289 research outputs found
Bio-inspired motion detection in an FPGA-based smart camera module
Köhler T, Roechter F, Lindemann JP, Möller R. Bio-inspired motion detection in an FPGA-based smart camera module. Bioinspiration & Biomimetics. 2009;4(1):015008.Flying insects, despite their relatively coarse vision and tiny nervous system, are capable of carrying out elegant and fast aerial manoeuvres. Studies of the fly visual system have shown that this is accomplished by the integration of signals from a large number of elementary motion detectors (EMDs) in just a few global flow detector cells. We developed an FPGA-based smart camera module with more than 10000 single EMDs, which is closely modelled after insect motion-detection circuits with respect to overall architecture, resolution and inter-receptor spacing. Input to the EMD array is provided by a CMOS camera with a high frame rate. Designed as an adaptable solution for different engineering applications and as a testbed for biological models, the EMD detector type and parameters such as the EMD time constants, the motion-detection directions and the angle between correlated receptors are reconfigurable online. This allows a flexible and simultaneous detection of complex motion fields such as translation, rotation and looming, such that various tasks, e. g., obstacle avoidance, height/distance control or speed regulation can be performed by the same compact device
Field Programmable Gate Array (FPGA) for Bio-Inspired Visuo-Motor Control Systems Applied to Micro-Air Vehicles
International audienc
Taking Inspiration from Flying Insects to Navigate inside Buildings
These days, flying insects are seen as genuinely agile micro air vehicles fitted with smart sensors and also parsimonious in their use of brain resources. They are able to visually navigate in unpredictable and GPS-denied environments. Understanding how such tiny animals work would help engineers to figure out different issues relating to drone miniaturization and navigation inside buildings. To turn a drone of ~1 kg into a robot, miniaturized conventional avionics can be employed; however, this results in a loss of their flight autonomy. On the other hand, to turn a drone of a mass between ~1 g (or less) and ~500 g into a robot requires an innovative approach taking inspiration from flying insects both with regard to their flapping wing propulsion system and their sensory system based mainly on motion vision in order to avoid obstacles in three dimensions or to navigate on the basis of visual cues. This chapter will provide a snapshot of the current state of the art in the field of bioinspired optic flow sensors and optic flow-based direct feedback loops applied to micro air vehicles flying inside buildings
The role of direction-selective visual interneurons T4 and T5 in Drosophila orientation behavior
In order to safely move through the environment, visually-guided animals
use several types of visual cues for orientation. Optic flow provides faithful
information about ego-motion and can thus be used to maintain a straight
course. Additionally, local motion cues or landmarks indicate potentially
interesting targets or signal danger, triggering approach or avoidance, respectively.
The visual system must reliably and quickly evaluate these cues
and integrate this information in order to orchestrate behavior. The underlying
neuronal computations for this remain largely inaccessible in higher
organisms, such as in humans, but can be studied experimentally in more
simple model species. The fly Drosophila, for example, heavily relies on
such visual cues during its impressive flight maneuvers. Additionally, it is
genetically and physiologically accessible. Hence, it can be regarded as an
ideal model organism for exploring neuronal computations during visual
processing.
In my PhD studies, I have designed and built several autonomous virtual
reality setups to precisely measure visual behavior of walking flies. The
setups run in open-loop and in closed-loop configuration. In an open-loop
experiment, the visual stimulus is clearly defined and does not depend on
the behavioral response. Hence, it allows mapping of how specific features
of simple visual stimuli are translated into behavioral output, which can
guide the creation of computational models of visual processing. In closedloop
experiments, the behavioral response is fed back onto the visual stimulus,
which permits characterization of the behavior under more realistic
conditions and, thus, allows for testing of the predictive power of the computational
models.
In addition, Drosophila’s genetic toolbox provides various strategies for
targeting and silencing specific neuron types, which helps identify which
cells are needed for a specific behavior. We have focused on visual interneuron
types T4 and T5 and assessed their role in visual orientation behavior.
These neurons build up a retinotopic array and cover the whole visual field
of the fly. They constitute major output elements from the medulla and have
long been speculated to be involved in motion processing.
This cumulative thesis consists of three published studies: In the first
study, we silenced both T4 and T5 neurons together and found that such flies
were completely blind to any kind of motion. In particular, these flies could
not perform an optomotor response anymore, which means that they lost
their normally innate following responses to motion of large-field moving
patterns. This was an important finding as it ruled out the contribution
of another system for motion vision-based behaviors. However, these flies
were still able to fixate a black bar. We could show that this behavior is
mediated by a T4/T5-independent flicker detection circuitry which exists in
parallel to the motion system.
In the second study, T4 and T5 neurons were characterized via twophoton
imaging, revealing that these cells are directionally selective and
have very similar temporal and orientation tuning properties to directionselective
neurons in the lobula plate. T4 and T5 cells responded in a
contrast polarity-specific manner: T4 neurons responded selectively to ON
edge motion while T5 neurons responded only to OFF edge motion. When
we blocked T4 neurons, behavioral responses to moving ON edges were
more impaired than those to moving OFF edges and the opposite was true
for the T5 block. Hence, these findings confirmed that the contrast polarityspecific
visual motion pathways, which start at the level of L1 (ON) and L2
(OFF), are maintained within the medulla and that motion information is
computed twice independently within each of these pathways.
Finally, in the third study, we used the virtual reality setups to probe the
performance of an artificial microcircuit. The system was equipped with a
camera and spherical fisheye lens. Images were processed by an array of
Reichardt detectors whose outputs were integrated in a similar way to what
is found in the lobula plate of flies. We provided the system with several rotating
natural environments and found that the fly-inspired artificial system
could accurately predict the axes of rotation
Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review
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 modelling 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 of insects' visual systems in the literature. These motion perception models or neural networks comprise 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, as well as the small target motion detectors (STMDs) in dragonflies and hover flies. We also review the applications of these models to robots and vehicles. Through these modelling studies, we summarise the methodologies that generate different direction and size selectivity in motion perception. At last, we discuss about multiple systems integration and hardware realisation of these bio-inspired motion perception models
Optic Flow Based Visual Guidance: From Flying Insects to Miniature Aerial Vehicles
International audienc
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