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

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 traf

SUBMITTER: Fu Q 

PROVIDER: S-EPMC8378452 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature