Abstract
The emerging area of intelligent unmanned aerial vehicle (UAV) research has shown rapid development in recent years and offers a great number of research challenges for artificial intelligence. For both military and civil applications, there is a desire to develop more sophisticated UAV platforms where the emphasis is placed on development of intelligent capabilities. Imagine a mission scenario where a UAV is supplied with a 3D model of a region containing buildings and road structures and is instructed to fly to an arbitrary number of building structures and collect video streams of each of the building's respective facades. In this article, we describe a fully operational UAV platform which can achieve such missions autonomously. We focus on the path planner integrated with the platform which can generate collision free paths autonomously during such missions. Both probabilistic roadmap-based (PRM) and rapidly exploring random trees-based (RRT) algorithms have been used with the platform. The PRM-based path planner has been tested together with the UAV platform in an urban environment used for UAV experimentation.