13,727 research outputs found
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
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
Driving a car with custom-designed fuzzy inferencing VLSI chips and boards
Vehicle control in a-priori unknown, unpredictable, and dynamic environments requires many calculational and reasoning schemes to operate on the basis of very imprecise, incomplete, or unreliable data. For such systems, in which all the uncertainties can not be engineered away, approximate reasoning may provide an alternative to the complexity and computational requirements of conventional uncertainty analysis and propagation techniques. Two types of computer boards including custom-designed VLSI chips were developed to add a fuzzy inferencing capability to real-time control systems. All inferencing rules on a chip are processed in parallel, allowing execution of the entire rule base in about 30 microseconds, and therefore, making control of 'reflex-type' of motions envisionable. The use of these boards and the approach using superposition of elemental sensor-based behaviors for the development of qualitative reasoning schemes emulating human-like navigation in a-priori unknown environments are first discussed. Then how the human-like navigation scheme implemented on one of the qualitative inferencing boards was installed on a test-bed platform to investigate two control modes for driving a car in a-priori unknown environments on the basis of sparse and imprecise sensor data is described. In the first mode, the car navigates fully autonomously, while in the second mode, the system acts as a driver's aid providing the driver with linguistic (fuzzy) commands to turn left or right and speed up or slow down depending on the obstacles perceived by the sensors. Experiments with both modes of control are described in which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Simulation results as well as indoors and outdoors experiments are presented and discussed to illustrate the feasibility and robustness of autonomous navigation and/or safety enhancing driver's aid using the new fuzzy inferencing hardware system and some human-like reasoning schemes which may include as little as six elemental behaviors embodied in fourteen qualitative rules
Towards AER VITE: building spike gate signal
Neuromorphic engineers aim to mimic the precise and
efficient mechanisms of the nervous system to process
information using spikes from sensors to actuators. There are
many available works that sense and process information in a
spike-based way. But there are still several gaps in the actuation
and motor control field in a spike-based way. Spike-based
Proportional-Integrative-Derivative controllers (PID) are
present in the literature. On the other hand, neuro-inspired
control models as VITE (Vector Integration To End point) and
FLETE (Factorization of muscle Length and muscle Tension)
are also present in the literature. This paper presents another
step toward the spike implementation of those neuro-inspired
models. We present a spike-based ramp multiplier. VITE
algorithm generates the way to achieve a final position targeted
by a mobile robotic arm. The block presented is used as a gate
for the way involved and it also puts the incoming movement on
speed with a variable slope profile. Only spikes for information
representation were used and the process is in real time. The
software simulation based on Simulink and Xilinx System
Generator shows the accurate adjust to the traditional
processing for short time periods and the hardware tests
confirm and extend the previous simulated results for any time.
We have implemented the spikes generator, the ramp multiplier
and the low pass filter into the Virtex-5 FPGA and connected
this with an USB-AER (Address Event Representation) board to
monitor the spikes.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
AER Auditory Filtering and CPG for Robot Control
Address-Event-Representation (AER) is a
communication protocol for transferring asynchronous events
between VLSI chips, originally developed for bio-inspired
processing systems (for example, image processing). The event
information in an AER system is transferred using a highspeed
digital parallel bus. This paper presents an experiment
using AER for sensing, processing and finally actuating a
Robot. The AER output of a silicon cochlea is processed by an
AER filter implemented on a FPGA to produce rhythmic
walking in a humanoid robot (Redbot). We have implemented
both the AER rhythm detector and the Central Pattern
Generator (CPG) on a Spartan II FPGA which is part of a
USB-AER platform developed by some of the authors.Commission of the European Communities IST-2001-34124 (CAVIAR)Comisión Interministerial de Ciencia y Tecnología TIC-2003-08164-C03-0
Building Blocks for Spikes Signals Processing
Neuromorphic engineers study models and
implementations of systems that mimic neurons behavior in the
brain. Neuro-inspired systems commonly use spikes to
represent information. This representation has several
advantages: its robustness to noise thanks to repetition, its
continuous and analog information representation using digital
pulses, its capacity of pre-processing during transmission time,
... , Furthermore, spikes is an efficient way, found by nature, to
codify, transmit and process information. In this paper we
propose, design, and analyze neuro-inspired building blocks
that can perform spike-based analog filters used in signal
processing. We present a VHDL implementation for FPGA.
Presented building blocks 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. Those low requirements of hardware
allow the integration of a high number of blocks inside a FPGA,
allowing to process fully in parallel several spikes coded signals.Junta de Andalucía P06-TIC-O1417Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Ministerio de Ciencia e Innovación TEC2006-11730-C03-0
Parallel processing architecture for computing inverse differential kinematic equations of the PUMA arm
In advanced robot control problems, on-line computation of inverse Jacobian solution is frequently required. Parallel processing architecture is an effective way to reduce computation time. A parallel processing architecture is developed for the inverse Jacobian (inverse differential kinematic equation) of the PUMA arm. The proposed pipeline/parallel algorithm can be inplemented on an IC chip using systolic linear arrays. This implementation requires 27 processing cells and 25 time units. Computation time is thus significantly reduced
Integrated 2-D Optical Flow Sensor
I present a new focal-plane analog VLSI sensor that estimates optical flow in two visual dimensions. The chip significantly improves previous approaches both with respect to the applied model of optical flow estimation as well as the actual hardware implementation. Its distributed computational architecture consists of an array of locally connected motion units that collectively solve for the unique optimal optical flow estimate. The novel gradient-based motion model assumes visual motion to be translational, smooth and biased. The model guarantees that the estimation problem is computationally well-posed regardless of the visual input. Model parameters can be globally adjusted, leading to a rich output behavior. Varying the smoothness strength, for example, can provide a continuous spectrum of motion estimates, ranging from normal to global optical flow. Unlike approaches that rely on the explicit matching of brightness edges in space or time, the applied gradient-based model assures spatiotemporal continuity on visual information. The non-linear coupling of the individual motion units improves the resulting optical flow estimate because it reduces spatial smoothing across large velocity differences. Extended measurements of a 30x30 array prototype sensor under real-world conditions demonstrate the validity of the model and the robustness and functionality of the implementation
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