A Motor-less and Gear-less Bio-mimetic Robotic Fish Design

In this paper, we describe our current work on bio-inspired locomotion systems using a deformable structure and smart materials, concretely Shape Memory Alloys, exploring the possibility of building motor-less and gear-less robots. A swimming underwater robot has been developed whose movements are generated using such actuators, used for bending the backbone of the fish, which in turn causes a change on the curvature of the body. This paper focuses on how standard swimming patterns can be reproduced with the proposed design, using an actuation dynamics model identified in prior work.

Amorphous piezoresistive and piezoelectric sensors for robotics applications

Ultrasonic transducers have often been used in the development of sensory systems for robotics applications. In most cases, these sensory systems are based on the determination of times of flight for signals from every transducer. In this work we have used piezoresistive and piezoelectric materials to measure the instant and position collision in metallic structures by using the difference of the times of propagation of an acoustic wave when it is produced over a ferromagnetic (iron, steel or another material) based structure. An immediate application of the proposed method is the detection and location of impacts over the metallic links of an industrial robot or the collision position in a metallic structure for an automated inspection

Bio-inspired FPGA Architecture for Self-Calibration of an Image Compression Core based on Wavelet Transforms in Embedded Systems

A generic bio-inspired adaptive architecture for image compression suitable to be implemented in embedded systems is presented. The architecture allows the system to be tuned during its calibration phase. An evolutionary algorithm is responsible of making the system evolve towards the required performance. A prototype has been implemented in a Xilinx Virtex-5 FPGA featuring an adaptive wavelet transform core directed at improving image compression for specific types of images. An Evolution Strategy has been chosen as the search algorithm and its typical genetic operators adapted to allow for a hardware friendly implementation. HW/SW partitioning issues are also considered after a high level description of the algorithm is profiled which validates the proposed resource allocation in the device fabric. To check the robustness of the system and its adaptation capabilities, different types of images have been selected as validation patterns. A direct application of such a system is its deployment in an unknown environment during design time, letting the calibration phase adjust the system parameters so that it performs efcient image compression. Also, this prototype implementation may serve as an accelerator for the automatic design of evolved transform coefficients which are later on synthesized and implemented in a non-adaptive system in the final implementation device, whether it is a HW or SW based computing device. The architecture has been built in a modular way so that it can be easily extended to adapt other types of image processing cores. Details on this pluggable component point of view are also given in the paper.

BaTboT: a biologically inspired flapping and morphing bat robot actuated by SMA-based artificial muscles

