Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining

This study evaluates the machining performance of newly developed modified jatropha oils (MJO1, MJO3 and MJO5), both with and without hexagonal boron nitride (hBN) particles (ranging between 0.05 and 0.5 wt%) during turning of AISI 1045 using minimum quantity lubrication (MQL). The experimental results indicated that, viscosity improved with the increase in MJOs molar ratio and hBN concentration. Excellent tribological behaviours is found to correlated with a better machining performance were achieved by MJO5a with 0.05 wt%. The MJO5a sample showed the lowest values of cutting force, cutting temperature and surface roughness, with a prolonged tool life and less tool wear, qualifying itself to be a potential alternative to the synthetic ester, with regard to the environmental concern

Efficient and Versatile Locomotion With Highly Compliant Legs

Drawing inspiration from nature, this paper introduces and compares two compliant robotic legs that are able to perform precise joint torque and position control, enable passive adaption to the environment, and allowfor the exploitation of natural dynamic motions.We report in detail on the design and control of both prototypes and elaborate specifically on the problem of precise foot placement during flight without the sacrifice of efficient energy storage during stance. This is achieved through an integrated design and control approach that incorporates series elastic actuation, series damping actuation, and active damping through torque control. The two legs are employed in efficient hopping/ running motions for which they achieve performance similar to humans or animals. This paper is concluded by a comparison of the various design choices with respect to performance and applicability, as well as an outlook on the usage of these legs in a fully actuated quadruped

A humanoid robot pushing model inspired by human motion

This thesis explores an observed method used by humans when pushing a large object of unknown mass. Body motion and reaction forces are analyzed for feet-apart pushing with varying stance length. It is found that, via articulation of the waist, a human will push their static zero-moment point (ZMP) as far forward as possible prior to pushing. Along with an extended back leg, this provides a larger support region in which the ZMP can move before stability is lost. Using this motion, the subject can produce a larger force than if the waist is constrained. Further, in this stance the subject is stable without object contact and can exert a range of forces by controlling mass distribution at the feet. For this increases in force exertion and stability, a linearized double inverted pendulum model with a feet-apart stance is proposed for use in the humanoid robot pushing of an unknown mass. Using the human pushing data and our humanoid, HUBO+, the advantage of this model and the added degree of freedom is shown against the commonly used single inverted pendulum model for humanoid robot pushing.M.S., Mechanical Engineering and Mechanics -- Drexel University, 201

Modelado matemático de un robot bípedo con equilibrio dinámico

La investigación presenta el desarrollo de un modelo matemático configurable de robot bípedo capaz de emular robots comerciales así como otros diseños antropomórficos para la prueba de controles de marcha en un ambiente virtual. El modelo cuenta con sub-sistemas que permiten configurar condiciones especiales de contacto visco elástico entre los pies del robot y el suelo; la acción de fuerzas de perturbación dinámicas son contrarrestadas por el control de equilibrio del robot que mantiene una postura erguida durante las fases de marcha. El modelo se prueba en condiciones de apoyo doble, apoyo simple y marcha para estudiar su respuesta a perturbaciones externas como internas.The project presents the development of a configurable mathematical model for a biped robot capable of emulating commercial robots as well as other anthropomorphic design for the test of gait controls in a virtual environment. The model has sub-systems allowing the set of special conditions of visco elastic contact between the feet of the robot and the ground; the simulation of the dynamic disturbance forces are canceled by the robot’s balance control keeping it on a erect stance . The model is tested under conditions of double support, single support and gait to study its response to external and internal disturbancesMaestrí

