Many enterprises experience difficulty in training people to work with expensive equipment, which is needed for carrying out profitable work tasks (e.g., production line robots). Similar problems are found when work is of a complex and safety-critical nature (e.g., nuclear environments, explosive placement, surgery). A common problem faced by educational institutions concerns the limited availability of expensive robotics equipment, with which students in the didactic program can work, in order to acquire valuable "hands on" experience. This paper describes a method of education and training involving off-line usage of virtual reality environments for task planning. When tasks are developed to the satisfaction of the trainee, they are exported to remote physical hardware, via the Internet, for real-world execution. Development of the system and the training experiments is discussed, along with some of the issues raised for telerobotics and solutions to the problem of detecting collisions in the virtual world. The approach has been shown to be viable, and increases the education and training possibilities for key workers while maintaining a low cost of ownership. The downtime of mission critical equipment is minimized while the gaining of valuable experience is maximized.

Conventional combinational design philosophies are, in many cases, proving inadequate for the sophisticated products and speed of response demanded by today's markets. Mechatronic design philosophies and concurrent practices for achieving the physical embodiment of the designers creative output are seen as an appropriate response to the challenge. Mechatronics is a holistic design philosophy which can yield intelligent machines (e.g. self-driving vehicles, robotic surgeons) for the benefit of mankind. The advent of new technologies has provided new possibilities for intelligent machines. The appropriate use of knowledge engineering has given much scope in this direction and this paper reports a number of applications across a range of industries from rock processing to food handling. It is clear that great benefit has been derived from the application of knowledge engineering and that techniques such as fuzzy logic and artificial neural networks have much to offer in complex systems where sensor data may be imprecise, where there may be a multiplicity of input data with possible conflicts or where it is not possible to generate precise algorithmic relationships or transfer functions. There is still, however, much research to be done regarding topologies of massively parallel systems, design rules for their creation, paradigms for sensor fusion and most importantly, reliability engineering and the concept of graceful degradation.