Self-supervised learning of the biologically-inspired obstacle avoidance of hexapod walking robot

@article{ek2019SelfsupervisedLO,
  title={Self-supervised learning of the biologically-inspired obstacle avoidance of hexapod walking robot},
  author={Petr {\vC}{\'i}{\vz}ek and Jan Faigl},
  journal={Bioinspiration \& Biomimetics},
  year={2019},
  volume={14}
}
In this paper, we propose an integrated biologically inspired visual collision avoidance approach that is deployed on a real hexapod walking robot. The proposed approach is based on the Lobula giant movement detector (LGMD), a neural network for looming stimuli detection that can be found in visual pathways of insects, such as locusts. Although a superior performance of the LGMD in the detection of intercepting objects has been shown in many collision avoiding scenarios, its direct integration… 

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Collision avoidance using a model of the locust LGMD neuron

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