Software controlador de bajo nivel para el robot Lola-OPTM

Un robot serpiente constituido por módulos, llamado en la literatura modular snake robot o MSR como se hará referencia en este trabajo, es un sistema robótico biomórfico construido al concatenar múltiples módulos, cuya estructura mecánica se asemeja al cuerpo de las serpientes. Las principales cualidades que le diferencian de otros tipos de robot son: la reducida longitud de su sección transversal respecto a la de su sección longitudinal, lo que le permite moverse y maniobrar en espacios estrechos, y la versatilidad, otorgada por su diseño modular, para adoptar un amplio rango de posturas, posibilitando diversos esquemas de locomoción, cada uno conseguido al variar su forma secuencialmente.Ingeniero (a) ElectrónicoPregrad

An Adaptable Robotic Snake using a Compliant Actuated Tensegrity Structure for Locomotion and its Motion Pattern Analysis

The thesis explores the possibilities that using a compliant actuated tensegrity structure to build an adapted robotic snake for locomotion. With the development of modern society, people are relying more and more on robots to assist in their work. The robotic snake is a type of robot that is often used in exploration and relief work on complex terrain due to its unique bionic structure. However, traditional snake-like robots have structures that focus on specific snake-like movement patterns, but cannot actually simulate how the spine and muscles of a snake can work, thus losing out on desirable features such as high energy efficiency and flexibility. In this work, a tensegrity structure is researched to enable a robotic snake to realize the structure and capabilities of a snake. A prototype has been built for experiments: three segments connected by springs and strings which forms a tension network. The prototype is actuated by the change of the tension within the network, just as the muscles in a snake contract and stretch around the spine. Experiments with the prototype show that it can carry out effective rectilinear movement and steering movement on a variety of terrain, and its overall speed is mainly limited by the friction coefficient of the ground. However, because the underside of the body module prevents the module from tilting, the prototype cannot perform serpentine movement. More improvements in the shape design of the body modules and motion control could also be studied in future work

Processo para medição e avaliação de atrito com fins de facilitar movimentação de robô ápode

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2018.Todos os seres vivos são resultados de um processo de validação e otimização de, no mínimo, milhares de anos, período de testes superior à quaisquer teorias ou mecanismos desenvolvidos por seres humanos. Por isso, o uso de tecnologias bioinspiradas é algo até óbvio, principalmente em situações onde animais tem desempenho melhor do que máquinas tradicionais, como busca e salvamento de sobreviventes em destroços. Em verdade, já existe na literatura uma série de projetos de robôs bioinspirados em cobras, os quais buscam fazer uso do estilo de locomoção de fácil equilíbrio do animal e sua adaptabilidade a diversos terrenos. Contudo, a existência de tais projetos não significa nem sua aplicação prática de maneira satisfatória nem o melhor uso das possibilidades que estes trazem, o que se explica em parte pelo fato de muitos dos robôs presentes na literatura sofrem do mesmo mal: a falta do estudo adequado sobre os efeitos do atrito na movimentação e de como o tornar útil. Uma falha que poderia ser perdoada em vários projetos que não exijam precisão fina de deslocamento, mas que traz perdas de informação e otimização enquanto considerados robôs inspirados em cobras. Nada na natureza é por acaso, e se estes animais possuem acabamentos superficiais nanométricos em suas escamas ventrais diferentes dos observados em suas escamas dorsais, os quais alteram o coeficiente de atrito em três direções das escamas, há uma razão por trás. E é por essa lógica que o principal objetivo deste trabalho não foi desenvolver soluções inovadoras para melhorar a movimentação dos ditos robôs cobra, mas sim buscar respostas simples que tenham ficado pra trás durante a evolução destes. Como por exemplo, a explicação lógica e física do porquê as cobras enrolam seus corpos enquanto de movem e como esse fenômeno pode ser aplicado. Em suma, para se seguir adiante em robôs cobra com aplicação melhor e mais ampla, esse trabalho propõe olhar para trás, para o começo. Foram selecionadas três frentes de desenvolvimento: geometria e morfologia do robô, desenho e teste de escamas artificiais e determinação de uma rotina baseada em relações trigonométricas através da qual um robô seja capaz de obter um senso quanto ao atrito do terreno onde se encontre. Todas as três etapas pediram por um estudo morfológico e físico de robótica modular, movimentação cobra, anatomia e comportamento de cobras além de definições quanto ao fenômeno de atrito e seus modelos. Com isso, a presença de uma extensa revisão bibliográfica em diferentes conteúdos foi inevitável, porém os resultados foram mais que satisfatórios, enquanto compostos por uma validação quanto a aplicabilidade de soluções simples bioinspiradas, as quais podem ser aplicadas à quaisquer robôs com movimentação cobra.All living beings are the result of a validation and optimization process of at least thousands of years, to superior test period than any mechanism or theory developed by humans. Therefore, the use of bioinspired technologies is obvious, especially in conditions where animals have better performance than traditional machinery, such as search and rescue of survivors in accident debris. In fact, the already exist to sieries of projects the snake inspired robots in literature, whose made use of the stable locomotion gait of the animal and it’s adaptability to unknow terrain. However, the existence of such project does not translate into it’s optimal application nor in the better use of it’s inehenrent possibilities, what can be explained in part by tha fact that most of the robots found in the literature commit the same sin: the lack of a proper study about friction effects on locomotion and how to make it valuable. A mistake that could be forgiven in most projects that do not call for fine movement precision, but that causes loss of information and optimization while considering snake bioinspired robots. Nothing is done in nature without a reason, and if those animals have nanometric superficial characteristics in it’s ventral scales that are different from it’s dorsal ones, and if those characteristics alter the scale friction coefficiente in three different directions, there is a reason behind it. And this is why the main objective of this work was not to develop a brand new solution to optimize the locomotion of so said robots, but rather look for simple answers that have been left behing during the robots evolution. For example, the logical and physical reason for why they snake wrap their bodies during it’s movement and how this phenomenon can be applied. In short, to move ahead with snake robots with a better and wider application, this work proposes to look back, to the starter point. Three development fronts where selected: geometry and morphology of the robot, design and test of artificial scales and determination of the routine based on trigomometric relations such that it allows the robot to get a sense of the it’s terrain friction. All three fronts required a morphological and physic studie of modular robotics, snake locomotion, snake anatomy and behavior as well as definitions of the friction phenomena and it’s models. With that, the presence of an extense literature review was inescapable, but the results here are more satisfactory, while presentis to validation of the applicability of simple bioispires solutions that can be applied in any robot with snake locomotion

