Paolo Arena

STDP-based behavior learning on the TriBot robot

This paper describes a correlation-based navigation algorithm, based on an unsupervised learning paradigm for spiking neural networks, called Spike Timing Dependent Plasticity (STDP). This algorithm was implemented on a new bio-inspired hybrid mini-robot called TriBot to learn and increase its behavioral capabilities. In fact correlation based algorithms have been found to explain many basic behaviors in simple animals. The main interesting consequence of STDP is that the system is able to learn high-level sensor features, based on a set of basic reflexes, depending on some low-level sensor inputs. TriBot is composed of 3 modules, the first two being identical and inspired by the Whegs hybrid robot. The peculiar characteristics of the robot consists in the innovative shape of the three-spoke appendages that allow to increase stability of the structure. The last module is composed of two standard legs with 3 degrees of freedom each. Thanks to the cooperation among these modules, TriBot is able to face with irregular terrains overcoming potential deadlock situations, to climb high obstacles compared to its size and to manipulate objects. Robot experiments will be reported to demonstrate the potentiality and the effectiveness of the approach.

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

CNN wave based computation for robot navigation planning

In this work a methodology for real-time robot navigation in a complex, dynamically changing environment, based on wave computation and implemented by cellular neural networks (CNNs) is introduced. The keypoint of the approach is to consider the environment in which the robot moves as an excitable medium. Obstacles and targets represent the source of autowave generation. The wavefronts propagating in the CNN medium provide to the robot all the information to achieve an adaptive motion avoiding the obstacles and directed to the target. In particular the paradigm of reaction-diffusion (RD) equations are used to implement a CNN-based wave computation for navigation control. Experimental results validating the approach are shown.

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.

Reaction-diffusion CNN chip. I. IC implementation

Many works have proved the capability of CNN architecture to reproduce complex phenomena described through partial differential equations (PDEs). This array of simple nonlinear systems called cells, for their analog and spatiotemporal distributed method of processing signals, are considered a powerful tool to generate real-time solutions of nonlinear PDEs. A two-layer 2D CNN is able to solve particular PDEs, so called Reaction-Diffusion Equations. This particular topology of CNN has been called RD-CNN. A silicon implementation of this architecture is presented.

Visual learning in drosophila: application on a roving robot and comparisons

Visual learning is an important aspect of fly life. Flies are able to extract visual cues from objects, like colors, vertical and horizontal distributedness, and others, that can be used for learning to associate a meaning to specific features (i.e. a reward or a punishment). Interesting biological experiments show trained stationary flying flies avoiding flying towards specific visual objects, appearing on the surrounding environment. Wild-type flies effectively learn to avoid those objects but this is not the case for the learning mutant rutabaga defective in the cyclic AMP dependent pathway for plasticity. A bio-inspired architecture has been proposed to model the fly behavior and experiments on roving robots were performed. Statistical comparisons have been considered and mutant-like effect on the model has been also investigated.

Visual homing: Experimental results on an autonomous robot

In this paper a new visual homing algorithm, implemented using the CNN-based vision system called Eye-Ris, is introduced. Design details and experimental results using a roving robot are also presented. The methodology as well as experimental inspiration was drawn by insect, which are able to show superb homing capabilities. In fact, in a variety of natural environments, different insect species are able to localize the nest position by using panoramic visual sensors. Exploiting this localization method, it is possible to implement algorithms which let a robot, endowed with a panoramic camera, come back to a reference position from any starting point in an arena, implementing the so-called visual homing method. If the home is a recharging station, visual homing can help to recharge the battery pack in an autonomous mobile robot, as shown in the paper.

Drosophila-inspired visual orientation model on the Eye-RIS platform:experiments on a roving robot

Behavioral experiments on fruit flies had shown that they are attracted by near objects and they prefer front-to-back motion. In this paper a visual orientation model is implemented on the Eye-Ris vision system and tested using a roving platform. Robotic experiments are used to collect statistical data regarding the system behaviour: followed trajectories, dwelling time, distribution of gaze direction and others strictly resembling the biological experimental setup on the flies. The statistical analysis has been performed in different scenarios where the robot faces with different object distribution in the arena. The acquired data has been used to validate the proposed model making a comparison with the fruit fly experiments.

Sensory Feedback in locomotion control

This chapter focuses on the sensing processes and their interactions with locomotion control. The analysis has been accomplished taking into account different levels of behavior. Moreover dynamic simulators and robotic structures have been used to investigate the biological principles governing the sensory feedback in the real world.

Embedding the AnaFocus' Eye-RIS vision system in roving robots to enhance the action-oriented perception

This paper aims to describe how the AnaFocus Eye-RIS family of vision systems has been successfully embedded within the roving robots developed under the framework of SPARK and SPARK II European projects to solve the action-oriented perception problem in real time. Indeed, the Eye-RIS family is a set of vision systems which are conceived for single-chip integration using CMOS technologies. The Eye-RIS systems employ a bio-inspired architecture where image acquisition and processing are truly intermingled and the processing itself is carried out in two steps. At the first step, processing is fully parallel owing to the concourse of dedicated circuit structures which are integrated close to the sensors. These structures handle basically analog information. At the second step, processing is realized on digitally-coded information data by means of digital processors. On the other hand, SPARK I and SPARK II are European research projects which goal is to develop completely new sensing-perceiving-moving artefacts inspired by the basic principles of living systems and based on the concept of selforganization. As a result, its low-power consumption together with its huge image-processing capabilities makes the Eye-RIS vision system a suitable choice to be embedded within the roving robots developed under the framework of SPARK projects and to implement in real time the resulting mathematical models for action-oriented perception.