Bats are animals that posses high maneuvering capabilities. Their wings contain dozens of articulations that allow the animal to perform aggressive maneuvers by means of controlling the wing shape during flight (morphing-wings). There is no other flying creature in nature with this level of wing dexterity and there is biological evidence that the inertial forces produced by the wings have a key role in the attitude movements of the animal. This can inspire the design of highly articulated morphing-wing micro air vehicles (not necessarily bat-like) with a significant wing-to-body mass ratio. This thesis presents the development of a novel bat-like micro air vehicle (BaTboT) inspired by the morphing-wing mechanism of bats. BaTboT’s morphology is alike in proportion compared to its biological counterpart Cynopterus brachyotis, which provides the biological foundations for developing accurate mathematical models and methods that allow for mimicking bat flight. In nature bats can achieve an amazing level of maneuverability by combining flapping and morphing wingstrokes. Attempting to reproduce the biological wing actuation system that provides that kind of motion using an artificial counterpart requires the analysis of alternative actuation technologies more likely muscle fiber arrays instead of standard servomotor actuators. Thus, NiTinol Shape Memory Alloys (SMAs) acting as artificial biceps and triceps muscles are used for mimicking the morphing wing mechanism of the bat flight apparatus. This antagonistic configuration of SMA-muscles response to an electrical heating power signal to operate. This heating power is regulated by a proper controller that allows for accurate and fast SMA actuation. Morphing-wings will enable to change wings geometry with the unique purpose of enhancing aerodynamics performance. During the downstroke phase of the wingbeat motion both wings are fully extended aimed at increasing the area surface to properly generate lift forces. Contrary during the upstroke phase of the wingbeat motion both wings are retracted to minimize the area and thus reducing drag forces. Morphing-wings do not only improve on aerodynamics but also on the inertial forces that are key to maneuver. Thus, a modeling framework is introduced for analyzing how BaTboT should maneuver by means of changing wing morphology. This allows the definition of requirements for achieving forward and turning flight according to the kinematics of the wing modulation. Motivated by the biological fact about the influence of wing inertia on the production of body accelerations, an attitude controller is proposed. The attitude control law incorporates wing inertia information to produce desired roll (φ) and pitch (θ) acceleration commands. This novel flight control approach is aimed at incrementing net body forces (Fnet) that generate propulsion. Mimicking the way how bats take advantage of inertial and aerodynamical forces produced by the wings in order to both increase lift and maneuver is a promising way to design more efficient flapping/morphing wings MAVs. The novel wing modulation strategy and attitude control methodology proposed in this thesis provide a totally new way of controlling flying robots, that eliminates the need of appendices such as flaps and rudders, and would allow performing more efficient maneuvers, especially useful in confined spaces. As a whole, the BaTboT project consists of five major stages of development: - Study and analysis of biological bat flight data reported in specialized literature aimed at defining design and control criteria. - Formulation of mathematical models for: i) wing kinematics, ii) dynamics, iii) aerodynamics, and iv) SMA muscle-like actuation. It is aimed at modeling the effects of modulating wing inertia into the production of net body forces for maneuvering. - Bio-inspired design and fabrication of: i) skeletal structure of wings and body, ii) SMA muscle-like mechanisms, iii) the wing-membrane, and iv) electronics onboard. It is aimed at developing the bat-like platform (BaTboT) that allows for testing the methods proposed. - The flight controller: i) control of SMA-muscles (morphing-wing modulation) and ii) flight control (attitude regulation). It is aimed at formulating the proper control methods that allow for the proper modulation of BaTboT’s wings. - Experiments: it is aimed at quantifying the effects of properly wing modulation into aerodynamics and inertial production for maneuvering. It is also aimed at demonstrating and validating the hypothesis of improving flight efficiency thanks to the novel control methods presented in this thesis. This thesis introduces the challenges and methods to address these stages. Windtunnel experiments will be oriented to discuss and demonstrate how the wings can considerably affect the dynamics/aerodynamics of flight and how to take advantage of wing inertia modulation that the morphing-wings enable to properly change wings’ geometry during flapping. Resumen: Los murciélagos son mamíferos con una alta capacidad de maniobra. Sus alas están conformadas por docenas de articulaciones que permiten al animal maniobrar gracias al cambio geométrico de las alas durante el vuelo. Esta característica es conocida como (alas mórficas). En la naturaleza, no existe ningún especimen volador con semejante grado de dexteridad de vuelo, y se ha demostrado, que las fuerzas inerciales producidas por el batir de las alas juega un papel fundamental en los movimientos que orientan al animal en vuelo. Estas características pueden inspirar el diseño de un micro vehículo aéreo compuesto por alas mórficas con redundantes grados de libertad, y cuya proporción entre la masa de sus alas y el cuerpo del robot sea significativa. Esta tesis doctoral presenta el desarrollo de un novedoso robot