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

In the recent years, different indoor local positioning techniques are proposed for robotics or UAV systems. This is due to the new research and industrial applications that they can cover. Assessing the performance and autonomous manoeuvring capability of the UAV in a dynamic and interactive indoor environment is challenging. To this end, this paper proposes a Performance Visualized Assessment (PVA) model to assess the performance quality of an autonomous UAV system in indoor environments. The PVA 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 UAV 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 UAV when operating navigation missions. The PVA 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.