Zhao Xingang

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.

A new LP-based obstacle-avoided model in path planning problem

A new linear programming obstacle-avoided model is presented in target pursuit and obstacle-avoided problem. The obstacle-avoided model only has the linear program once for obstacle avoidance at one time step. The collision forecasting domain of vehicle and obstacle is obtained, which can prevent the happening of collision between vehicle and obstacle. In the target pursuit problem, the dynamic equation of mass point with linear damping is taken as the state equation of vehicles motion. “Expansible Target Size” is proposed to describe the pursuit process for target. The Linear programming method is used to model and solve the obstacle-avoided pursuit problem. Finally, simulation is given to show the efficiency of the method.

Design of upper extremity motor function mirror rehabilitation system for post-stroke patients based on FES

An upper extremity motor function mirror rehabilitation method based on Functional Electrical Stimulation (FES) was proposed to provide subject-oriented rehabilitation training for hemiplegic patients with upper limb motor dysfunction. Firstly, recognize the motion pattern of the healthy side by Microsoft Kinect. The motion pattern consists of three kind of mode, i.e. slow speed mode, medium speed mode, fast speed mode. Secondly, the paralyzed upper joints were controlled to track the pattern of the healthy side by stimulating appropriate muscles. The stimulating intensity, i.e. the width of the current pulse, is controlled with iterative learning control method. Experiments on healthy subject indicated that this rehabilitation system could help post-stroke patients conduct customized subject-oriented training effectively.