Guido Vagliasindi

Reactive navigation through multiscroll systems: from theory to real-time implementation

Abstract In this paper a new reactive layer for multi-sensory integration applied to robot navigation is proposed. The new robot navigation technique exploits the use of a chaotic system able to be controlled in real-time towards less complex orbits, like periodic orbits or equilibrium points, considered as perceptive orbits. These are subject to real-time modifications on the basis of environment changes acquired through a distributed sensory system. The strategy is inspired to the olfactory bulb neural activity observed in rabbits subject to external stimuli. The mathematical details of the approach are given including simulation results in a virtual environment. Furthermore the proposed strategy has been tested on an experimental environment consisting of an FPGA-based hardware driving an autonomous roving robot. The obtained results demonstrate the capability to perform a real-time navigation control.

CPG-MTA implementation for locomotion control

This paper deals with the new realization of the VLSI chip for locomotion control introduced in Arena et al. (2003). With respect to the previous version, the new chip includes the fully functional generation of several locomotion gaits suitable for the control of hexapod robots and other bio-inspired structures. Experimental results and the application of the chip to the control of two bio-inspired robots are shown.

CNN wave based computation for robot navigation on ACE16K

The CNN wave based computation is an approach for real time robot navigation in a complex environment based on the idea of considering the environment in which the robot moves as an excitable medium. Obstacles represent the sources of autowave generation. The waveform propagating in the CNN medium provide to the robot all the information to achieve an adaptive motion avoiding the obstacles. In this paper we implement entirely this strategy on the ACE16K CNN-chip.

Fuzzy logic and support vector machine approaches to regime identification in JET

A plasma regime is a distinct type of plasma confinement, which can be identified from several conventional plasma diagnostic signals. An accurate and general regime identifier is considered an important tool for future real time applications in Joint European Torus (JET). In this perspective, a traditional approach based on Discriminant Analysis was tested, using various sets of JET real time signals. Unfortunately, no combination of signals managed to provide a success rate higher than 90%. To improve the performance and increase the generalization capability, an identifier based on Fuzzy Logic was developed, which allowed inclusion of the time evolution of the Dalpha, a quantity normally not exploited by more traditional solutions. With this technique a success rate of 95% was achieved using only Dalpha and the derivative of betaN diamagnetic as inputs. A support vector machines approach, based again on a suitably defined distance like discriminant analysis, provided slightly inferior results with exactly the same inputs but matched the fuzzy logic method with the inclusion of the absolute value of betaN diamagnetic. This comparative performance assessment of the various methods is an important first step on the route to identify the best solution for a regime identifier for JET and in due course for ITER

Image processing with cellular nonlinear networks implemented on field-programmable gate arrays for real-time applications in nuclear fusion.

In the past years cameras have become increasingly common tools in scientific applications. They are now quite systematically used in magnetic confinement fusion, to the point that infrared imaging is starting to be used systematically for real-time machine protection in major devices. However, in order to guarantee that the control system can always react rapidly in case of critical situations, the time required for the processing of the images must be as predictable as possible. The approach described in this paper combines the new computational paradigm of cellular nonlinear networks (CNNs) with field-programmable gate arrays and has been tested in an application for the detection of hot spots on the plasma facing components in JET. The developed system is able to perform real-time hot spot recognition, by processing the image stream captured by JET wide angle infrared camera, with the guarantee that computational time is constant and deterministic. The statistical results obtained from a quite extensive set of examples show that this solution approximates very well an ad hoc serial software algorithm, with no false or missed alarms and an almost perfect overlapping of alarm intervals. The computational time can be reduced to a millisecond time scale for 8 bit 496560-sized images. Moreover, in our implementation, the computational time, besides being deterministic, is practically independent of the number of iterations performed by the CNN-unlike software CNN implementations.

Weak Chaos Control for Action-Oriented Perception: Real Time Implementation via FPGA

In this paper a new robot navigation technique based on sensing-perception-action loop, called weak chaos control, has been implemented in a low level hardware structure using an FPGA (Field Programmable Gate Array). The underlying idea is that perception can be represented by chaotic attractors whose dynamical evolution depends on sensorial stimuli. To achieve this goal sensors equipped on the robot are associated with reference trajectories controlling the chaotic system. This represents a key issue in the representation of perception under the concurrent control of different sensorial stimuli. The implemented strategy has been tested on an experimental environment using a roving robot and demonstrates the capability to perform a real-time navigation control

A WAVE-BASED CNN GENERATOR FOR THE CONTROL AND ACTUATION OF A LAMPREY-LIKE ROBOT

In this work a project which aims at the construction of LampBot II, an autonomous biologically-inspired swimming robot, is presented. LampBot II is capable of mimicking the undulatory swimming of a sea lamprey. The system used to generate locomotion is based on the paradigm of the Central Pattern Generator (CPG) and is implemented on a chip based on a Cellular Nonlinear Network (CNN). Two prototype structures have been developed in order to evaluate the feasibility of the actuation by means of Shape Memory Alloy (SMA) wires or servo motors.

A Disruption Predictor Based on Fuzzy Logic Applied to JET Database

Disruptions remain a serious issue for International Thermonuclear Experimental Reactor (ITER) and the commercial reactor. In the past years, significant efforts have been devoted to the development of reliable disruption predictors. In this paper, the potential of fuzzy logic in this field is explored. A new disruption predictor for Joint European Torus (JET), which is based on fuzzy logic, was tested off-line using data collected during several JET campaigns, covering a very wide range of scenarios and plasma parameters. In the standard configuration, it was able to predict 92% of disruptions with about 18% of false alarms (FAs). The flexibility of the approach, which is to provide a different tradeoff between missed and FAs, depending on the desired results in terms of safety, is demonstrated. The potential of the technique to help quantifying the risks that are associated with the operation outside the already explored parameter space is also discussed.

Implementation of a Moving Target Tracking Algorithm Using Eye-RIS Vision System on a Mobile Robot

A moving target tracking algorithm is proposed here and implemented on the Anafocus Eye-RIS vision system, which is a compact and modular platform to develop real time image processing applications. The algorithm combines moving-object detection with feature extraction in order to identify the specific target in the environment. The algorithm was tested in a mobile robotics experiment in which a robot, with the Eye-RIS mounted on it, pursued another one representing the moving target, demonstrating its performance and capabilities.

Implementation of a drosophila-inspired orientation model on the Eye-Ris platform

A behavioral model, recently derived from experiments on fruit-flies, was implemented, with successful comparative experiments on orientation control in real robots. This model has been firstly implemented in a standard CNN structure, using an algorithm based on classical, space-invariant templates. Subsequently, the Eye-Ris platform was utilised for the implementation of the whole strategy, at the aim to constitute a stand alone smart sensor for orientation control in bio-inspired robotic platforms. The Eye-Ris vl.2 is a visual system, made by Anafocus, that employs a fully-parallel mixed-signal array sensor-processor chip. Some experiments are reported using a commercial roving platform, the Pioneer P3-AT, showing the reliability of the proposed implementation and usefulness in higher level perceptual tasks.