A passive system approach to increase the energy efficiency in walk movements based in a realistic simulation environment

This paper presents a passive system that increases the walk energy efficiency of a Humanoid robot. A passive system is applied to the simulated robot allowing the energy consumption to be reduced. The optimal parameters for the passive system depend on the joint and gait trajectories. Final results prove the benefits of the presented system apply. It was optimized thanks to a realistic simulator where the humanoid robot was modeled. The model was validated against a real robot

Suspended Load Path Tracking Control Using a Tilt-rotor UAV Based on Zonotopic State Estimation

This work addresses the problem of path tracking control of a suspended load using a tilt-rotor UAV. The main challenge in controlling this kind of system arises from the dynamic behavior imposed by the load, which is usually coupled to the UAV by means of a rope, adding unactuated degrees of freedom to the whole system. Furthermore, to perform the load transportation it is often needed the knowledge of the load position to accomplish the task. Since available sensors are commonly embedded in the mobile platform, information on the load position may not be directly available. To solve this problem in this work, initially, the kinematics of the multi-body mechanical system are formulated from the load's perspective, from which a detailed dynamic model is derived using the Euler-Lagrange approach, yielding a highly coupled, nonlinear state-space representation of the system, affine in the inputs, with the load's position and orientation directly represented by state variables. A zonotopic state estimator is proposed to solve the problem of estimating the load position and orientation, which is formulated based on sensors located at the aircraft, with different sampling times, and unknown-but-bounded measurement noise. To solve the path tracking problem, a discrete-time mixed $\mathcal{H}_2/\mathcal{H}_\infty$ controller with pole-placement constraints is designed with guaranteed time-response properties and robust to unmodeled dynamics, parametric uncertainties, and external disturbances. Results from numerical experiments, performed in a platform based on the Gazebo simulator and on a Computer Aided Design (CAD) model of the system, are presented to corroborate the performance of the zonotopic state estimator along with the designed controller

Experimental study of a two-DOF five bar closed-loop mechanism

This research is to carry out an experimental study to examine and verify the effectiveness of the control algorithms and strategies developed at the Advanced Engineering Design Laboratory (AEDL). For this purpose, two objectives are set to be achieved in this research. The first objective is to develop a generic experiment environment (test bed) such that different control approaches and algorithms can be implemented on it. The second objective is to conduct an experimental study on the examined control algorithms, as applied to the above test bed. To achieve the first objective, two main test beds, namely, the real-time controllable (RTC) mechanism and the hybrid machine, have been developed based on a two degree of freedom (DOF) closed-loop five-bar linkage. The 2-DOF closed-loop mechanism is employed in this study as it is the simplest of multi-DOF closed-loop mechanisms, and control approaches and conclusions based on a 2-DOF mechanism are generic and can be applied to a closed-loop mechanism with a higher number of degrees of freedom. The RTC mechanism test bed is driven by two servomotors and the hybrid machine is driven by one servomotor and a traditional CV motor. To achieve the second objective, an experimental study on different control algorithms has been conducted. The Proportional Derivative (PD) based control laws, i.e., traditional iii PD control, Nonlinear-PD (NPD) control, Evolutionary PD (EPD) control, non-linear PD learning control (NPD-LC) and Adaptive Evolutionary Switching-PD (AES-PD) are applied to the RTC mechanism; and as applied to the Hybrid Actuation System (HAS), the traditional PD control and the SMC control techniques are examined and compared. In the case of the RTC mechanism, the experiments on the five PD-based control algorithms, i.e., PD control, NPD control, EPD, NPD-LC, and AES-PD, show that the NPD controller has better performance than the PD controller in terms of the reduction in position tracking errors. It is also illustrated by the experiments that iteration learning control (ILC) techniques can be used to improve the trajectory tracking performance. However, AES-PD showed to have a faster convergence rate than the other ILC control laws. Experimental results also show that feedback ILC is more effective than the feedforward ILC and has a faster convergence rate. In addition, the results of the comparative study of the traditional PD and the Computed Torque Control (CTC) technique at both low and high speeds show that at lower speeds, both of these controllers provide similar results. However, with an increase in speed, the position tracking errors using the CTC control approach become larger than that of the traditional PD control. In the case of the hybrid machine, PD control and SMC control are applied to the mechanism. The results show that for the control of the hybrid machine and the range of speed used in this experimental study, PD control can result in satisfactory performance. However, SMC proved to be more effective than PD control

