Near range path navigation using LGMD visual neural networks

In this paper, we proposed a method for near range path navigation for a mobile robot by using a pair of biologically inspired visual neural network – lobula giant movement detector (LGMD). In the proposed binocular style visual system, each LGMD processes images covering a part of the wide field of view and extracts relevant visual cues as its output. The outputs from the two LGMDs are compared and translated into executable motor commands to control the wheels of the robot in real time. Stronger signal from the LGMD in one side pushes the robot away from this side step by step; therefore, the robot can navigate in a visual environment naturally with the proposed vision system. Our experiments showed that this bio-inspired system worked well in different scenarios

Redundant neural vision systems: competing for collision recognition roles

Ability to detect collisions is vital for future robots that interact with humans in complex visual environments. Lobula giant movement detectors (LGMD) and directional selective neurons (DSNs) are two types of identified neurons found in the visual pathways of insects such as locusts. Recent modelling studies showed that the LGMD or grouped DSNs could each be tuned for collision recognition. In both biological and artificial vision systems, however, which one should play the collision recognition role and the way the two types of specialized visual neurons could be functioning together are not clear. In this modeling study, we compared the competence of the LGMD and the DSNs, and also investigate the cooperation of the two neural vision systems for collision recognition via artificial evolution. We implemented three types of collision recognition neural subsystems – the LGMD, the DSNs and a hybrid system which combines the LGMD and the DSNs subsystems together, in each individual agent. A switch gene determines which of the three redundant neural subsystems plays the collision recognition role. We found that, in both robotics and driving environments, the LGMD was able to build up its ability for collision recognition quickly and robustly therefore reducing the chance of other types of neural networks to play the same role. The results suggest that the LGMD neural network could be the ideal model to be realized in hardware for collision recognition

Looming detection by identified visual interneurons during larval development of the locust Locusta migratoria

Insect larvae clearly react to visual stimuli, but the ability of any visual neuron in a newly hatched insect to respond selectively to particular stimuli has not been directly tested. We characterised a pair of neurons in locust larvae that have been extensively studied in adults, where they are known to respond selectively to objects approaching on a collision course: the lobula giant motion detector (LGMD) and its postsynaptic partner, the descending contralateral motion detector (DCMD). Our physiological recordings of DCMD axon spikes reveal that at the time of hatching, the neurons already respond selectively to objects approaching the locust and they discriminate between stimulus approach speeds with differences in spike frequency. For a particular approaching stimulus, both the number and peak frequency of spikes increase with instar. In contrast, the number of spikes in responses to receding stimuli decreases with instar, so performance in discriminating approaching from receding stimuli improves as the locust goes through successive moults. In all instars, visual movement over one part of the visual field suppresses a response to movement over another part. Electron microscopy demonstrates that the anatomical substrate for the selective response to approaching stimuli is present in all larval instars: small neuronal processes carrying information from the eye make synapses both onto LGMD dendrites and with each other, providing pathways for lateral inhibition that shape selectivity for approaching objects.Fil: Simmons, Peter J.. University of Newcastle; Reino UnidoFil: Sztarker, Julieta. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Rind, F. Claire. University of Newcastle; Reino Unid

Exploring the southern ocean response to climate change

The purpose of this project was to couple a regional (Southern Ocean) ocean/sea ice model to the existing Goddard Institute for Space Science (GISS) atmospheric general circulation model (GCM). This modification recognizes: the relative isolation of the Southern Ocean; the need to account, prognostically, for the significant air/sea/ice interaction through all involved components; and the advantage of translating the atmospheric lower boundary (typically the rapidly changing ocean surface) to a level that is consistent with the physical response times governing the system evolution (that is, to the base of the fast responding ocean surface layer). The deeper ocean beneath this layer varies on time scales several orders of magnitude slower than the atmosphere and surface ocean, and therefore the boundary between the upper and deep ocean represents a more reasonable fixed boundary condition

Predator versus Prey:Locust Looming-Detector Neuron and Behavioural Responses to Stimuli Representing Attacking Bird Predators

