699 research outputs found
AER-based robotic closed-loop control system
Address-Event-Representation (AER) is an
asynchronous protocol for transferring the information of
spiking neuro-inspired systems. Actually AER systems are able
to see, to ear, to process information, and to learn. Regarding to
the actuation step, the AER has been used for implementing
Central Pattern Generator algorithms, but not for controlling
the actuators in a closed-loop spike-based way. In this paper we
analyze an AER based model for a real-time neuro-inspired
closed-loop control system. We demonstrate it into a differential
control system for a two-wheel vehicle using feedback AER
information. PFM modulation has been used to power the DC
motors of the vehicle and translation into AER of encoder
information is also presented for the close-loop. A codesign
platform (called AER-Robot), based into a Xilinx Spartan 3
FPGA and an 8051 USB microcontroller, with power stages for
four DC motors has been used for the demonstrator.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0
Spike-based control monitoring and analysis with Address Event Representation
Neuromorphic engineering tries to mimic biological
information processing. Address-Event Representation (AER) is
a neuromorphic communication protocol for spiking neurons
between different chips. We present a new way to drive robotic
platforms using spiking neurons. We have simulated spiking
control models for DC motors, and developed a mobile robot
(Eddie) controlled only by spikes. We apply AER to the robot
control, monitoring and measuring the spike activity inside the
robot. The mobile robot is controlled by the AER-Robot tool,
and the AER information is sent to a PC using the
USBAERmini2 interface.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0
From Vision Sensor to Actuators, Spike Based Robot Control through Address-Event-Representation
One field of the neuroscience is the neuroinformatic whose aim is to
develop auto-reconfigurable systems that mimic the human body and brain. In
this paper we present a neuro-inspired spike based mobile robot. From
commercial cheap vision sensors converted into spike information, through
spike filtering for object recognition, to spike based motor control models. A
two wheel mobile robot powered by DC motors can be autonomously
controlled to follow a line drown in the floor. This spike system has been
developed around the well-known Address-Event-Representation mechanism to
communicate the different neuro-inspired layers of the system. RTC lab has
developed all the components presented in this work, from the vision sensor, to
the robot platform and the FPGA based platforms for AER processing.Ministerio de Ciencia e Innovación TEC2006-11730-C03-02Junta de Andalucía P06-TIC-0141
AER and dynamic systems co-simulation over Simulink with Xilinx System Generator
Address-Event Representation (AER) is a
neuromorphic communication protocol for transferring
information of spiking neurons implemented into VLSI chips.
These neuro-inspired implementations have been used to design
sensor chips (retina, cochleas), processing chips (convolutions,
filters) and learning chips, what makes possible the
development of complex, multilayer, multichip neuromorphic
systems. In biology one of the last steps of the processing is to
move a muscle, to apply the results of these complex
neuromorphic processing to the real world. One interesting
question is to be able to transform, or translate, the AER
information into robot movements, like for example, moving a
DC motor. This paper presents several ways to translate AER
spikes into DC motor power, and to control a DC motor speed,
based on Pulse Frequency Modulation. These methods have
been simulated into Simulink with Xilinx System Generator,
and tested into the AER-Robot platform.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0
Spike-based VITE control with Dynamic Vision Sensor applied to an Arm Robot.
Spike-based motor control is very important in the
field of robotics and also for the neuromorphic engineering
community to bridge the gap between sensing / processing
devices and motor control without losing the spike philosophy
that enhances speed response and reduces power consumption.
This paper shows an accurate neuro-inspired spike-based system
composed of a DVS retina, a visual processing system that detects
and tracks objects, and a SVITE motor control, where everything
follows the spike-based philosophy. The control system is a spike
version of the neuroinspired open loop VITE control algorithm
implemented in a couple of FPGA boards: the first one runs the
algorithm and the second one drives the motors with spikes. The
robotic platform is a low cost arm with four degrees of freedom.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02/01Ministerio de Economía y Competitividad TEC2012-37868-C04-02/0
On the Designing of Spikes Band-Pass Filters for FPGA
In this paper we present two implementations of spike-based bandpass
filters, which are able to reject out-of-band frequency components in the
spike domain. First one is based on the use of previously designed spike-based
low-pass filters. With this architecture the quality factor, Q, is lower than 0.5.
The second implementation is inspired in the analog multi-feedback filters
(MFB) topology, it provides a higher than 1 Q factor, and ideally tends to
infinite. These filters have been written in VHLD, and synthesized for FPGA.
Two spike-based band-pass filters presented take advantages of the spike rate
coded representation to perform a massively parallel processing without complex
hardware units, like floating point arithmetic units, or a large memory. These low
requirements of hardware allow the integration of a high number of filters inside
a FPGA, allowing to process several spike coded signals fully in parallel.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
Neuromorphic computing is a new paradigm for design of both the computing
hardware and algorithms inspired by biological neural networks. The event-based
nature and the inherent parallelism make neuromorphic computing a promising
paradigm for building efficient neural network based architectures for control
of fast and agile robots. In this paper, we present a spiking neural network
architecture that uses sensory feedback to control rotational velocity of a
robotic vehicle. When the velocity reaches the target value, the mapping from
the target velocity of the vehicle to the correct motor command, both
represented in the spiking neural network on the neuromorphic device, is
autonomously stored on the device using on-chip plastic synaptic weights. We
validate the controller using a wheel motor of a miniature mobile vehicle and
inertia measurement unit as the sensory feedback and demonstrate online
learning of a simple 'inverse model' in a two-layer spiking neural network on
the neuromorphic chip. The prototype neuromorphic device that features 256
spiking neurons allows us to realise a simple proof of concept architecture for
the purely neuromorphic motor control and learning. The architecture can be
easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference
Synthetic retina for AER systems development
Neuromorphic engineering tries to mimic biology in
information processing. Address-Event Representation (AER) is
a neuromorphic communication protocol for spiking neurons
between different layers. AER bio-inspired image sensor are
called “retina”. This kind of sensors measure visual information
not based on frames from real life and generates corresponding
events. In this paper we provide an alternative, based on cheap
FPGA, to this image sensors that takes images provided by an
analog video source (video composite signal), digitalizes it and
generates AER streams for testing purposes.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0
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