From Motion Planning to Control - A Navigation Framework for an Autonomous Unmanned Aerial Vehicle

Abstract

The use of Unmanned Aerial Vehicles (UAVs) which can operate autonomously in dynamic and complex operational environments is becoming increasingly more common. While the application domains in which they are currently used are still predominantly military in nature, in the future we can expect widespread usage in the civil and commercial sectors. In order to insert such vehicles into commercial airspace, it is inherently important that these vehicles can generate collision-free motion plans and also be able to modify such plans during their execution in order to deal with contingencies which arise during the course of operation. In this paper, we present a fully deployed autonomous unmanned aerial vehicle, based on a Yamaha RMAX helicopter, which is capable of navigation in urban environments. We describe a motion planning framework which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Trees together with a path following controller that is used during path execution. Integrating deliberative services, such as planners, seamlessly with control components in autonomous architectures is currently one of the major open problems in robotics research. We show how the integration between the motion planning framework and the control kernel is done in our system. Additionally, we incorporate a dynamic path reconfigurability scheme. It offers a surprisingly efficient method