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Autonomous Flying With Neuromorphic Sensing.
Parlevliet, Patricia P; Kanaev, Andrey; Hung, Chou P; Schweiger, Andreas; Gregory, Frederick D; Benosman, Ryad; de Croon, Guido C H E; Gutfreund, Yoram; Lo, Chung-Chuan; Moss, Cynthia F.
Afiliação
  • Parlevliet PP; Central Research and Technology, Airbus, Munich, Germany.
  • Kanaev A; U.S. Office of Naval Research Global, London, United Kingdom.
  • Hung CP; United States Army Research Laboratory, Aberdeen Proving Ground, Maryland, MD, United States.
  • Schweiger A; Airbus Defence and Space GmbH, Manching, Germany.
  • Gregory FD; U.S. Army Research Laboratory, London, United Kingdom.
  • Benosman R; Department of Bioengineering, Imperial College London, London, United Kingdom.
  • de Croon GCHE; Institut de la Vision, INSERM UMRI S 968, Paris, France.
  • Gutfreund Y; Biomedical Science Tower, University of Pittsburgh, Pittsburgh, PA, United States.
  • Lo CC; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States.
  • Moss CF; Micro Air Vehicle Laboratory, Department of Control and Operations, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands.
Front Neurosci ; 15: 672161, 2021.
Article em En | MEDLINE | ID: mdl-34054420
Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Neurosci Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Neurosci Ano de publicação: 2021 Tipo de documento: Article