A Real-Time Autonomous Flight Navigation Trajectory Assessment for Unmanned Aerial Vehicles

Abstract

In the recent years, different indoor local positioning techniques are proposed for robotics or UA V systems. This is due to the new research and industrial applications that they can cover. Assessing the performance and autonomous manoeuvring capability of the UA V in a dynamic and interactive indoor environment is challenging. To this end, this paper proposes a Performance Visualized Assessment (PV A) model to assess the performance quality of an autonomous UA V system in indoor environments. The PV A model includes Chi-square Inference (CSI) module and Visualized Mission Grid (VMG) map. The CSI has an optical flow indoor trajectory tracking and localization technique. It estimates the UA V flying positioning indoor without the GPS service. The VMG map has a visualized domain knowledge of the environment and the navigation mission scenario. The PVA model checks and visualizes the trajectory and the behaviour of the UA V when operating navigation missions. The PV A model is applied to track and assess the performance of a quadrotor UAV in real-Time search missions. The results show the ability of the model to estimate and visualize the performance quality of the search missions with convenient accuracy. It narrows down the needed parameters of a critical assessment and reduces a human supervisor workload while monitoring the system's performance. © 2018 IEEE.