{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/real-time-neuromorphic-navigation-integrating","title":"Real-Time Neuromorphic Navigation: Integrating Event-Based Vision and Physics-Driven Planning on a Parrot Bebop2 Quadrotor","arxiv_id":"2407.00931","date":"2024-07-01","proceeding":null,"authors":["Amogh Joshi","Sourav Sanyal","Kaushik Roy"],"abstract":"In autonomous aerial navigation, real-time and energy-efficient obstacle avoidance remains a significant challenge, especially in dynamic and complex indoor environments. This work presents a novel integration of neuromorphic event cameras with physics-driven planning algorithms implemented on a Parrot Bebop2 quadrotor. Neuromorphic event cameras, characterized by their high dynamic range and low latency, offer significant advantages over traditional frame-based systems, particularly in poor lighting conditions or during high-speed maneuvers. We use a DVS camera with a shallow Spiking Neural Network (SNN) for event-based object detection of a moving ring in real-time in an indoor lab. Further, we enhance drone control with physics-guided empirical knowledge inside a neural network training mechanism, to predict energy-efficient flight paths to fly through the moving ring. This integration results in a real-time, low-latency navigation system capable of dynamically responding to environmental changes while minimizing energy consumption. We detail our hardware setup, control loop, and modifications necessary for real-world applications, including the challenges of sensor integration without burdening the flight capabilities. Experimental results demonstrate the effectiveness of our approach in achieving robust, collision-free, and energy-efficient flight paths, showcasing the potential of neuromorphic vision and physics-driven planning in enhancing autonomous navigation systems.","url_abs":"https://arxiv.org/abs/2407.00931v1","url_pdf":"https://arxiv.org/pdf/2407.00931v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"real-time-neuromorphic-navigation-integrating","repo_url":"https://github.com/amoghj98/neuronav","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}