Adaptive Model Predictive Control-Driven Approach for Visual Detection of Micro- UAVs
Gunasekaran Raja, Selvam Essaky, Deepak Suresh Rajendran, Sai Ganesh Senthivel, Sebastian B. Knorr, Kapal Dev · 2024
Unmanned Aerial Vehicles (UAVs) provide a good base platform for dynamic vision-based target detection. The detection process can be enhanced by utilizing multiple UAVs. In such scenarios, precise trajectory planning is essential to achieve objectives while avoiding collisions with obstacles. Furthermore, in most cases, Air-to-Air (A2A) detection of micro-UAVs in large-scale environments is challenging due to their dynamic movements and other complex parameters, such as poor light, motion blur, and occlusion. To solve these challenges, developing an algorithm that can adapt the control strategy based on changing system and environmental parameters is essential. This paper proposes an Adaptive Model Predictive Control (AMPC)-driven hybrid GANYOLOX framework to mitigate these navigation and detection challenges. The proposed AMPC allows model updates during multi-UAV operations, which enables estimation techniques to predict changes in the system model and helps to compute the target UAV position. Alternatively, the framework constructs a hybrid GAN-YOLOX model to overcome various A2A detection challenges. Extensive comparative analysis of the hybrid GANYOLOX framework achieved 94% detection accuracy and out-performed existing DL frameworks by 8%.