Lobula plate/lobula columnar, type 2 (LPLC2) visual projection neurons in the fly’s visual system possess highly looming-selective properties, making them ideal for developing artificial collision detection systems. The four dendritic branches of individual LPLC2 neurons, each tuned to specific directional motion, enhance the robustness of looming detection by utilizing radial motion opponency. Existing models of LPLC2 neurons either concentrate on individual cells to detect centroid-focused expansion or utilize population-voting strategies to obtain global collision information. However, their potential for addressing multi-target collision scenarios remains largely untapped. In this study, we propose a numerical model for LPLC2 populations, leveraging a bottom-up attention mechanism driven by motion-sensitive neural pathways to generate attention fields (AFs). This integration of AFs with highly nonlinear LPLC2 responses enables precise and continuous detection of multiple looming objects emanating from any region of the visual field. We began by conducting comparative experiments to evaluate the proposed model against two related models, highlighting its unique characteristics. Next, we tested its ability to detect multiple targets in dynamic natural scenarios. Finally, we validated the model using real-world video data collected by aerial robots. Experimental results demonstrate that the proposed model excels in detecting, distinguishing, and tracking multiple looming targets with remarkable speed and accuracy. This advanced ability to detect and localize looming objects, especially in complex and dynamic environments, holds great promise for overcoming collision-detection challenges in mobile intelligent machines.

Amari’s Dynamic Neural Field (DNF) framework provides a brain-inspired approach to modeling the average activation of neuronal groups. Leveraging a single field, DNF has become a promising foundation for low-energy looming perception module in robotic applications. However, the previous DNF methods face significant challenges in detecting incoherent or inconsistent looming features—conditions commonly encountered in real-world scenarios, such as collision detection in rainy weather. Insights from the visual systems of fruit flies and locusts reveal encoding ON/OFF visual contrast plays a critical role in enhancing looming selectivity. Additionally, lateral excitation mechanism potentially refines the responses of loom-sensitive neurons to both coherent and incoherent stimuli. Together, these offer valuable guidance for improving looming perception models. Building on these biological evidence, we extend the previous single-field DNF framework by incorporating the modeling of ON/OFF visual contrast, each governed by a dedicated DNF. Lateral excitation within each ON/OFF-contrast field is formulated using a normalized Gaussian kernel, and their outputs are integrated in the Summation field to generate collision alerts. Experimental evaluations show that the proposed model effectively addresses incoherent looming detection challenges and significantly outperforms state-of-the-art locust-inspired models. It demonstrates robust performance across diverse stimuli, including synthetic rain effects, underscoring its potential for reliable looming perception in complex, noisy environments with inconsistent visual cues.

Vision, utilizing eyes or cameras as the predominant sensory input, emerges as the primary and informative perception source for both animals and robots, facilitating crucial functions such as perception, navigation, interaction, and comprehensive understanding of their surroundings. Inspired by insect-like invertebrate animals’ remarkable visual processing abilities which are achieved despite their constrained computational resources, this paper delves into the realm of bio-inspired autonomous navigation. The objective is to tackle the substantial challenges of cost efficiency, ensuring a solely vision-based algorithm guides the agent to its destination without collisions. To this end, a vision-guided navigation model is proposed by orchestrating the neural model of ant’s visual navigation and crab’s looming spatial localization. As the exploratory endeavor in constructing a bio-inspired autonomous visual navigation model, which demands significantly lower computational resources in comparison to prevailing engineering solutions. The effectiveness of this model has been validated through rigorous systematic experiments, lending empirical support to its capabilities. The proposed vision-motion closed-loop framework imparts valuable insights for the development of more efficient and precise autonomous systems, embodying the principle of drawing inspiration from nature’s wisdom.

