Multisensory Control of Stabilization Reflexes

Abstract

Besides the academic outputs produced during the period of funding and probably beyond one of the most important outcomes over the last couple of year has been a clear research agenda for this work. While collaborating with the groups of Graham Taylor and Sean Humbert this research has shaped into a system neuroscience approach that combines quantitative behavior, electrophysiology, functional anatomy and modeling to advance our understanding of biological control design with a strong emphasis on the underlying neuronal mechanisms. In particular the interactions with Sean Humbert have resulted in several tangible outputs, e.g. the proof of concept study showing that LPTC receptive fields can be successfully used to control orientation, proximity and forward speed of a quadropter (O35) or the development of an ocellar senor (C6). Future interactions between the group and the groups of collaborators will aim to further discover and exploit biological design principles in the context of control and navigation of autonomous robotic systems. The emphasis will be to advance autonomy and manoeuvrability by multisensor fusion, dynamic range fractioning and the integration of inner- and outer loop control.

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Document Details

Document Type
Technical Report
Publication Date
Aug 22, 2012
Accession Number
ADA566261

Entities

People

  • Holger G. Krapp

Organizations

  • Imperial College London

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Bioengineering
  • Cells
  • Closed Loop Systems
  • Collision Avoidance
  • Control Systems
  • Control Systems Engineering
  • Coordinate Systems
  • Detection
  • Detectors
  • Energy Consumption
  • Information Processing
  • Lepidoptera
  • Navigation
  • Nervous System
  • Neurons
  • Neurosciences

Readers

  • Systems Analysis and Design
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy
  • Autonomy - Autonomous System Control
  • Autonomy - Human-Robot Interaction