Optimization of Watt’s Six-bar Linkage to Generate Straight and Parallel Leg Motion

This paper considers optimal synthesis of a special type of four-bar linkages. Combination of this optimal four-bar linkage with on of it's cognates and elimination of two redundant cognates will result in a Watt's six-bar mechanism, which generates straight and parallel motion. This mechanism can be utilized for legged machines. The advantage of this mechanism is that the leg remains straight during it's contact period and because of it's parallel motion, the legs can be as wide as desired to increase contact area and decrease the number of legs required to keep body's stability statically and dynamically. “Genetic algorithm” optimization method is used to find optimal lengths. It is especially useful for problems like the coupler curve equation which are completely nonlinear or extremely difficult to solve

Exploiting Natural Dynamics in the Control of a Planar Bipedal Walking Robot

Natural dynamics can be exploited in the control of bipedal walking robots: the swing leg can swing freely once started; a kneecap can be used to prevent the leg from inverting; and a compliant ankle can be used to naturally transfer the center of pressure along the foot and help in toe o#. Each of these mechanisms helps make control easier to achieve and results in motion that is smooth and natural looking. We describe a simple control algorithm using these natural mechanisms which requires very little computation. The necessary sensing consists of joint angles and velocities, body pitch and angular velocity, and ground reaction forces. Using this simple algorithm, we have controlled a seven link planar bipedal robot, called Spring Flamingo, to walk. Video, photographs, and more information on Spring Flamingo can be found at http://www.leglab.ai.mit.edu 1 Introduction A powerful practice in machine design and control is to design mechanisms which have natural dynamics that make contr..

A study on automatic gait parameter tuning for biped walking robots

Automatic gait parameter tuning for biped walking robots is the subject of this thesis. The biped structure is one of the most versatile ones for the employment of mobile robots in the human environment. Their control is challenging because of their many DOFs and nonlinearities in their dynamics. Open loop walking with offline walk pattern generation is one of the methods for walking control. in this method the reference positions of the foot centers with respect to the body center are generated as functionals. Commonly, the tuning process for the trajectory generation is based on numerous trial and error steps. Obviously, this is a time consuming and elaborate process. In this work, online adaptation schemes for one of the trajectory parameters, "z-reference asymmetry", which is used for the compensation of uneven weight distribution of the robot in the sagittal plane, is proposed. In one of the approaches presented, this parameter is tuned online. As an alternative to parameter tuning, a functional learning scheme employing fuzzy identifiers is tested too. Fuzzy identifiers are universal function approximators. Fuzzy system parameters are adapted via back-propagation. An on-line tuning scheme for biped walk parameters however can only be successful if there is sufficient time for training without falling. The training might last hundreds of reference cycles. This implies that a mechanism for keeping the robot in continuous walk, even when the parameter settings are totally wrong, is necessary during training. In this work, virtual torsional springs which resist against deviations of the robot trunk angles from zero, are attached to the trunk center of the biped. The torques generated by the springs serve as the criteria for the tuning and help in maintaining a stable and a longer walk. The springs are removed after training. This novel approach can be applied to a wide range of control systems that involve parameter tuning. 3-D simulation techniques using C++ are employed for the model of a 12-DOF biped robot to test the proposed adaptive method. in order to visualize the walking, simulation results are animated using an OpenGL based animation environment. As a result of the simulations, a functional for the desired parameter, keeping the system in balance while walking, is generated

Aplicación de algoritmos bioinspirados basados en visión por computador para la detección de equilibrio de robots humanoides

Existen gran cantidad de estudios que señalan la importancia de la visión en seres humanos para el control del equilibrio. El objeto de este proyecto es sentar las bases y analizar la viabilidad de la utilización de sistemas bioinspirados basados en visión por computador como herramienta para el control del equilibrio de robots humanoides. El presente proyecto realiza una revisión del estado del arte y analiza qué algoritmos existentes y disponibles utilizados para la obtención de la posición en tiempo real mediante visión por computador resultan interesantes de cara a ser implementados para control del equilibrio. Optándose por implementar el algoritmo Fovis de odometría visual y el método iterativo de resolución del problema “Perspective-n-Point”. Se han realizado una serie de pruebas con el robot humanoide TEO de la Universidad Carlos III de Madrid. Los resultados obtenidos por ambos algoritmos fueron comparados entre ellos y con los obtenidos por el sensor inercial actualmente utilizado por TEO para tareas de control de equilibrio. Los resultados obtenidos por Fovis en las pruebas llevan a concluir que la odometría visual representa una opción a tener en cuenta para el control del equilibrio de robots humanoides, abriendo una vía a futuros trabajos. Este trabajo forma parte de un proyecto más amplio que busca utilizar el robot humanoide TEO para imitar el comportamiento de un camarero, para lo cual el control de la postura corporal y el equilibrio son factores de gran relevancia.There exist many studies that evidence the important role that vision plays on balance control in human beings. The aim of this project is to lay the groundwork and analyze the viability of using bioinspired systems based on computer vision in order to control balance on humanoid robots. This Project makes an state of art revission and analyzes wich of the existant and available real time pose estimation algorithms fit for being used on balance control tasks on humanoid robots. Odometry visual algorithm Fovis and iterative “Perspective-n-Point” problem solver were chosen to be implemented. Several tests were done in humanoid robot TEO of University Carlos III of Madrid. A comparission between both method’s results was done and also with current inertial sensor used by TEO for balance control tasks. The results obtained by Fovis lead to conclude that visual odometry represents a good option for balance control tasks on humanoid robots, laying the ground for further work. This work is carried out within the framework of a bigger project that looks for using humanoid robot TEO to imitate a waiter’s behavior, for that objective pose and balance control are quite relevant issues.Ingeniería Electrónica Industrial y Automátic