人の行動の表現と認識に関する研究

In recent years, analyzing human motion and recognizing a performed action from a video sequence has become very important and has been a well-researched topic in the field of computer vision. The reason behind such attention is its diverse applications in different domains like robotics, human computer interaction, video surveillance, controller-free gaming, video indexing, mixed or virtual reality, intelligent environments, etc. There are a number of researches performed on motion recognition in the last few decades. The state of the art action recognition schemes generally use a holistic or a body part based approach to represent actions. Most of the methods provide reasonable recognition results, but they are sometimes not suitable for online or real time systems because of their complexity in action representation. In this thesis, we address this issue by proposing a novel action representation scheme.The proposed action descriptor is based on a basic idea that rather than detecting the exact body parts or analyzing each action sequence, human action can be represented by a distribution of local texture patterns extracted from spatiotemporal templates. In this study, we use a novel way of generating those templates. Motion History Image (MHI) merges an action sequence into a single template. However, having the problem in overwriting old information by a new one in the MHI, we use a variant named Directional MHI (DMHI) to diffuse the action sequence into four directional templates. And then we use the Local Binary Pattern (LBP) operator, but with a unique way, a rotated bit arranged LBP, to extract the local texture patterns from those DMHI templates. These spatiotemporal patterns form the basis of our action descriptor which is formulated into a concatenated block histogram to serve as a feature vector for action recognition. However, the extracted patterns by LBP tends to lose the temporal information in a DMHI, therefore we take a linear combination of the motion history information and texture information to represent an action sequence. We also use some variants of the proposed action representation that include the shape or pose information of the action silhouettes as a form of histogram.We show that, by effective classification of such histograms, i.e., action descriptor, robust human action recognition is possible. We demonstrate the effectiveness of the proposed method along with some variants of the method over two benchmark dataset; the Weizmann dataset and KTH dataset. Our results are directly comparable or superior to the results reported over these datasets. Higher recognition rates found in the experiment suggest that, compared to complex representation, the proposed simple and compact representation can achieve robust recognition of human activity for practical use. Besides the recognition rate, due to the simplicity of the proposed technique, it is also advantageous with respect to computational load.九州工業大学博士学位論文 学位記番号:工博甲第409号 学位授与年月日:平成28年3月25日1.Introduction|2.Action Representation and Recognition|3.Experiments and Results|4.Conclusion九州工業大学平成27年

人の行動の表現と認識に関する研究

In recent years, analyzing human motion and recognizing a performed action from a video sequence has become very important and has been a well-researched topic in the field of computer vision. The reason behind such attention is its diverse applications in different domains like robotics, human computer interaction, video surveillance, controller-free gaming, video indexing, mixed or virtual reality, intelligent environments, etc. There are a number of researches performed on motion recognition in the last few decades. The state of the art action recognition schemes generally use a holistic or a body part based approach to represent actions. Most of the methods provide reasonable recognition results, but they are sometimes not suitable for online or real time systems because of their complexity in action representation. In this thesis, we address this issue by proposing a novel action representation scheme.The proposed action descriptor is based on a basic idea that rather than detecting the exact body parts or analyzing each action sequence, human action can be represented by a distribution of local texture patterns extracted from spatiotemporal templates. In this study, we use a novel way of generating those templates. Motion History Image (MHI) merges an action sequence into a single template. However, having the problem in overwriting old information by a new one in the MHI, we use a variant named Directional MHI (DMHI) to diffuse the action sequence into four directional templates. And then we use the Local Binary Pattern (LBP) operator, but with a unique way, a rotated bit arranged LBP, to extract the local texture patterns from those DMHI templates. These spatiotemporal patterns form the basis of our action descriptor which is formulated into a concatenated block histogram to serve as a feature vector for action recognition. However, the extracted patterns by LBP tends to lose the temporal information in a DMHI, therefore we take a linear combination of the motion history information and texture information to represent an action sequence. We also use some variants of the proposed action representation that include the shape or pose information of the action silhouettes as a form of histogram.We show that, by effective classification of such histograms, i.e., action descriptor, robust human action recognition is possible. We demonstrate the effectiveness of the proposed method along with some variants of the method over two benchmark dataset; the Weizmann dataset and KTH dataset. Our results are directly comparable or superior to the results reported over these datasets. Higher recognition rates found in the experiment suggest that, compared to complex representation, the proposed simple and compact representation can achieve robust recognition of human activity for practical use. Besides the recognition rate, due to the simplicity of the proposed technique, it is also advantageous with respect to computational load.九州工業大学博士学位論文 学位記番号:工博甲第409号 学位授与年月日:平成28年3月25日1.Introduction|2.Action Representation and Recognition|3.Experiments and Results|4.Conclusion九州工業大学平成27年

