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Self-Adaptive Differential Evolution for Bio-Inspired Neuromorphic Collision Avoidance

Abstract · Apr 17, 2017 03:14 ·

evolution lgmd locust looming obstacle neuromorphic avoidance cs-ne

Arxiv Abstract

  • Llewyn Salt
  • David Howard

We present the optimisation of a neuromorphic adaptation of a spiking neural network model of the locust Lobula Giant Movement Detector (LGMD), which detects looming objects and can be used to facilitate obstacle avoidance in robotic applications. Our model is constrained by the parameters of a mixed signal analogue-digital neuromorphic device and is driven by the output of a neuromorphic vision sensor DVS. Due to the number of user-defined parameters and the difficulty to find values that perform well we investigate the use of Differential Evolution and self-adaptive DE (SADE) to find optimal values. We demonstrate that these optimisation algorithms are suitable candidates to find suitable parameters for an obstacle avoidance system on an unmanned aerial vehicle (UAV).

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