Autonomous navigation is an essential capability for both robots and animals, enabling them to achieve their locomotion objectives within specific environments. Drawing inspirations from nature, there has been recent introduction of an insect-inspired algorithm for autonomous navigation. This model integrates global working memory inspired by sweat bee path integration (PI) and local cues derived from the locust giant motion detector (LGMD). Experimental findings provide evidence of the effectiveness of this integrated approach in addressing navigation tasks, involving both stationary and moving obstacles. Furthermore, the algorithm demonstrates efficient computation, independent of external data or environment dependency. However, there is scope for improvement concerning collision avoidance and autonomous navigation performance. To address these concerns, two solutions are proposed in this study: 1) employing the Directional Selective Neuron (DSN) model as an alternative to LGMD for local cues, offering collision signals and visual object motion direction to enhance collision avoidance motion control, and 2) introducing a fusion mechanism that integrates DSN output with PI global memory to enhance navigation performance. Simulation data reveals the following outcomes: 1) in static environments, complete avoidance of collisions during foraging-homing and a significant decrease in navigation task completion time, and 2) in dynamic environments, notably improved collision avoidance robustness and navigation efficiency. These results effectively demonstrate the enhancement achieved through the adoption of the proposed bio-inspired method and exemplify the principle of learning from nature.

There are primarily two classes of bio-inspired looming perception visual systems. The first class employs hierarchical neural networks inspired by well-acknowledged anatomical pathways responsible for looming perception, and the second maps nonlinear relationships between physical stimulus attributes and neuronal activity. However, even with multi-layered structures, the former class is sometimes fragile in looming selectivity, i.e., the ability to well discriminate between approaching and other categories of movements. While the latter class leaves qualms regarding how to encode visual movements to indicate physical attributes like angular velocity/size. Beyond those, we propose a novel looming perception model based on dynamic neural field (DNF). The DNF is a brain-inspired framework that incorporates both lateral excitation and inhibition within the field through instant feedback, it could be an easily-built model to fulfill the looming sensitivity observed in biological visual systems. To achieve our target of looming perception with computational efficiency, we introduce a single-field DNF with adaptive lateral interactions and dynamic activation threshold. The former mechanism creates antagonism to translating motion, and the latter suppresses excitation during receding. Accordingly, the proposed model exhibits the strongest response to moving objects signaling approaching over other types of external stimuli. The effectiveness of the proposed model is supported by relevant mathematical analysis and ablation study. The computational efficiency and robustness of the model are verified through systematic experiments including on-line collision-detection tasks in micro-mobile robots, at success rate of 93% compared with state-of-the-art methods. The results demonstrate its superiority over the model-based methods concerning looming perception.

Accurate perception of collision acts as the basis of artificial vision systems for autonomous collision detection and avoidance. The lobula giant motion detector (LGMD) in the locust's brain is a natural and simple detector responding most strongly to images of approaching objects, which has been extensively studied and modeled. However, noisy signals greatly obstruct the LGMD models upon reliability and robustness. To address this problem, the existing models incorporate additional pre-processing methods or network layers which, nevertheless, further increase the computational complexity of LGMD models. On the other hand, the signal processing within neurons and synapses has been demonstrated as non-deterministic, and this may help neural computation resist noise. This raises the question that whether LGMD incorporated with indeterminacy realized by probabilistic mechanism will improve its anti-noise performance. This paper investigates this point with a probabilistic LGMD model, an attempt to conform to biological facts. The probabilistic model introduces indeterminacy to signal operations including transmission, interaction, integration between multiple layers of a classical LGMD neural network. The proposed model has been systematically tested with a range of stimuli including simple scenes with artificial noise, and real-world complex scenes. We intriguingly found that the performance of LGMD on the aspect of combating noise has been significantly improved, demonstrating that adding a probabilistic mechanism is an easy but effective way of fighting noisy signals in collision perception.

This paper investigates the coordination of multiple looming sensitive neuronal models to get closer to the ultra-selectivity represented by organisms, i.e., responding to merely approaching objects. In locust's brain, a group of lobula giant movement detectors (LGMDs) cooperate very well in collision perception. Although the existing single neuron computation can portray individual LGMDs relatively well, it seems to be insufficient to handle all complex situations as stimuli like receding and translating still greatly stimulate the models. Compounding is a method of biological problem-solving, therefore, this paper conjectures that there is signal deepening between LGMDs. We propose a composite model by deepening the forward dendritic circuitry of existing LGMD models. Two types of ensemble herein are investigated with which the LGMD1 cascaded with LGMD2 is named LGMD1-LGMD2, and the LGMD2 cascaded with LGMD1 is named LGMD2-LGMD1. We carry out systematic experiments to test our hypothesis. The results demonstrate that compared with the single neuron models, the proposed composite model features extreme looming selectivity to only approaching objects over other categories of movements. Specifically, the selectivity of either LGMD1 or LGMD2 has been mutually enhanced built upon their original selectivity, i.e., the LGMD1-LGMD2 ensemble is sensitive to approaching white and dark objects, not to any receding or translating stimuli; while the LGMD2-LGMD1 ensemble only responds to approaching dark objects. Accordingly, this research provides a novel case study on the ensemble of motion perception neuronal models to achieve stronger selectivity that a single neuron model cannot accomplish.