A Foot Placement Strategy for Robust Bipedal Gait Control

This thesis introduces a new measure of balance for bipedal robotics called the foot placement estimator (FPE). To develop this measure, stability first is defined for a simple biped. A proof of the stability of a simple biped in a controls sense is shown to exist using classical methods for nonlinear systems. With the addition of a contact model, an analytical solution is provided to define the bounds of the region of stability. This provides the basis for the FPE which estimates where the biped must step in order to be stable. By using the FPE in combination with a state machine, complete gait cycles are created without any precalculated trajectories. This includes gait initiation and termination. The bipedal model is then advanced to include more realistic mechanical and environmental models and the FPE approach is verified in a dynamic simulation. From these results, a 5-link, point-foot robot is designed and constructed to provide the final validation that the FPE can be used to provide closed-loop gait control. In addition, this approach is shown to demonstrate significant robustness to external disturbances. Finally, the FPE is shown in experimental results to be an unprecedented estimate of where humans place their feet for walking and jumping, and for stepping in response to an external disturbance

Humanoid robot control of complex postural tasks based on learning from demostration

Mención Internacional en el título de doctorThis thesis addresses the problem of planning and controlling complex tasks in a humanoid robot from a postural point of view. It is motivated by the growth of robotics in our current society, where simple robots are being integrated. Its objective is to make an advancement in the development of complex behaviors in humanoid robots, in order to allow them to share our environment in the future. The work presents different contributions in the areas of humanoid robot postural control, behavior planning, non-linear control, learning from demonstration and reinforcement learning. First, as an introduction of the thesis, a group of methods and mathematical formulations are presented, describing concepts such as humanoid robot modelling, generation of locomotion trajectories and generation of whole-body trajectories. Next, the process of human learning is studied in order to develop a novel method of postural task transference between a human and a robot. It uses the demonstrated action goal as a metrics of comparison, which is codified using the reward associated to the task execution. As an evolution of the previous study, this process is generalized to a set of sequential behaviors, which are executed by the robot based on human demonstrations. Afterwards, the execution of postural movements using a robust control approach is proposed. This method allows to control the desired trajectory even with mismatches in the robot model. Finally, an architecture that encompasses all methods of postural planning and control is presented. It is complemented by an environment recognition module that identifies the free space in order to perform path planning and generate safe movements for the robot. The experimental justification of this thesis was developed using the humanoid robot HOAP-3. Tasks such as walking, standing up from a chair, dancing or opening a door have been implemented using the techniques proposed in this work.Esta tesis aborda el problema de la planificación y control de tareas complejas de un robot humanoide desde el punto de vista postural. Viene motivada por el auge de la robótica en la sociedad actual, donde ya se están incorporando robots sencillos y su objetivo es avanzar en el desarrollo de comportamientos complejos en robots humanoides, para que en el futuro sean capaces de compartir nuestro entorno. El trabajo presenta diferentes contribuciones en las áreas de control postural de robots humanoides, planificación de comportamientos, control no lineal, aprendizaje por demostración y aprendizaje por refuerzo. En primer lugar se desarrollan un conjunto de métodos y formulaciones matemáticas sobre los que se sustenta la tesis, describiendo conceptos de modelado de robots humanoides, generación de trayectorias de locomoción y generación de trayectorias del cuerpo completo. A continuación se estudia el proceso de aprendizaje humano, para desarrollar un novedoso método de transferencia de una tarea postural de un humano a un robot, usando como métrica de comparación el objetivo de la acción demostrada, que es codificada a través del refuerzo asociado a la ejecución de dicha tarea. Como evolución del trabajo anterior, se generaliza este proceso para la realización de un conjunto de comportamientos secuenciales, que son de nuevo realizados por el robot basándose en las demostraciones de un ser humano. Seguidamente se estudia la ejecución de movimientos posturales utilizando un método de control robusto ante imprecisiones en el modelado del robot. Para analizar, se presenta una arquitectura que aglutina los métodos de planificación y el control postural desarrollados en los capítulos anteriores. Esto se complementa con un módulo de reconocimiento del entorno y extracción del espacio libre para poder planificar y generar movimientos seguros en dicho entorno. La justificación experimental de la tesis se ha desarrollado con el robot humanoide HOAP-3. En este robot se han implementado tareas como caminar, levantarse de una silla, bailar o abrir una puerta. Todo ello haciendo uso de las técnicas propuestas en este trabajo.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Manuel Ángel Armada Rodríguez.- Secretario: Luis Santiago Garrido Bullón.- Vocal: Sylvain Calino