Scene analysis for autonomous system control

The potential of autonomous airborne platforms have long been considered for applications in surveillance, exploration and search and rescue. For example, an autonomous blimp or airship, able to navigate through complex unstructured environments, could be used to survey the aftermath of dangerous earthquake zones, or be employed to provide unique replay angles in sporting events. Therefore autonomous airships fill an important gap in the spectrum of aerial observations, supplying images with better resolution and much more acquisition flexibility than those acquired through satellite or airplanes. This paper proposes to configure a semi-autonomous Unmanned Aerial Vehicle (UAV) for research into navigation and tracking through visual servoing using an airborne robotic platform. The architecture incorporates an object detection/tracking algorithm, which aims to provide robust performance under practical indoor environments. Station keeping techniques are also integrated to allow the airborne platform to hover in a static position above a desired target. Through combining the Circular Hough Transform with a number of image processing techniques, a resilient landmark segmentation algorithm is produced. ‘Path Prediction’, ‘Velocity Prediction’ and a ‘Dynamic Radius’ algorithm are developed to improve the speed and reliability of visual tracking. In addition, a ‘Heuristic Grid’ approach is examined as a means of performing station holding with the autonomous platform. The system is capable of segmenting and tracking the desired landmark from an image scene. A series of control commands are then sent to the autonomous vehicle to successfully keep the target within the image window.