Physiological studies have shown that a group of locust's lobula giant movement detectors (LGMDs) has a diversity of collision selectivity to approaching objects, relatively darker or brighter than their backgrounds in cluttered environments. Such diversity of collision selectivity can serve locusts to escape from attack by natural enemies, and migrate in swarm free of collision. For computational studies, endeavours have been made to realize the diverse selectivity which, however, is still one of the most challenging tasks especially in complex and dynamic real world scenarios. The existing models are mainly formulated as multi-layered neural networks with merely feed-forward information processing, and do not take into account the effect of re-entrant signals in feedback loop, which is an essential regulatory loop for motion perception, yet never been explored in looming perception. In this paper, we inaugurate feedback neural computation for constructing a new LGMD-based model, named F-LGMD to look into the efficacy upon implementing different collision selectivity. Accordingly, the proposed neural network model features both feed-forward processing and feedback loop. The feedback control propagates output signals of parallel ON/OFF channels back into their starting neurons, thus makes part of the feed-forward neural network, i.e. the ON/OFF channels and the feedback loop form an iterative cycle system. Moreover, the feedback control is instantaneous, which leads to the existence of a fixed point whereby the fixed point theorem is applied to rigorously derive valid range of feedback coefficients. To verify the effectiveness of the proposed method, we conduct systematic experiments covering synthetic and natural collision datasets, and also online robotic tests. The experimental results show that the F-LGMD, with a unified network, can fulfil the diverse collision selectivity revealed in physiology, which not only reduces considerably the handcrafted parameters compared to previous studies, but also offers a both efficient and robust scheme for collision perception through feedback neural computation.

Being one of the most fundamental and crucial capacity of robots and animals, autonomous navigation that consists of goal approaching and collision avoidance enables completion of various tasks while traversing different environments. In light of the impressive navigational abilities of insects despite their tiny brains compared to mammals, the idea of seeking solutions from insects for the two key problems of navigation, i.e., goal approaching and collision avoidance, has fascinated researchers and engineers for many years. However, previous bio-inspired studies have focused on merely one of these two problems at one time. Insect-inspired navigation algorithms that synthetically incorporate both goal approaching and collision avoidance, and studies that investigate the interactions of these two mechanisms in the context of sensory-motor closed-loop autonomous navigation are lacking. To fill this gap, we propose an insect-inspired autonomous navigation algorithm to integrate the goal approaching mechanism as the global working memory inspired by the sweat bee's path integration (PI) mechanism, and the collision avoidance model as the local immediate cue built upon the locust's lobula giant movement detector (LGMD) model. The presented algorithm is utilized to drive agents to complete navigation task in a sensory-motor closed-loop manner within a bounded static or dynamic environment. Simulation results demonstrate that the synthetic algorithm is capable of guiding the agent to complete challenging navigation tasks in a robust and efficient way. This study takes the first tentative step to integrate the insect-like navigation mechanisms with different functionalities (i.e., global goal and local interrupt) into a coordinated control system that future research avenues could build upon.

In this paper, a numerical neural network inspired by the lobula plate/lobula columnar type II (LPLC2), the ultra-selective looming sensitive neurons identified within visual system of Drosophila, is proposed utilising non-linear computation. This method aims to be one of the explorations towards solving the collision perception problem resulted from radial motion. Taking inspiration from the distinctive structure and placement of directionally selective neurons (DSNs) named T4/T5 interneurons and their post-synaptic neurons, the motion opponency along four cardinal directions is computed in a non-linear way and subsequently mapped into four quadrants. More precisely, local motion excites adjacent neurons ahead of the ongoing motion, whilst transfers inhibitory signals to presently-excited neurons with slight temporal delay. From comparative experimental results collected, the main contribution is established by sculpting the ultra-selective features of generating a vast majority of responses to dark centroid-emanated centrifugal motion patterns whilst remaining nearly silent to those starting from other quadrants of receptive field (RF). The proposed method also distinguishes relatively dark approaching objects against brighter background and light ones against dark background via exploiting ON/OFF parallel channels, which well fits the physiological findings. Accordingly, the proposed neural network consolidates the theory of non-linear computation in Drosophila's visual system, a prominent paradigm for studying biological motion perception. This research also demonstrates potential of being fused with attention mechanism towards utility in devices such as unmanned aerial vehicles (UAVs), protecting them from unexpected and imminent collision by calculating a safer flying pathway.

