Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic

Fu, Qinbing, Sun, Xuelong, liu, Tian , Hu, Cheng and Yue, Shigang (2021) Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic. Frontiers in Robotics and AI, 8 . p. 529872. ISSN 22969144

Full content URL: https://doi.org/10.3389/frobt.2021.529872

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Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic
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Abstract

Collision prevention sets a major research and development obstacle for intelligent robots
and vehicles. This paper investigates the robustness of two state-of-the-art neural network
models inspired by the locust’s LGMD-1 and LGMD-2 visual pathways as fast and low-energy collision alert systems in critical scenarios. Although both the neural circuits have
been studied and modelled intensively, their capability and robustness against real-time
critical traffic scenarios where real-physical crashes will happen have never been
systematically investigated due to difficulty and high price in replicating risky traffic with
many crash occurrences. To close this gap, we apply a recently published robotic platform
to test the LGMDs inspired visual systems in physical implementation of critical traffic
scenarios at low cost and high flexibility. The proposed visual systems are applied as the
only collision sensing modality in each micro-mobile robot to conduct avoidance by abrupt
braking. The simulated traffic resembles on-road sections including the intersection and
highway scenes wherein the roadmaps are rendered by coloured, artificial pheromones
upon a wide LCD screen acting as the ground of an arena. The robots with light sensors at
bottom can recognise the lanes and signals, tightly follow paths. The emphasis herein is
laid on corroborating the robustness of LGMDs neural systems model in different dynamic
robot scenes to timely alert potential crashes. This study well complements previous
experimentation on such bio-inspired computations for collision prediction in more critical
physical scenarios, and for the first time demonstrates the robustness of LGMDs inspired
visual systems in critical traffic towards a reliable collision alert system under constrained
computation power. This paper also exhibits a novel, tractable, and affordable robotic
approach to evaluate online visual systems in dynamic scenes.

Keywords:bio-inspired computation, collision prediction, robust visual systems, LGMDs, micro-robot, critical robot traffic
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:46873
Deposited On:11 Oct 2021 10:47

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