Degree Name

Doctor of Philosophy


Faculty of Engineering


Programming robotic systems to carry out industrial manufacturing processes is often a difficult, time consuming and costly exercise. The goal of the research presented in this thesis is to develop a means to reduce the time and difficulty associated with programming these industrial robotic systems. This will allow robotic automation to be more easily justified for use in low volume manufacturing scenarios, where frequent reprogramming of the robotic devices is necessary.

In this thesis, a novel approach to the efficient programming of industrial robotic systems is presented. This method, termed automated offline programming (AOLP), involves the incorporation of computer automation into the existing offline programming (OLP) methodology. By automating many of the steps in the programming process that were previously performed manually, the overall programming process can be performed in a much more rapid and optimised manner.

Supplementary research in the fields of motion planning and robot path optimisation was required in order to develop algorithms tailored for our application. A review of recent advancements in motion planning algorithms uncovered the variety of different approaches used for planning paths of robotic devices. New and novel algorithms, specifically developed for industrial manipulator style robots, were conceptualised, developed and tested during this phase of the research project. An improved robot path optimisation algorithm is also presented and tested.

The AOLP system developed in this body of work was tested in a real world robotic welding cell. This system was found to drastically reduce programming times for a robotic welding process, when compared to the methods used previously to program the cell. It was found that the developed AOLP system provides significant benefit to the overall operation of the robotic cell, allowing its use to be justified in situations where, in the past, fully manual fabrication was used due to anticipated difficulties associated with programming.