The perception of looming objects moving in depth is a basis of artificially dynamic vision system, which has been widely used in robots for autonomous obstacle detection-and-avoidance. How to reliably detect looming objects in chaotic environments is prerequisite, however, still a challenging problem. The current looming perception models or neural networks are greatly affected by input variability on visual contrast between looming object and its background. In this case, the responses of looming detection neurons in animals are robust, which suggests that contrast cues are well encoded in biological visual neural pathways. Considering the physiological homology between Drosophila and locust, this paper draws lessons from the progress of Drosophila physiology to improve the current locust-inspired looming perception model. Two contrast computation schemes herein are proposed: (1) In the early stage of visual processing, the instantaneous feedback mechanism based contrast normalisation dynamically suppress the preliminary motion signals with respect to time. (2) In the later stage of processing, a parallel channel dedicated to calculating local contrast of motion signal is converged to weaken high-contrast signals. Through the comparative tests against many pure and natural scenes, the proposed method works effectively and robustly to reduce fluctuation and variance of response against high input variability on contrast. Here we highlight the effectiveness of temporally dynamic suppression to motion signals in the proposed neural network model, which significantly improves the fidelity of looming perception. This study also shows its competitiveness in the repository of bio-inspired looming perception models.

In nature, crabs have a panoramic vision for the localization and perception of accelerating motion from local segments to global view in order to guide reactive behaviours including escape. The visual neuronal ensemble in crab plays crucial roles in such capability, however, has never been investigated and modelled as an artificial vision system. To bridge this gap, we propose an accelerating motion perception model (AMPM) mimicking the visual neuronal ensemble in crab. The AMPM includes two main parts, wherein the pre-synaptic network from the previous modelling work simulates 16 MLGI neurons covering the entire view to localize moving objects. The emphasis herein is laid on the original modelling of MLGIs' post-synaptic network to perceive accelerating motions from a global view, which employs a novel spatial-temporal difference encoder (STDE), and an adaptive spiking threshold temporal difference encoder (AT-TDE). Specifically, the STDE transforms “time-to-travel” between activations of two successive segments of MLG1 into excitatory post-synaptic current (EPSC), which decays with the elapse of time. The AT-TDE in two directional, i.e., counter-clockwise and clockwise accelerating detectors guarantees “non-firing” to constant movements. Accordingly, the accelerating motion can be effectively localized and perceived by the whole network. The systematic experiments verified the feasibility and robustness of the proposed method. The model responses to translational accelerating motion also fit many of the explored physiological features of direction selective neurons in the lobula complex of crab (i.e. lobula complex direction cells, LCDCs). This modelling study not only provides a reasonable hypothesis for such biological neural pathways, but is also critical for developing a new neuromorphic sensor strategy.

This paper describes a versatile platform for swarm robotics research. It integrates multiple pheromone communication with a dynamic visual scene along with real time data transmission and localization of multiple-robots. The platform has been built for inquiries into social insect behavior and bio-robotics. By introducing a new research scheme to coordinate olfactory and visual cues, it not only complements current swarm robotics platforms which focus only on pheromone communications by adding visual interaction, but also may fill an important gap in closing the loop from bio-robotics to neuroscience. We have built a controllable dynamic visual environment based on our previously developed ColCOSΦ (a multi-pheromones platform) by enclosing the arena with LED panels and interacting with the micro mobile robots with a visual sensor. In addition, a wireless communication system has been developed to allow transmission of real-time bi-directional data between multiple micro robot agents and a PC host. A case study combining concepts from the internet of vehicles (IoV) and insect-vision inspired model has been undertaken to verify the applicability of the presented platform, and to investigate how complex scenarios can be facilitated by making use of this platform.

