A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network

Sensors

Zhijie Zhang¹  Rong Tang¹  Zhao Yang¹  Jiahuan Lu¹  Jie Bao¹ 

[1] National Key Laboratory of Air Traffic Flow Management, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;

关键词: trajectory prediction;    quadrotor;    gated recurrent unit;    position;   

DOI  :  10.3390/s20247061

来源: DOAJ

【 摘 要 】

This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.

【 授权许可】

Unknown