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Initial work on path generation routines for the University of Surrey Manipulator demonstrated the need for an improved control strategy. In view of the difficulties encountered in obtainins such a strategy by analytical means it was decided to investigate the feasibility of seneratins switching profiles for individual degrees of freedom of the manipulator by a suitable computer simulation. A computer implementation of an exponential constant volume pressure transient formed the basis for a simulation of one degree of freedom of the manipulator that requires a minimum of experimental data for its operation. Using this approach a simulation of the trajectory of a single axis of the manipulator under the control of a simple switch-over sequence was carried out in forward and reverse time It was argued that such a simulator package is suitable for the generation of switching profiles for the axis and a number of suggestions were made concerning the use of such simulators as the basis for the control of the arm.

This thesis documents the research that has led to the development, in prototype form, of a modular sensing system for use in a robot's work cell. The system implemented overcomes one of the major limitations of existing sensing systems, that is the difficulty of altering their sensing characteristics. The majority of sensing systems that are currently available are inflexible in that the addition of extra sensors requires, at the very least, substantial changes to both hardware and software. What these systems require is a facility through which users can easily, and readily, make changes to the configuration of the sensors they employ. In the modular system described, the sensors that provide the information about the robot's work cell are independent of the algorithms that make use of the information. That is, the sensors do not need any knowledge as to when or how the information they provide will be used. Similarly, the algorithms that make use of the data do not need any knowledge as to the provider of the data. This separation of data provider from data user enables the software that controls the sensors (and even the sensors themselves) to be upgraded without corresponding changes to the data user software. Additional sensors can easily be added to the system while redundant sensors can simply be removed. The location of objects within the robot's workspace is achieved by building a model of the workspace using the information provided by a number of sensors. As a prerequisite to model construction three problems had to be addressed. Firstly, the information extracted from different sensors is generally at different resolutions. Secondly, the representation of 3-D space requires large amounts of computer memory. Thirdly, the production of the 3-D model, particularly when a large number of sensors are involved requires a substantial amount of processor time. The first two problems were addressed using a data structure that allowed compact data storage, while the final problem was reduced by identifying parallel aspects of the processing and implementing them on a network of transputers. After the objects within the robot's workspace have been located, the next stage is to identify them. The identification is achieved by calculating the degree of match between measurable characteristics of the object to be identified and the same measurable characteristics of known objects. The degree of match, which is similar but not identical to the correlation function, between the object to be identified and each known object is then used to determine, if possible, the required identity of the object. The work contained within the thesis not only demonstrates the feasibility and benefits of a modular sensing system, over traditional sensing system, but has brought to light some points that will need further thought before a fully functional system is produced. The last chapter contains, in addition to a full and detailed list of conclusions made during the research, a summary of some of these areas that still require further work.

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In this research work, a study has been carried out on the current development of teleoperation and the control strategies adopted to co-ordinate human operator input and computer autonomous control. Based on the findings, a human and computer cooperative control concept has been developed to overcome the shortcomings of the existing control strategies. It utilises the human and computer characteristic advantages to complement each other to improve task performance and increase task success rate. Some aspects of the tasks are shared between humans and computers while computer assistance is introduced into the human supervisory functions in this co-operative control. Traditional problems of teleoperation such as time delay and task dependence of high-level control have been addressed and overcome by utilising the distributed and parallel processing system architecture and an expandable knowledge base incorporated with generic and task specific modules. Guidelines and an ideal system model have been proposed to provide system developers with future design guidance. A telerobotic system has been built based on the developed human and computer cooperative control concept. It uses the client/server model to achieve distributed and parallel processing. Operators can perform tasks through direct manual control and computer assisted task-level control provided in the system. Multiple manual inputs have been introduced to suit different control conditions. They provide the operator with intuitive and hardware independent input methods to manipulate the remote robot. A computer vision system has been developed to provide not only the visual feedback of the remote environment but also assistance in object location and robot navigation. Task modules containing generic and specific task handling functions are used for achieving human and computer co-operation at both task planning and execution levels. The experiment results obtained from the system evaluation have shown that the task completion time in co-operative control mode is half the time required in human direct manual control. Task performance achieved in co-operative control mode is less sensitive to the difference between the operators' capability. In conclusion, the usability and effectiveness of the co-operative control, and the validity of the techniques of camera calibration and image processing has been proven through the demonstration task.