On the Creation and Use of Forward Models in Robot Motor Control

Advancements in robotics have the potential to aid humans in many realms of exploration as well as daily life: from search and rescue work, to space and deep sea exploration, to in-home assistance to improve the quality of life for those with limited mobility. One of the main milestones that needs to be met for robotics to achieve these ends is a robust ability to manipulate objects and locomote in cluttered and changing environments. A prerequisite to these skills is the ability to understand the current state of the world as well as how actions result in changes to the environment; in short, a robot needs a way to model itself and the world around it. With recent advances in machine learning and access to cheap and fast computation, one of the most promising avenues for creating robust models is to learn a neural network to approximate the dynamics of the system. Learning a data-driven model that accurately replicates the dynamics of a robot and its environment is an active area of robotics research. This model needs to be accurate, it needs to operate using sensors that are often high dimensional, and it needs to be robust to changes within the system and the surrounding environment. In this thesis, we investigate ways to improve the processof learning data-driven dynamics models as well as ways to reduce the dimensionality of a robot’s state space. We start by trying to improve the long-term accuracy of neural network based forward models. Learning forward models is more complicated than it appears on the surface. While it is easy to learn a model to predict the change of a system over a short horizon, it is challenging to assure this performance over a long horizon. We investigate the concept of adding temporal information into the loss function of the forward model during training; we demonstrate that this improves the accuracy of a model when it is used to predict over long horizons. While we are currently working with low dimensional systems, we eventually want to apply our learned models to robots with high dimensional state spaces. To make learning feasible, we need to find ways to learn a lower dimensional representation of the state space (also known as a latent space) to make learning models in the real world computationally feasible. We present a method to improve the usefulness of a learned latent space using a method we call context training: we learn a latent space alongside a forward model to encourage the learned latent space to retain the variables critical to learning the dynamics of the system. In all of our experiments, we spend significant time in analysis and evaluation. A large portion of literature demonstrating the effectiveness of data-driven forward models in robot control settings often only presents the final controller performance. We were often left curious about what the model was learning independent of the control scenario. We set out to do our own deep dive into exactly what data-driven forward models are predicting. We evaluate all of our models over long horizons. We also look deeper than just the mean and median loss values. We plot the full distribution of loss values over the entire horizon. The literature on data-driven models that do evaluate model prediction accuracy often focuses on the mean and median prediction errors; while these are important metrics, we found that looking at these metrics alone can sometimes obscure subtle but important effects. A high mean loss is often a result of poor performance on only a subset of the test dataset; one model can outperform other models with lower mean error values on a majority of the test set, but it can be skewed to look like the worst performer by having a few highly inaccurate outliers. We observe that models often have a subset of a test dataset on which they perform best; we seek to limit the use of a model to regions of the test dataset where it has high accuracy by using an ensemble of models. We find that if we train an ensemble of forward models, the accuracy of the models is higher when they all agree on a prediction. Conversely, when the ensemble of models disagrees, the prediction is often poor. We explore this relationship and propose future ways to apply it. Finally, we look into the application of improved model accuracy and context trained latent spaces. We start by testing the performance of our context training architecture as a method to reduce the state space dimensionality in a model-free reinforcement learning (MFRL) reaching task. We hypothesize that a policy trained with a latent space observation derived using our context trained encoder will outperform a policy trained with a latent space observation derived from a standard autoencoder. Unfortunately, we found no difference in task performance between the policies learned using either method. We end on a bright note by looking at the power of model-based control when we have access to an accurate model. We successfully use model predictive control (MPC) to generate robust locomotion for a simulated snake robot. With access to an accurate model, we are able to generate realistic snake gaits in a variety of environments with very little parameter tuning that are robust to changes in the environment

