Dynamic space debris removal via deep feature extraction and trajectory prediction in robotic systems - Zhang, Zhuyan; Zhang, Deli; Honarvar Shakibaei Asli, Barmak (MDPI, 2025)
This work introduces a comprehensive vision-based framework for autonomous space debris removal using robotic manipulators. A real-time debris detection module is built upon the YOLOv8 architecture, ensuring reliable target localization under varying illumination and occlusion conditions. Following detection, object motion states are estimated through a calibrated binocular vision system coupled with a physics-based collision model. Smooth interception trajectories are generated via a particle swarm optimization strategy integrated with a 5–5–5 polynomial interpolation scheme, enabling continuous and time-optimal end-effector motions. To anticipate future arm movements, a Transformer-based sequence predictor is enhanced by replacing conventional multilayer perceptrons with Kolmogorov–Arnold networks (KANs), improving both parameter efficiency and interpretability. In practice, the Transformer+KAN model compensates the manipulator’s trajectory planner to adapt to more complex scenarios. Each component is then evaluated separately in simulation, demonstrating stable tracking performance, precise trajectory execution, and robust motion prediction for intelligent on-orbit servicing.
This article belongs to the Section AI in Robotics
Keywords: 46 Information and Computing Sciences; 4007 Control Engineering, Mechatronics and Robotics; 40 Engineering; 4602 Artificial Intelligence; space debris; robotic arm serving; collision detection; particle swarm optimization; Kolmogorov–Arnold network