Non Cooperative Target

Research on non-cooperative targets focuses on autonomously detecting, tracking, and interacting with objects whose behavior and characteristics are unknown, a crucial challenge in domains like space debris removal and satellite servicing. Current efforts leverage computer vision, particularly convolutional neural networks (CNNs) like YOLO and Faster R-CNN, along with advanced control algorithms such as model predictive control (MPC) and online learning methods, to enable robust and efficient operations. These advancements are vital for improving space situational awareness, enabling autonomous rendezvous and docking, and facilitating safer and more sustainable space operations.