Action module planning and Cartesian based control of an experimental climbing robot

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1997.Includes bibliographical references (leaves 88-95).by David M. Bevly.M.S

Goal-Based Control and Planning in Biped Locomotion Using Computational Intelligence Methods

Este trabajo explora la aplicación de campos neuronales, a tareas de control dinámico en el domino de caminata bípeda. En una primera aproximación, se propone una arquitectura de control que usa campos neuronales en 1D. Esta arquitectura de control es evaluada en el problema de estabilidad para el péndulo invertido de carro y barra, usado como modelo simplificado de caminata bípeda. El controlador por campos neuronales, parametrizado tanto manualmente como usando un algoritmo evolutivo (EA), se compara con una arquitectura de control basada en redes neuronales recurrentes (RNN), también parametrizada por por un EA. El controlador por campos neuronales parametrizado por EA se desempeña mejor que el parametrizado manualmente, y es capaz de recuperarse rápidamente de las condiciones iniciales más problemáticas. Luego, se desarrolla una arquitectura extendida de control y planificación usando campos neurales en 2D, y se aplica al problema caminata bípeda simple (SBW). Para ello se usa un conjunto de valores _óptimos para el parámetro de control, encontrado previamente usando algoritmos evolutivos. El controlador óptimo por campos neuronales obtenido se compara con el controlador lineal propuesto por Wisse et al., y a un controlador _optimo tabular que usa los mismos parámetros óptimos. Si bien los controladores propuestos para el problema SBW implementan una estrategia activa de control, se aproximan de manera más cercana a la caminata dinámica pasiva (PDW) que trabajos previos, disminuyendo la acción de control acumulada. / Abstract. This work explores the application of neural fields to dynamical control tasks in the domain of biped walking. In a first approximation, a controller architecture that uses 1D neural fields is proposed. This controller architecture is evaluated using the stability problem for the cart-and-pole inverted pendulum, as a simplified biped walking model. The neural field controller is compared, parameterized both manually and using an evolutionary algorithm (EA), to a controller architecture based on a recurrent neural neuron (RNN), also parametrized by an EA. The non-evolved neural field controller performs better than the RNN controller. Also, the evolved neural field controller performs better than the non-evolved one and is able to recover fast from worst-case initial conditions. Then, an extended control and planning architecture using 2D neural fields is developed and applied to the SBW problem. A set of optimal parameter values, previously found using an EA, is used as parameters for neural field controller. The optimal neural field controller is compared to the linear controller proposed by Wisse et al., and to a table-lookup controller using the same optimal parameters. While being an active control strategy, the controllers proposed here for the SBW problem approach more closely Passive Dynamic Walking (PDW) than previous works, by diminishing the cumulative control action.Maestrí

Applied optimal control for dynamically stable legged locomotion

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 79-84).Online learning and controller adaptation will be an essential component for legged robots in the next few years as they begin to leave the laboratory setting and join our world. I present the first example of a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic bipedal walking from a blank slate using only trials implemented on the physical robot. The robot begins walking within a minute and learning converges in approximately 20 minutes. The learning works quickly enough that the robot is able to continually adapt to the terrain as it walks. This success can be attributed in part to the mechanics of our robot, which is capable of stable walking down a small ramp even when the computer is turned off. In this thesis, I analyze the dynamics of passive dynamic walking, starting with reduced planar models and working up to experiments on our real robot. I describe, in detail, the actor-critic reinforcement learning algorithm that is implemented on the return map dynamics of the biped. Finally, I address issues of scaling and controller augmentation using tools from optimal control theory and a simulation of a planar one-leg hopping robot. These learning results provide a starting point for the production of robust and energy efficient walking and running robots that work well initially, and continue to improve with experience.by Russell L. Tedrake.Ph.D