Modeling, Control and Energy Efficiency of Underwater Snake Robots

This thesis is mainly motivated by the attribute of the snake robots that they are able to move over land as well as underwater while the physiology of the robot remains the same. This adaptability to different motion demands depending on the environment is one of the main characteristics of the snake robots. In particular, this thesis targets several interesting aspects regarding the modeling, control and energy efficiency of the underwater snake robots. This thesis addresses the problem of modeling the hydrodynamic effects with an analytical perspective and a primary objective to conclude in a closed-form solution for the dynamic model of an underwater snake robot. Two mathematical models of the kinematics and dynamics of underwater snake robots swimming in virtual horizontal and vertical planes aimed at control design are presented. The presented models are derived in a closed-form and can be utilized in modern modelbased control schemes. In addition, these proposed models comprise snake robots moving both on land and in water which makes the model applicable for unified control methods for amphibious snake robots moving both on land and in water. The third model presented in this thesis is based on simplifying assumptions in order to derive a control-oriented model of an underwater snake robot moving in a virtual horizontal plane that is well-suited for control design and stability analysis. The models are analysed using several techniques. An extensive analysis of the model of a fully immersed underwater snake robot moving in a virtual horizontal plane is conducted. Based on this analysis, a set of essential properties that characterize the overall motion of underwater snake robots is derived. An averaging analysis reveals new fundamental properties of underwater snake robot locomotion that are useful from a motion planning perspective. In this thesis, both the motion analysis and control strategies are conducted based on a general sinusoidal motion pattern which can be used for a broad class of motion patterns including lateral undulation and eel-like motion. This thesis proposes and experimentally validates solutions to the path following control problem for biologically inspired swimming snake robots. In particular, line-of-sight (LOS) and integral line-of-sight (I-LOS) guidance laws, which are combined with a sinusoidal gait pattern and a directional controller that steers the robot towards and along the desired path are proposed. An I-LOS path following controller for steering an underwater snake robot along a straight line path in the presence of ocean currents of unknown direction and magnitude is presented and by using a Poincaré map, it is shown that all state variables of an underwater snake robot, except for the position along the desired path, trace out an exponentially stable periodic orbit. Moreover, this thesis presents the combined use of an artificial potential fields-based path planner with a new waypoint guidance strategy for steering an underwater snake robot along a path defined by waypoints interconnected by straight lines. The waypoints are derived by using a path planner based on the artificial potential field method in order to also address the obstacle avoidance problem. Furthermore, this thesis considers the energy efficiency of underwater snake robots. In particular, the relationship between the parameters of the gait patterns, the forward velocity and the energy consumption for the different motion patterns for underwater snake robots is investigated. Based on simulation results, this thesis presents empirical rules to choose the values for the parameters of the motion gait pattern of underwater snake robots. The experimental results support the derived properties regarding the relationship between the gait parameters and the power consumption both for lateral undulation and eel-like motion patterns. Moreover, comparison results are obtained for the total energy consumption and the cost of transportation of underwater snake robots and remotely operated vehicles (ROVs). Furthermore, in this thesis a multi-objective optimization problem is developed with the aim of maximizing the achieved forward velocity of the robot and minimizing the corresponding average power consumption of the system