aéreo inspirado en el mecanismo de ala mórfica de los murciélagos. El robot, llamado BaTboT, ha sido diseñado con parámetros morfológicos muy similares a los descritos por su símil biológico Cynopterus brachyotis. El estudio biológico de este especimen ha permitido la definición de criterios de diseño y modelos matemáticos que representan el comportamiento del robot, con el objetivo de imitar lo mejor posible la biomecánica de vuelo de los murciélagos. La biomecánica de vuelo está definida por dos tipos de movimiento de las alas: aleteo y cambio de forma. Intentar imitar como los murciélagos cambian la forma de sus alas con un prototipo artificial, requiere el análisis de métodos alternativos de actuación que se asemejen a la biomecánica de los músculos que actúan las alas, y evitar el uso de sistemas convencionales de actuación como servomotores ó motores DC. En este sentido, las aleaciones con memoria de forma, ó por sus siglas en inglés (SMA), las cuales son fibras de NiTinol que se contraen y expanden ante estímulos térmicos, han sido usados en este proyecto como músculos artificiales que actúan como bíceps y tríceps de las alas, proporcionando la funcionalidad de ala mórfica previamente descrita. De esta manera, los músculos de SMA son mecánicamente posicionados en una configuración antagonista que permite la rotación de las articulaciones del robot. Los actuadores son accionados mediante una señal de potencia la cual es regulada por un sistema de control encargado que los músculos de SMA respondan con la precisión y velocidad deseada. Este sistema de control mórfico de las alas permitirá al robot cambiar la forma de las mismas con el único propósito de mejorar el desempeño aerodinámico. Durante la fase de bajada del aleteo, las alas deben estar extendidas para incrementar la producción de fuerzas de sustentación. Al contrario, durante el ciclo de subida del aleteo, las alas deben contraerse para minimizar el área y reducir las fuerzas de fricción aerodinámica. El control de alas mórficas no solo mejora el desempeño aerodinámico, también impacta la generación de fuerzas inerciales las cuales son esenciales para maniobrar durante el vuelo. Con el objetivo de analizar como el cambio de geometría de las alas influye en la definición de maniobras y su efecto en la producción de fuerzas netas, simulaciones y experimentos han sido llevados a cabo para medir cómo distintos patrones de modulación de las alas influyen en la producción de aceleraciones lineales y angulares. Gracias a estas mediciones, se propone un control de vuelo, ó control de actitud, el cual incorpora información inercial de las alas para la definición de referencias de aceleración angular. El objetivo de esta novedosa estrategia de control radica en el incremento de fuerzas netas para la adecuada generación de movimiento (Fnet). Imitar como los murciélagos ajustan sus alas con el propósito de incrementar las fuerzas de sustentación y mejorar la maniobra en vuelo es definitivamente un tópico de mucho interés para el diseño de robots aéros mas eficientes. La propuesta de control de vuelo definida en este trabajo de investigación podría dar paso a una nueva forma de control de vuelo de robots aéreos que no necesitan del uso de partes mecánicas tales como alerones, etc. Este control también permitiría el desarrollo de vehículos con mayor capacidad de maniobra. El desarrollo de esta investigación se centra en cinco etapas: - Estudiar y analizar el vuelo de los murciélagos con el propósito de definir criterios de diseño y control. - Formular modelos matemáticos que describan la: i) cinemática de las alas, ii) dinámica, iii) aerodinámica, y iv) actuación usando SMA. Estos modelos permiten estimar la influencia de modular las alas en la producción de fuerzas netas. - Diseño y fabricación de BaTboT: i) estructura de las alas y el cuerpo, ii) mecanismo de actuación mórfico basado en SMA, iii) membrana de las alas, y iv) electrónica abordo. - Contro de vuelo compuesto por: i) control de la SMA (modulación de las alas) y ii) regulación de maniobra (actitud). - Experimentos: están enfocados en poder cuantificar cuales son los efectos que ejercen distintos perfiles de modulación del ala en el comportamiento aerodinámico e inercial. El objetivo es demostrar y validar la hipótesis planteada al inicio de esta investigación: mejorar eficiencia de vuelo gracias al novedoso control de orientación (actitud) propuesto en este trabajo. A lo largo del desarrollo de cada una de las cinco etapas, se irán presentando los retos, problemáticas y soluciones a abordar. Los experimentos son realizados utilizando un túnel de viento con la instrumentación necesaria para llevar a cabo las mediciones de desempeño respectivas. En los resultados se discutirá y demostrará que la inercia producida por las alas juega un papel considerable en el comportamiento dinámico y aerodinámico del sistema y como poder tomar ventaja de dicha característica para regular patrones de modulación de las alas que conduzcan a mejorar la eficiencia del robot en futuros vuelos.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics

We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.

Spectral Biomimetic Technique for Wood Classification Inspired by Human Echolocation

Los Clics palatales son sonidos interesantes para la ecolocalización humana pero tambien pueden utilizarse para determinar propiedades de la madera como para poder identificar tres especies diferentes de madera. Los resultados muestran que la ecolocalización humana tiene un enorme potencial.

Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. It has evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Crop row detection in maize fields inspired on the human visual perception

This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection.