Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad

Cellular Nonlinear Networks: optimized implementation on FPGA and applications to robotics

L'objectiu principal d'aquesta tesi consisteix a estudiar la factibilitat d'implementar un sensor càmera CNN amb plena funcionalitat basat en FPGA de baix cost adequat per a aplicacions en robots mòbils. L'estudi dels fonaments de les xarxes cel•lulars no lineals (CNNs) i la seva aplicació eficaç en matrius de portes programables (FPGAs) s'ha complementat, d'una banda amb el paral•lelisme que s'estableix entre arquitectura multi-nucli de les CNNs i els eixams de robots mòbils, i per l'altre banda amb la correlació dinàmica de CNNs i arquitectures memristive. A més, els memristors es consideren els substituts dels futurs dispositius de memòria flash per la seva capacitat d'integració d'alta densitat i el seu consum d'energia prop de zero. En el nostre cas, hem estat interessats en el desenvolupament d’FPGAs que han deixat de ser simples dispositius per a la creació ràpida de prototips ASIC per esdevenir complets dispositius reconfigurables amb integració de la memòria i els elements de processament general. En particular, s'han explorat com les arquitectures implementades CNN en FPGAs poden ser optimitzades en termes d’àrea ocupada en el dispositiu i el seu consum de potència. El nostre objectiu final ens ah portat a implementar de manera eficient una CNN-UM amb complet funcionament a un baix cost i baix consum sobre una FPGA amb tecnología flash. Per tant, futurs estudis sobre l’arquitectura eficient de la CNN sobre la FPGA i la interconnexió amb els robots comercials disponibles és un dels objectius d'aquesta tesi que se seguiran en les línies de futur exposades en aquest treball.El objetivo principal de esta tesis consiste en estudiar la factibilidad de implementar un sensor cámara CNN con plena funcionalidad basado en FPGA de bajo coste adecuado para aplicaciones en robots móviles. El estudio de los fundamentos de las redes celulares no lineales (CNNs) y su aplicación eficaz en matrices de puertas programables (FPGAs) se ha complementado, por un lado con el paralelismo que se establece entre arquitectura multi -núcleo de las CNNs y los enjambres de robots móviles, y por el otro lado con la correlación dinámica de CNNs y arquitecturas memristive. Además, los memristors se consideran los sustitutos de los futuros dispositivos de memoria flash por su capacidad de integración de alta densidad y su consumo de energía cerca de cero. En nuestro caso, hemos estado interesados en el desarrollo de FPGAs que han dejado de ser simples dispositivos para la creación rápida de prototipos ASIC para convertirse en completos dispositivos reconfigurables con integración de la memoria y los elementos de procesamiento general. En particular, se han explorado como las arquitecturas implementadas CNN en FPGAs pueden ser optimizadas en términos de área ocupada en el dispositivo y su consumo de potencia. Nuestro objetivo final nos ah llevado a implementar de manera eficiente una CNN-UM con completo funcionamiento a un bajo coste y bajo consumo sobre una FPGA con tecnología flash. Por lo tanto, futuros estudios sobre la arquitectura eficiente de la CNN sobre la FPGA y la interconexión con los robots comerciales disponibles es uno de los objetivos de esta tesis que se seguirán en las líneas de futuro expuestas en este trabajo.The main goal of this thesis consists in studying the feasibility to implement a full-functionality CNN camera sensor based on low-cost FPGA device suitable for mobile robotic applications. The study of Cellular Nonlinear Networks (CNNs) fundamentals and its efficient implementation on Field Programmable Gate Arrays (FPGAs) has been complemented, on one side with the parallelism established between multi-core CNN architecture and swarm of mobile robots, and on the other side with the dynamics correlation of CNNs and memristive architectures. Furthermore, memristors are considered the future substitutes of flash memory devices because of its capability of high density integration and its close to zero power consumption. In our case, we have been interested in the development of FPGAs that have ceased to be simple devices for ASIC fast prototyping to become complete reconfigurable devices embedding memory and processing elements. In particular, we have explored how the CNN architectures implemented on FPGAs can be optimized in terms of area occupied on the device or power consumption. Our final accomplishment has been implementing efficiently a fully functional reconfigurable CNN-UM on a low-cost low-power FPGA based on flash technology. Therefore, further studies on an efficient CNN architecture on FPGA and interfacing it with commercially-available robots is one of the objectives of this thesis that will be followed in the future directions exposed in this work

Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system

It is true that the "best" neural network is not necessarily the one with the most "brain-like" behavior. Understanding biological intelligence, however, is a fundamental goal for several distinct disciplines. Translating our understanding of intelligence to machines is a fundamental problem in robotics. Propelled by new advancements in Neuroscience, we developed a spiking neural network (SNN) that draws from mounting experimental evidence that a number of individual neurons is associated with spatial navigation. By following the brain's structure, our model assumes no initial all-to-all connectivity, which could inhibit its translation to a neuromorphic hardware, and learns an uncharted territory by mapping its identified components into a limited number of neural representations, through spike-timing dependent plasticity (STDP). In our ongoing effort to employ a bioinspired SNN-controlled robot to real-world spatial mapping applications, we demonstrate here how an SNN may robustly control an autonomous robot in mapping and exploring an unknown environment, while compensating for its own intrinsic hardware imperfections, such as partial or total loss of visual input.Comment: 8 pages, 3 Figures, International Conference on Neuromorphic Systems (ICONS 2018

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

New Perspectives on the Dialogue between Brains and Machines

Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies

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

Applications of Artificial Intelligence in the Treatment of Behavioral and Mental Health Conditions

Introduction Artificial intelligence (AI) is the branch of science that studies and designs intelligent devices. For individuals unfamiliar with artificial intelligence, the concept of intelligent machines may bring up visions of attractive human-like computers or robots, like those described in science fiction. Others may consider AI technology to be mysterious machines limited to research facilities or a technical triumph that will come in the far future. Popular media accounts on the deployment of aerial drones, autonomous autos, or the potential dangers of developing super-intelligent technologies may have raised some broad awareness of the subject