NeuroCopter: Neuromorphic Computation of 6D Ego-Motion of a Quadcopter

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.

[1]  R. Menzel.  The honeybee as a model for understanding the basis of cognition , 2012, Nature Reviews Neuroscience.

[2]  Thomas S. Collett,et al.  Memory use in insect visual navigation , 2002, Nature Reviews Neuroscience.

[3]  Astro Teller,et al.  Neural Programming and an Internal Reinforcement Policy , 1996 .

[4]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[5]  Holk Cruse,et al.  No Need for a Cognitive Map: Decentralized Memory for Insect Navigation , 2011, PLoS Comput. Biol..

[6]  Paul F. M. J. Verschure,et al.  IQR: a distributed system for real-time real-world neuronal simulation , 2002, Neurocomputing.

[7]  Randolf Menzel,et al.  Vector integration and novel shortcutting in honeybee navigation , 2012, Apidologie.

[8]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[9]  Yonina C. Eldar,et al.  Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration , 2009, Neural Computation.

[10]  R. Menzel,et al.  Honey bees navigate according to a map-like spatial memory. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Mandyam V. Srinivasan,et al.  Path integration in insects , 2003 .

[12]  Chi-Sang Poon,et al.  Neuromorphic Silicon Neurons and Large-Scale Neural Networks: Challenges and Opportunities , 2011, Front. Neurosci..

[13]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Johannes Schemmel,et al.  Six Networks on a Universal Neuromorphic Computing Substrate , 2012, Front. Neurosci..

[15]  R. Menzel,et al.  A Common Frame of Reference for Learned and Communicated Vectors in Honeybee Navigation , 2011, Current Biology.