This paper aims at addressing a challenging problem on reliably estimating image motion against highly variable natural signals which artificial dynamic vision systems are faced with. Previously, the visual system response always represents fluctuation and high variance influenced by spatial contrast, the local difference between neighbouring luminance values. Effective contrast computation is therefore a prerequisite for robust motion vision. In this regard, sighted animals such as flies are remarkably adept at estimating image motion regardless of image statistics by rapidly adjusting contrast sensitivity. Current artificial visual systems, however, cannot account for this capability. Learning from neuroscience, here we propose contrast vision computation to improve a state-of-the-art, bio-inspired neural network model for background motion estimation. This includes mainly two neural computation schemes of (1) an instantaneous, feedback divisive contrast normalisation prior to motion correlation in the ON and OFF pathways to reduce local contrast sensitivity, (2) parallel contrast pathways influencing ON/OFF motion signals, negatively, at the pooling output layer to suppress high-contrast optic flows. We created a dataset of many shifting natural images with high input variability to investigate the proposed method. The experiments have demonstrated the effectiveness and robustness of the proposed contrast vision computation to reduce response fluctuation and variance against natural signals. The fidelity of motion perception thus has been significantly increased. The proposed methods could be generic to other motion vision models dealing with high-contrast visual scenes.

Currently, collision detection methods based on visual cues are still challenged by several factors including ultrafast approaching velocity and noisy signal. Taking inspiration from nature, though the computational models of lobula giant movement detectors (LGMDs) in locust's visual pathways have demonstrated positive impacts on addressing these problems, there remains potential for improvement. In this paper, we propose a novel method mimicking neuronal refractoriness, i.e. the refractory period (RP), and further investigate its functionality and efficacy in the classic LGMD neural network model for collision perception. Compared with previous works, the two phases constructing RP, namely the absolute refractory period (ARP) and relative refractory period (RRP) are computationally implemented through a ‘link (L) layer’ located between the photoreceptor and the excitation layers to realise the dynamic characteristic of RP in discrete time domain. The L layer, consisting of local time-varying thresholds, represents a sort of mechanism that allows photoreceptors to be activated individually and selectively by comparing the intensity of each photoreceptor to its corresponding local threshold established by its last output. More specifically, while the local threshold can merely be augmented by larger output, it shrinks exponentially over time. Our experimental outcomes show that, to some extent, the investigated mechanism not only enhances the LGMD model in terms of reliability and stability when faced with ultra-fast approaching objects, but also improves its performance against visual stimuli polluted by Gaussian or Salt-Pepper noise. This research demonstrates the modelling of refractoriness is effective in collision perception neuronal models, and promising to address the aforementioned collision detection challenges.

Efficient and robust motion perception systems are important pre-requisites for achieving visually guided flights in future micro air vehicles. As a source of inspiration, the visual neural networks of flying insects such as honeybee and Drosophila provide ideal examples on which to base artificial motion perception models. In this paper, we have used this approach to develop a novel method that solves the fundamental problem of estimating angular velocity for visually guided flights. Compared with previous models, our elementary motion detector (EMD) based model uses a separate texture estimation pathway to effectively decode angular velocity, and demonstrates considerable independence from the spatial frequency and contrast of the gratings. Using the Unity development platform the model is further tested for tunnel centering and terrain following paradigms in order to reproduce the visually guided flight behaviors of honeybees. In a series of controlled trials, the virtual bee utilizes the proposed angular velocity control schemes to accurately navigate through a patterned tunnel, maintaining a suitable distance from the undulating textured terrain. The results are consistent with both neuron spike recordings and behavioral path recordings of real honeybees, thereby demonstrating the model's potential for implementation in micro air vehicles which have only visual sensors.

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 (LGMD), that is, 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 faced with. However, little has been done on modeling the LGMD2 and investigating its potential in robotics and vehicles. In this article, we build an LGMD2 visual neural network which possesses the similar collision selectivity of an LGMD2 neuron in locust via the modeling 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.

Insects use visual information to estimate angular velocity of retinal image motion, which determines a variety of flight behaviours including speed regulation, tunnel centring and visual navigation. For angular velocity estimation, honeybees show large spatial-independence against visual stimuli, whereas the previous models have not fulfilled such an ability. To address this issue, we propose a bio-plausible model for estimating the image motion velocity based on behavioural experiments of the honeybee flying through patterned tunnels. The proposed model contains mainly three parts, the texture estimation layer for spatial information extraction, the delay-and-correlate layer for temporal information extraction and the decoding layer for angular velocity estimation. This model produces responses that are largely independent of the spatial frequency in grating experiments. And the model has been implemented in a virtual bee for tunnel centring simulations. The results coincide with both electro-physiological neuron spike and behavioural path recordings, which indicates our proposed method provides a better explanation of the honeybee's image motion detection mechanism guiding the tunnel centring behaviour.

