Research/ SPACE AI & AUTONOMY
An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras
Antonio Giulio Coretti, Mattia Varile, Mario Edoardo Bertaina
Learning-based MethodsarXiv: cs.LG · Motion Planning & NavigationOpenAlex topic: Robotic Path Planning Algorithms · Perception & State EstimationOpenAlex keyword: event cameras
arXivcs.LGOpenAlexAdvanced Neural Network Applications · Advanced Memory and Neural Computing · Robotic Path Planning Algorithms
Space debris poses a significant threat, driving research into active and passive mitigation strategies. This work presents an innovative collision avoidance system utilizing event-based cameras - a novel imaging technology well-suited for Space Situational Awareness (SSA) and Space Traffic Management (STM). The system, employing a Stack-CNN algorithm (previously used for meteor detection), analyzes real-time event-based camera data to detect faint moving objects. Testing on terrestrial data demonstrates the algorithm's ability to enhance signal-to-noise ratio, offering a promising approach for on-board space imaging and improving STM/SSA operations.
Comments: 18th International Conference on Space Operations - Safety and sustainability of Space Operations (SSU)