Colias IV: The Affordable Micro Robot Platform with Bio-inspired Vision

Hu, Cheng; Fu, Qinbing; Yue, Shigang

doi:10.1007/978-3-319-96728-8_17

Cited by 24 publications

(15 citation statements)

References 18 publications

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“…Table 2 elucidates the parameters. In this study, we set up the parameters of neural networks depending on 1) prior knowledges from neuroscience (Rind and Bramwell, 1996;Simmons and Rind, 1997;Rind et al, 2016), 2) previous experience on modelling and experimenting of the LGMD-1 and the LGMD-2 neuron models (Fu et al, 2018b;Fu et al, 2019b), 3) considerations of fast implementation with optimisation as embedded vision systems for online visual processing (Hu et al, 2018). More concretely, the convolutional matrices W 1 , W 2 , W 3 , and W 4 are not only based on previous biological and computational studies, but also optimised by "bitwise operation" on the embedded system.…”

Section: Setting Network Parametersmentioning

confidence: 99%

“…As illustrated in Figure 3, 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). The CBU serves preliminary robot features such as motion, power management and some basic sensing like the bumper infra-red (IR) sensors in Figure 3.…”

Section: Micro-mobile Robotmentioning

confidence: 99%

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

Sun

Liu

et al. 2021

Front. Robot. AI

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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.

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Section: Setting Network Parametersmentioning

confidence: 99%

Section: Micro-mobile Robotmentioning

confidence: 99%

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

Sun

Liu

et al. 2021

Front. Robot. AI

Self Cite

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“…Moreover, the visual coverage of camera could reach up to 70 degrees. More detailed configuration of the Colias robot can be found in a recent work [39]. Fig.…”

Section: B Robot Configurationmentioning

confidence: 99%

Improved Collision Perception Neuronal System Model With Adaptive Inhibition Mechanism and Evolutionary Learning

Peng

et al. 2020

IEEE Access

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Accurate and timely perception of collision in highly variable environments is still a challenging problem for artificial visual systems. As a source of inspiration, the lobula giant movement detectors (LGMDs) in locust's visual pathways have been studied intensively, and modelled as quick collision detectors against challenges from various scenarios including vehicles and robots. However, the state-of-the-art LGMD models have not achieved acceptable robustness to deal with more challenging scenarios like the various vehicle driving scenes, due to the lack of adaptive signal processing mechanisms. To address this problem, we propose an improved neuronal system model, called LGMD + , that is featured by novel modelling of spatiotemporal inhibition dynamics with biological plausibilities including 1) lateral inhibitions with global biases defined by a variant of Gaussian distribution, spatially, and 2) an adaptive feed-forward inhibition mediation pathway, temporally. Accordingly, the LGMD + performs more effectively to detect merely approaching objects threatening head-on collision risks by appropriately suppressing motion distractors caused by vibrations, near-miss or approaching stimuli with deviations from the centre view. Through evolutionary learning with a systematic dataset of various crash and non-collision driving scenarios, the LGMD + shows improved robustness outperforming the previous related methods. After evolution, its computational simplicity, flexibility and robustness have also been well demonstrated by real-time experiments of autonomous micro-mobile robots.

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“…In this research, the micro robot platform Colias IV is utilized [17]. Colias IV is a micro ground robot occupying a circular footprint bearing the diameter of 4 cm.…”

Section: The Robot Platform and Experiments Arena Configurationmentioning

confidence: 99%

A Hybrid Visual-Model Based Robot Control Strategy for Micro Ground Robots

Liu

et al. 2018

From Animals to Animats 15

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This paper proposed a hybrid vision-based robot control strategy for micro ground robots by mediating two vision models from mixed categories: a bio-inspired collision avoidance model and a segmentation based target following model. The implemented model coordination strategy is described as a probabilistic model using finite state machine (FSM) that allows the robot to switch behaviours adapting to the acquired visual information. Experiments demonstrated the stability and convergence of the embedded hybrid system by real robots, including the studying of collective behaviour by a swarm of such robots with environment mediation. This research enables micro robots to run visual models with more complexity. Moreover, it showed the possibility to realize aggregation behaviour on micro robots by utilizing vision as the only sensing modality from non-omnidirectional cameras.

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Colias IV: The Affordable Micro Robot Platform with Bio-inspired Vision

Cited by 24 publications

References 18 publications

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

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

Improved Collision Perception Neuronal System Model With Adaptive Inhibition Mechanism and Evolutionary Learning

A Hybrid Visual-Model Based Robot Control Strategy for Micro Ground Robots

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