Searle's attack on various interpretations of Artificial Intelligence has been the most thorough challenge to the philosophical foundations of Artificial Intelligence. In this paper we attempt to contribute to a growing body of argument as to why Searle is mistaken in his attack. We propose an analogy between intelligent objects and flying objects, leading to a definition of Artificial Intelligence similar to that of aerodynamics, but one which attempts to present general laws of intelligence in man and machines alike.

This paper reviews some current research problems in Artificial Intelligence applied to robotics, in particular the processing of sensory information and robot programming. It is perceived that much progress has been made in applying AI techniques to particular isolated tasks, but the important theme at the leading edge of the AI-based robotics technology seems to be the interfacing of the subsystems into an integrated environment. This requires flexibility of the subsystems themselves. But on the other hand it also increases the flexibility of the integrated system in that it broadens the variety of ways for solving problems through functional combination of the subsystems. The presentation in this paper is illustrated by examples from the hardware and software environment of the advanced robotics research at the Turing Institute.

Off-line programming of robots has a number of clear advantages over traditional ‘teach1’ methods which require the robot to be taken out of production. However, off-line programming techniques require extensive geometric facilities which are ideally provided by a geometric modelling system.

The use of modelling covers the planning of the robot workcell, as well as the detailed planning of the robot operations. Assembly, in particular, requires detailed geometric information concerning geometric features and solid properties.

The problem of controlling two cooperating robot arms is investigated. The task is to move an object from one place to another by grasping it at two different points using two robot arms. The path of the object is determined first in the Cartesian coordinate system, and the corresponding joint variable trajectory is evaluated from the object path for each robot. Each robot is then position controlled so that it follows its joint variable trajectory. The method was successfully applied to two RHINO robot system.

Two algorithms are presented which deal with translating an elliptic object, A, without collision amidst elliptic obstacles. These are:

(i) An exact algorithm, of complexity O(N log N), where N is the number of obstacles, yielding all directions along which the object is separable from the obstacles by a single translation.

(ii) An algorithm quadratic in N, which yields, with a degree of approximation determined by the user, a path of the object from its initial to its final position.

Part 1 of the paper* was concerned with the Jacobian of serial robot-arms and its matrix of cofactors; part 2 emphasizes the end-effector's velocity characteristics as affected at special configurations. The displacement about a ‘fixture-point’ is extended dually to a ‘fixture-plane’, and then to a ‘fixture-line’. These fixture-elements serve to highlight the prolixity of special configurations. Reciprocal screws lead to a complete survey of how the end-effector can ‘twist’ when one freedom is lost; extension to loss of more freedoms is explained. Then the fixture-elements are given elementary displacements that allow common requirements to be studied with the minimum of complications. Further investigation is thought to have promise for robot-control.

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.

We consider the problem of distance estimation in the context of a mobile camera, as, for example, when the camera is attached to a robot arm. It is natural in this case to consider stereo motion, and we present here the theoretical basis for an axial stereo method based on cross-correlation using the Fast Fourier Transform, which is appropriate in the context considered here. Finally, we describe the state of implementation of the system in our laboratory.

The paper describes a newly developed three-dimensional computer animation system to be used for creating motion programs for a set of actors. The animation package includes a language for describing spatial motion of bodies, as well as an interactive tool to be used for assigning geometrical attributes to them and for guiding and visualizing the play-back of motion programs. One of the main uses of the package is robot programming and motion simulation. A geometrical modeller, which is a an aid in creating the models of animated bodies, is also described.

This paper investigates the kinematics and motion of a human arm as a manipulator with seven degrees of freedom, and how to deal with the extra degree of freedom that exists. It proposes that a change of configuration be divided into a sequence of motions where each time one of the joints is locked. It then presents a general technique to solve inverse kinematic equations of the different reduced models that arise.

Computer simulation is a major tool in validation of robot motion planning systems, since, on the one hand, underlying theory of algorithms typically requires questionable assumptions and simplifications, and, on the other hand, experiments with hardware are necessarily limited by available resources and time. This is especially true when the motion planning system in question is based on sensor feedback and the generated trajectory is, therefore, unpredictable. This paper describes a simulation system ROPAS (for RObot PAth Simulation) for testing one approach — called Dynmic Path Planning (DPP) — to sensor-based robot collision avoidance in an environment with unknown obstacles. Using real time graphics animation of the motion planning system, the user can simulate the behavior of an autonomous vehicle or a robot arm manipulator with a fixed base. The overall structure of the system is described, and examples are presented.

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).

Artificial Intelligence clearly influences Robotics, but the advent of the latter alters the character of A.I. itself, bringing it closer to natural intelligence. This is partly due to greater attention to processes depending on continuous variables, and the combination of these with concept-based or “logical” processes. Some fundamental A.I. principles, notably Minsky's heuristic connection, involve continuity, and the advent ofRobotics should stimulate developments which take them into account. A scheme for a robot which can increase its speed of operation by a learning process is outlined.