Superando la brecha de la realidad: Algoritmos de aprendizaje por imitación y por refuerzos para problemas de locomoción robótica bípeda

ilustraciones, diagramas, fotografíasEsta tesis presenta una estrategia de entrenamiento de robots que utiliza técnicas de aprendizaje artificial para optimizar el rendimiento de los robots en tareas complejas. Motivado por los impresionantes logros recientes en el aprendizaje automático, especialmente en juegos y escenarios virtuales, el proyecto tiene como objetivo explorar el potencial de estas técnicas para mejorar las capacidades de los robots más allá de la programación humana tradicional a pesar de las limitaciones impuestas por la brecha de la realidad. El caso de estudio seleccionado para esta investigación es la locomoción bípeda, ya que permite dilucidar los principales desafíos y ventajas de utilizar métodos de aprendizaje artificial para el aprendizaje de robots. La tesis identifica cuatro desafíos principales en este contexto: la variabilidad de los resultados obtenidos de los algoritmos de aprendizaje artificial, el alto costo y riesgo asociado con la realización de experimentos en robots reales, la brecha entre la simulación y el comportamiento del mundo real, y la necesidad de adaptar los patrones de movimiento humanos a los sistemas robóticos. La propuesta consiste en tres módulos principales para abordar estos desafíos: Enfoques de Control No Lineal, Aprendizaje por Imitación y Aprendizaje por Reforzamiento. El módulo de Enfoques de Control No Lineal establece una base al modelar robots y emplear técnicas de control bien establecidas. El módulo de Aprendizaje por Imitación utiliza la imitación para generar políticas iniciales basadas en datos de captura de movimiento de referencia o resultados preliminares de políticas para crear patrones de marcha similares a los humanos y factibles. El módulo de Aprendizaje por Refuerzos complementa el proceso mejorando de manera iterativa las políticas paramétricas, principalmente a través de la simulación pero con el rendimiento en el mundo real como objetivo final. Esta tesis enfatiza la modularidad del enfoque, permitiendo la implementación de los módulos individuales por separado o su combinación para determinar la estrategia más efectiva para diferentes escenarios de entrenamiento de robots. Al utilizar una combinación de técnicas de control establecidas, aprendizaje por imitación y aprendizaje por refuerzos, la estrategia de entrenamiento propuesta busca desbloquear el potencial para que los robots alcancen un rendimiento optimizado en tareas complejas, contribuyendo al avance de la inteligencia artificial en la robótica no solo en sistemas virtuales sino en sistemas reales.The thesis introduces a comprehensive robot training framework that utilizes artificial learning techniques to optimize robot performance in complex tasks. Motivated by recent impressive achievements in machine learning, particularly in games and virtual scenarios, the project aims to explore the potential of these techniques for improving robot capabilities beyond traditional human programming. The case study selected for this investigation is bipedal locomotion, as it allows for elucidating key challenges and advantages of using artificial learning methods for robot learning. The thesis identifies four primary challenges in this context: the variability of results obtained from artificial learning algorithms, the high cost and risk associated with conducting experiments on real robots, the reality gap between simulation and real-world behavior, and the need to adapt human motion patterns to robotic systems. The proposed approach consists of three main modules to address these challenges: Non-linear Control Approaches, Imitation Learning, and Reinforcement Learning. The Non-linear Control module establishes a foundation by modeling robots and employing well-established control techniques. The Imitation Learning module utilizes imitation to generate initial policies based on reference motion capture data or preliminary policy results to create feasible human-like gait patterns. The Reinforcement Learning module complements the process by iteratively improving parametric policies, primarily through simulation but ultimately with real-world performance as the ultimate goal. The thesis emphasizes the modularity of the approach, allowing for the implementation of individual modules separately or their combination to determine the most effective strategy for different robot training scenarios. By employing a combination of established control techniques, imitation learning, and reinforcement learning, the framework seeks to unlock the potential for robots to achieve optimized performances in complex tasks, contributing to the advancement of artificial intelligence in robotics.DoctoradoDoctor en ingeniería mecánica y mecatrónic

An approach of optimising S-curve trajectory for a better energy consumption

In today's manufacturing industry, higher productivity and sustainability should go hand-in-hand. This practice is motivated by governmental regulations as well as customers' awareness. For the current time, one of the inexpensive solutions is motion planning for an improved energy consumption. This paper introduces a general approach that is valid for testing and optimising energy consumption of the input motion profile. The Particle Swarm Optimisation method (PSO) is used because of its mathematical simplicity and quick convergence. Being commonly used, s-curve motion profile is reconstructed and optimised for a better energy consumption. The results show potential energy reduction and better positioning for the system configured according to the optimised s-curve

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored