Title
NeuroCopter: neuromorphic computation of 6D ego-motion of a quadcopter
Abstract
The navigation capabilities of honeybees are surprisingly complex. Experimental evidence suggests that honeybees rely on a map-like neuronal representation of the environment. Intriguingly, a honeybee brain exhibits approximately one million neurons only. In an interdisciplinary enterprise, we are investigating models of high-level processing in the nervous system of insects such as spatial mapping and decision making. We use a robotic platform termed NeuroCopter that is controlled by a set of functional modules. Each of these modules initially represents a conventional control method and, in an iterative process, will be replaced by a neural control architecture. This paper describes the neuromorphic extraction of the copter's ego motion from sparse optical flow fields. We will first introduce the reader to the system's architecture and then present a detailed description of the structure of the neural model followed by simulated and real-world results.
Year
DOI
Venue
2013
10.1007/978-3-642-39802-5_13
Living Machines
Keywords
Field
DocType
neuromorphic computation,nervous system,functional module,high-level processing,experimental evidence,detailed description,neural control architecture,conventional control method,neural model,honeybee brain,ego motion
Architecture,Iterative and incremental development,Computer science,Biomimetics,Quadcopter,Neuromorphic engineering,Artificial intelligence,Artificial neural network,Optical flow,Computation
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
9
Name
Order
Citations
PageRank
Tim Landgraf1327.36
Benjamin Wild2183.43
Benjamin Wild3183.43
Tobias Ludwig400.34
Philipp Nowak500.34
Lovisa Helgadottir600.34
Philipp Breinlinger700.34
Martin Nawrot800.34
Raúl Rojas913719.71