The thesis investigates obstacle detection and path planning methods appropriate
for use in an unmanned helicopter, which are essential to establishing autonomous
control of the vehicle.
The research described here is complementary to a feasibility study by EA Technology
Ltd to consider the use of robotic vehicles in the inspection of overhead
electricity power lines. The context of this project is the proposal that the inspection
be performed by a small, remotely-piloted helicopter fitted with a video
camera, returning video imagery to the ground-based observer.
A major obstacle to this concept is that, under current flight regulations, the
helicopter would not be allowed to fly outside the visual range of the ground
station. This work investigates whether autonomous obstacle detection based on
machine vision coupled with 3D path planning has the potential to remove t his
constraint.
The thesis first discusses the requirements and current methods of overhead line
inspection. A review of relevant machine vision and path planning methods is
given and a method for placing these into a hierarchical control system architecture
is described. A method for fast 3D path planning, based on the distance
transform, is introduced and experimental results are presented to show its effectiveness.
Implementation of the control system, integrating the vision system
and path planner, is done by means of a scale model laboratory test rig. The
construction of this test rig is described and results are presented to show obstacle
avoidance in action. The thesis ends with an assessment of how far the research
has advanced the prospect of autonomous aerial power line inspection.