The robust detection of small targets against cluttered background is important for future artificial visual systems in searching and tracking applications. The insects' visual systems have demonstrated excellent ability to avoid predators, find prey or identify conspecifics - which always appear as small dim speckles in the visual field. Build a computational model of the insects' visual pathways could provide effective solutions to detect small moving targets. Although a few visual system models have been proposed, they only make use of small-field visual features for motion detection and their detection results often contain a number of false positives. To address this issue, we develop a new visual system model for small target motion detection against cluttered moving backgrounds. Compared to the existing models, the small-field and wide-field visual features are separately extracted by two motion-sensitive neurons to detect small target motion and background motion. These two types of motion information are further integrated to filter out false positives. Extensive experiments showed that the proposed model can outperform the existing models in terms of detection rates.

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

Vibro-driven robotic (VDR) systems use stick-slip motions for locomotion. Due to the underactuated nature of the system, efficient design and control are still open problems. We present a new energy preserving design based on a spring-augmented pendulum. We indirectly control the friction-induced stick-slip motions by exploiting the passive dynamics in order to achieve an improvement in overall travelling distance and energy efficiency. Both collocated and non-collocated constraint conditions are elaborately analysed and considered to obtain a desired trajectory generation profile. For tracking control, we develop a partial feedback controller for the driving pendulum which counteracts the dynamic contributions from the platform. Comparative simulation studies show the effectiveness and intriguing performance of the proposed approach, while its feasibility is experimentally verified through a physical robot. Our robot is to the best of our knowledge the first nonlinear-motion prototype in literature towards the VDR systems.

The nature endows animals robust vision systems for extracting and recognizing different motion cues, detecting predators, chasing preys/mates in dynamic and cluttered environments. Direction selective neurons (DSNs), with preference to certain orientation visual stimulus, have been found in both vertebrates and invertebrates for decades. In this paper, with respect to recent biological research progress in motion-detecting circuitry, we propose a novel way to model DSNs for recognizing movements on four cardinal directions. It is based on an architecture of ON and OFF visual pathways underlies a theory of splitting motion signals into parallel channels, encoding brightness increments and decrements separately. To enhance the edge selectivity and speed response to moving objects, we put forth a bio-plausible spatial-temporal network structure with multiple connections of same polarity ON/OFF cells. Each pair-wised combination is filtered with dynamic delay depending on sampling distance. The proposed vision system was challenged against image streams from both synthetic and cluttered real physical scenarios. The results demonstrated three major contributions: first, the neural network fulfilled the characteristics of a postulated physiological map of conveying visual information through different neuropile layers; second, the DSNs model can extract useful directional motion cues from cluttered background robustly and timely, which hits at potential of quick implementation in vision-based micro mobile robots; moreover, it also represents better speed response compared to a state-of-the-art elementary motion detector.

The developments of robotics inform research across a broad range of disciplines. In this paper, we will study and compare two collision selective neuron models via a vision-based autonomous micro robot. In the locusts' visual brain, two Lobula Giant Movement Detectors (LGMDs), i.e. LGMD1 and LGMD2, have been identified as looming sensitive neurons responding to rapidly expanding objects, yet with different collision selectivity. Both neurons have been modeled and successfully applied in robotic vision system for perceiving potential collisions in an efficient and reliable manner. In this research, we conduct binocular neuronal models, for the first time combining the functionalities of LGMD1 and LGMD2 neurons, in the visual modality of a ground mobile robot. The results of systematic on-line experiments demonstrated three contributions of this research: (1) The arena tests involving multiple robots verified the effectiveness and robustness of a reactive motion control strategy via integrating a bilateral pair of LGMD1 and LGMD2 models for collision detection in dynamic scenarios. (2) We pinpointed the different collision selectivity between LGMD1 and LGMD2 neuron models, which fulfill corresponding biological research. (3) The utilized micro robot may also benefit researches on other embedded vision systems as well as swarm robotics.