An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras

2025/06/19 by Coretti, Antonio Giulio, Varile, Mattia, Bertaina, Mario Edoardo
Engineering · Computer Science · #Space Satellite Systems and Control #Advanced Memory and Neural Computing #Age of Information Optimization

paper · pdf · doi:10.48550/arxiv.2506.16436

Abstract

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.

Citations

• Predicting the Probability of Collision of a Satellite with Space Debris: A Bayesian Machine Learning Approach
• Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach
• Event-based Object Detection and Tracking for Space Situational\n Awareness

Related

• Event-based Object Detection and Tracking for Space Situational\n Awareness
• Markers Identification for Relative Pose Estimation of an Uncooperative Target
• Cooperative Multi-spacecraft Observation of Incoming Space Threats
• Space Debris: Are Deep Learning-based Image Enhancements part of the Solution?
• Toward Onboard AI-Enabled Solutions to Space Object Detection for Space Sustainability