There can be little doubt that we are seeing a rapid acceleration in the pace of technological change as we approach the 1990s. In manufacturing industry this move towards automation is seen as a key element in preserving and improving competitiveness - the ‘fitter’ part of the well-known ‘leaner and fitter’ prescription for the revival of British industry.

It is, however, important to remember that technology is only one term in the equation and that, for the prescription to work, consideration must be made of the social and economic implications of change. This paper explores some of these implications from both a national and a company-level perspective and concludes that the benefits of advanced automation will only be realised if a total systems approach is taken to its implementation.

Continuing the work presented in part I, ‡ 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. We define a locally minimum velocity path as an optimal robot trajectory, given only local information about obstacle trajectories. We 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. Our results apply to both robots with and without maximum velocity limits.

Recently, three-dimensional motion analysis and shape recovery have attracted growing attention as promising avenues of approach to image understanding, object reconstruction as well as computer vision for robotic Systems. The image generation problem and the model generation problem are presented. More specifically, the inputs to the image generator are an old image, object model, motion specification, and hidden line and hidden surface algorithms. The output is a new image. Since the object model is given, the top-down approach is usually used. On the other hand, for the model generation problem, the input is an image sequence while the output is an object model. Since the object model is not given, and bottom-up approach is usually used.

In this paper, the largest possible object approach is proposed and the advantages of this approach are stated. They are:

1. This approach may be applicable to objects with planar surfaces as well as nonplanar surfaces.

2. This approach may be applicable to the case that there are more than one face change per frame.

3. This approach may be applicable when the camera is moving.

4. This approach may be applicable when the object is measured by several measuring stations.

By using this approach, algorithms for simple object reconstruction given a sequence of pictures are presented together with illustrative examples. The relevance and importance of this work are discussed.

The results of this paper may have useful applications in object reconstruction, pictorial data reduction and computer vision for robotic Systems.

Dynamic equations of chain structured robotic manipulators with compliant links and joints are developed in a non-recursive symbolic form. A program is developed in REDUCE to automate the symbolic expansion of these equations for any given chain structured manipulator. The symbolic non-recursive form of dynamic model is particularly suitable for controller synthesis and real-time control implementations. The link flexibility is included in the formulation using assumed mode shapes. The mode shapes and the parameters that are functions of the mode shapes are kept in symbolic form so that once a symbolic model is generated, different types of mode shapes can be studied using the same model. Because of the structural similarity between the developed equations and the well known rigid manipulator equations, the computer automated symbolic expansion capability presented here are likely to be utilized widely by many other researchers in the area who are already familiar with rigid manipulator problems.