UAV 3D Path Planning Based on IHDR Autonomous-Learning-Framework

An autonomous learning framework for UAV (unmanned aerial vehicle) 3D path planning is proposed. This framework consists of three parts, i.e. knowledge learning, knowledge retrieving and updating online. In this framework, the control value will be retrieved firstly from the existed knowledge when UAV runs online, so as the current action of the robot can be guided by the results. If the decisions retrieved from the knowledge base are invalid for the current UAV states, the custom algorithm for UAV path planning will be launched online and it generates the decisions for UAV’s movement in real time. In the meanwhile, the knowledge library is updated by adding the new decisions for the current states. Additionally, the knowledge library is constructed by the algorithm of incremental hierarchical discriminant regression (IHDR) and k-D tree, respectively. Among these methods, IHDR can construct a hierarchical tree by using the past path planning samples. By several simulations, IHDR method demonstrates better real time performance than the traditional k-D tree method under the proposed framework.