Neural based obstacle avoidance with CPG controlled hexapod walking robot

Čížek, Petr; Milicka, Pavel; Faigl, Jan

doi:10.1109/ijcnn.2017.7965914

Cited by 12 publications

(10 citation statements)

References 19 publications

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“…The LGMD-1 (namely LGMD in this paper) was firstly investigated as many quick collision-detecting visual systems with different theories shaping its specific collision selectivity (e.g., [3], [14], [20], [21], [36]). Some methods have been successfully applied in ground robots [25], [37]- [41], and UAV [26], [27]. Recently, an LGMD's neighbouring partner -the LGMD-2, with unique responsive preference to only darker approaching objects relative to the background, has also been built as quick collision selective neuronal system models with implementation in micro-robots [23], [24], [42], [43].…”

Section: A Bio-inspired Vision For Collision Perceptionmentioning

confidence: 99%

“…More precisely, the first firing of left or right-side LGMD guides the reactive avoidance to the right or left, after the perception. Recently, a more complex learning based control strategy was successfully combined with the LGMD in a hexapod walking robot, with validation in interception avoidance scenarios [25], [41]. Importantly, this work presents an end-to-end structure of bio-inspired neural networks connecting both the perception and avoidance steps.…”

Section: B Lgmd Modelsmentioning

confidence: 99%

“…Although the LGMD's efficacy has been validated with challenges from different scenarios including the ground vehicles [11]- [17], robots [18]- [25], and unmanned aerial vehicles (UAV) [26], [27], the models have been tested mostly with indoor (lab) scenes, rarely with on-road or outdoor situations. The robustness to deal with highly variable statistics of complex environments has not achieved an acceptable level, due to the lack of adaptive signal processing mechanisms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: A Bio-inspired Vision For Collision Perceptionmentioning

confidence: 99%

Section: B Lgmd Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Peng

et al. 2020

IEEE Access

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“…Learning from the locust's LGMDs visual pathways and circuits, there have been many modelling studies to investigate either the LGMD-1 or the LGMD-2 against various visual scenes including online, wheeled mobile robots (Blanchard et al, 2000;Yue and Rind, 2005;Badia et al, 2010;Fu et al, 2016;Fu et al, 2017;Fu et al, 2018b;Isakhani et al, 2018;Fu et al, 2019b), walking robot (Cizek et al, 2017;Cizek and Faigl, 2019), UAVs (Salt et al, 2017, Salt et al, 2019Zhao et al, 2019), and off-line car driving scenarios, e.g. (Keil et al, 2004;Stafford et al, 2007;Krejan and Trost, 2011;Hartbauer, 2017;Fu et al, 2019a, Fu et al, 2020a.…”

Section: Introductionmentioning

confidence: 99%

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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“…In the past decade, the LGMD1 neuron has been modelled with a good number of studies and tested in ground robots, e.g. [16]- [19], and recently in UAVs [20], [21]. These LGMD1-based modelling studies have demonstrated that the biological visual systems can be good paradigms to develop energy-efficient and reliable collision detection visual systems for real-world applications.…”

Section: Introductionmentioning

confidence: 99%

A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects

Peng

et al. 2020

IEEE Trans. Cybern.

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Building an efficient and reliable collision perception visual system is a challenging problem for future robots and autonomous vehicles. The biological visual neural networks, which have evolved over millions of years in nature, and are working perfectly in the real world, could be ideal models for designing artificial vision systems. In the locust's visual pathways, a lobula giant movement detector, i.e. the LGMD2, has been identified as a looming perception neuron that responds most strongly to darker approaching objects relative to their backgrounds, similar situations which many ground vehicles and robots are often facing with. However, little has been done on modelling the LGMD2 and investigating its potential in robotics and vehicles. In this research, we build an LGMD2 visual neural network which possesses the similar collision selectivity of an LGMD2 neuron in locust, via the modelling of biased-ON and OFF pathways splitting visual signals into parallel ON/OFF channels. With stronger-inhibition (bias) in the ON pathway, this model responds selectively to darker looming objects. The proposed model has been tested systematically with a range of stimuli including real-world scenarios. It has also been implemented in a micro mobile robot and tested with real-time experiments. The experimental results have verified the effectiveness and robustness of the proposed model for detecting darker looming objects against various dynamic and cluttered backgrounds.

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Neural based obstacle avoidance with CPG controlled hexapod walking robot

Cited by 12 publications

References 19 publications

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

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

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

A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects

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