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

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ULTRACEPT researcher Dr Qinbing Fu recently published his journal article Fu, Qinbing, Sun, Xuelong, liu, Tian et al, Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic. Frontiers in Robotics and AI, 8 . p. 529872, 2021. ISSN 22969144. In this post, Dr Fu shares with us the highlights of this research.

Research Summary

Collision prevention sets a major research and development obstacle for intelligent robots and vehicles. This research 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 research also exhibits a novel, tractable, and affordable robotic approach to evaluate online visual systems in dynamic scenes.

Research Highlights

This research corroborates the robustness of LGMDs (Figure 1) neuronal systems model to timely alert potential crashes in dynamic multi-robot scenes. To sharpen up the acuity of LGMDs inspired visual systems in collision sensing, an original hybrid LGMD-1 and LGMD-2 neural networks model (Figure 2) is proposed with non-linear mapping from network outputs to alert firing rate, which works effectively.

Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic
FIGURE 1. Schematic illustration of the LGMD-1 and the LGMD-2 neuromorphology. Visual stimuli are received by the pre-synaptic dendrite structures of both neurons. The feed-forward inhibition (FFI) pathway connects the LGMD-1. The DCMD (descending contra-lateral movement detector) is a one-to-one post-synaptic target neuron to the LGMD-1 conveying spikes to motion control neural system. The post-synaptic partner neuron to the LGMD-2 yet remains unknown.
Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic
FIGURE 2. Schematic illustration of the proposed feed-forward collision prediction visual neural networks. There are three layers pre-synaptic to the two neurons, the photoreceptor (P), lamina (LA) and medulla (ME) layers. The pre-synaptic neural networks of LGMD-1 and LGMD-2 share the same visual processing in the first two, P and LA layers. The processing yet differs in the third ME layer for the purpose of separating their different selectivity. The ME layer consists of ON/OFF channels wherein the ON channels are rigorously suppressed in the LGMD-2’s circuit (dashed lines). The delayed information is formed by convolving surrounding non-delayed signals in space. The FFI is an individual inhibition pathway to merely the LGMD-1. The PM is a mediation pathway to the medulla layer of the LGMD-2. The two LGMDs pool their pre-synaptic signals respectively to generate spikes that are passed to their post-synaptic neurons. Notably, the non-linearly mapped, hybrid firing rate is the network output deciding the corresponding collision avoidance response.

This research complements previous experimentation on the proposed bio-inspired computation approach to collision prediction in more critical, real-physical scenarios.

This research exhibits an innovative, tractable, and affordable robotic approach to evaluate online visual systems in different dynamic scenes.

Research Platform

This research applies our developed robotic platform as shown below. The autonomous mobile robot used in this study is called Colias-IV (Hu et al., 2018), which includes mainly two components that provide different functions, namely the Colias Basic Unit (CBU), and the Colias Sensing Unit (CSU).

Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic
FIGURE 3. Overview of the robotic platform consisting of multiple-pheromone module and micro-mobile robots. The pheromone module is composed of a camera system connecting a computer and a TV arena. The micro-mobile robot comprises a visual sense board implementing the proposed visual systems, and a motion board for route following and emergency braking. Four colour sensors are marked in the bottom view of the robot used for sensing optically rendered pheromone cues displayed upon the LCD screen. The ID-pattern on top of robot is used to run a real time localisation system.

Supplementary Video

We have a supplemental video to explain this novel research outcomes.

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