Many arthropods possess escape-triggering neural mechanisms that help them evade predators. These mechanisms are important neuroethological models, but they are rarely investigated using predator-like stimuli because there is often insufficient information on real predator attacks. Locusts possess uniquely identifiable visual neurons (the descending contralateral movement detectors, DCMDs) that are well-studied looming motion detectors. The DCMDs trigger ‘glides’ in flying locusts, which are hypothesised to be appropriate last-ditch responses to the looms of avian predators. To date it has not been possible to study glides in response to stimuli simulating bird attacks because such attacks have not been characterised. We analyse video of wild black kites attacking flying locusts, and estimate kite attack speeds of 10.8±1.4 m/s. We estimate that the loom of a kite’s thorax towards a locust at these speeds should be characterised by a relatively low ratio of half size to speed (l/|v|) in the range 4–17 ms. Peak DCMD spike rate and gliding response occurrence are known to increase as l/|v| decreases for simple looming shapes. Using simulated looming discs, we investigate these trends and show that both DCMD and behavioural responses are strong to stimuli with kite-like l/|v| ratios. Adding wings to looming discs to produce a more realistic stimulus shape did not disrupt the overall relationships of DCMD and gliding occurrence to stimulus l/|v|. However, adding wings to looming discs did slightly reduce high frequency DCMD spike rates in the final stages of object approach, and slightly delay glide initiation. Looming discs with or without wings triggered glides closer to the time of collision as l/|v| declined, and relatively infrequently before collision at very low l/|v|. However, the performance of this system is in line with expectations for a last-ditch escape response

Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated

Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant movement detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the development of an LGMD model for use as an artificial collision detector in robotic applications. To date, robots have been equipped with only a single, central artificial LGMD sensor, and this triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly, for a robot to behave autonomously, it must react differently to stimuli approaching from different directions. In this study, we implement a bilateral pair of LGMD models in Khepera robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD models using methodologies inspired by research on escape direction control in cockroaches. Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration, the khepera robots could escape an approaching threat in real time and with a similar distribution of escape directions as real locusts. We also found that by optimising these algorithms, we could use them to integrate the left and right DCMD responses of real jumping locusts offline and reproduce the actual escape directions that the locusts took in a particular trial. Our results significantly advance the development of an artificial collision detection and evasion system based on the locust LGMD by allowing it reactive control over robot behaviour. The success of this approach may also indicate some important areas to be pursued in future biological research

Insect-vision inspired collision warning vision processor for automobiles

Vision is expected to play important roles for car safety enhancement. Imaging systems can be used to enlarging the vision field of the driver. For instance capturing and displaying views of hidden areas around the car which the driver can analyze for safer decision-making. Vision systems go a step further. They can autonomously analyze the visual information, identify dangerous situations and prompt the delivery of warning signals. For instance in case of road lane departure, if an overtaking car is in the blind spot, if an object is approaching within collision course, etc. Processing capabilities are also needed for applications viewing the car interior such as >intelligent airbag systems> that base deployment decisions on passenger features. On-line processing of visual information for car safety involves multiple sensors and views, huge amount of data per view and large frame rates. The associated computational load may be prohibitive for conventional processing architectures. Dedicated systems with embedded local processing capabilities may be needed to confront the challenges. This paper describes a dedicated sensory-processing architecture for collision warning which is inspired by insect vision. Particularly, the paper relies on the exploitation of the knowledge about the behavior of Locusta Migratoria to develop dedicated chips and systems which are integrated into model cars as well as into a commercial car (Volvo XC90) and tested to deliver collision warnings in real traffic scenarios.Gobierno de España TEC2006-15722European Community IST:2001-3809

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

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

Structural Organization of the Presynaptic Density at Identified Synapses in the Locust Central Nervous System

In a synaptic active zone, vesicles aggregate around a densely staining structure called the presynaptic density. We focus on its three-dimensional architecture and a major molecular component in the locust. We used electron tomography to study the presynaptic density in synapses made in the brain by identified second-order neuron of the ocelli. Here, vesicles close to the active zone are organized in two rows on either side of the presynaptic density, a level of organization not previously reported in insect central synapses. The row of vesicles that is closest to the density's base includes vesicles docked with the presynaptic membrane and thus presumably ready for release, whereas the outer row of vesicles does not include any that are docked. We show that a locust ortholog of the Drosophila protein Bruchpilot is localized to the presynaptic density, both in the ocellar pathway and compound eye visual neurons. An antibody recognizing the C-terminus of the Bruchpilot ortholog selectively labels filamentous extensions of the presynaptic density that reach out toward vesicles. Previous studies on Bruchpilot have focused on its role in neuromuscular junctions in Drosophila, and our study shows it is also a major functional component of presynaptic densities in the central nervous system of an evolutionarily distant insect. Our study thus reveals Bruchpilot executes similar functions in synapses that can sustain transmission of small graded potentials as well as those relaying large, spike-evoked signals. J. Comp. Neurol. 520:384–400, 2012. © 2011 Wiley Periodicals, Inc