327 research outputs found

    Trajectory generation for continuous leg forces during double support and heel-to-toe shift based on divergent component of motion

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    This paper works with the concept of Divergent Component of Motion (DCM), also called โ€™(instantaneous) Capture Pointโ€™. We present two real-time DCM trajectory generators for uneven (three-dimensional) ground surfaces, which lead to continuous leg (and corresponding ground reaction) force profiles and facilitate the use of toe-off motion during double support. Thus, the resulting DCM trajectories are well suited for real-world robots and allow for increased step length and step height. The performance of the proposed methods was tested in numerous simulations and experiments on IHMCโ€™s Atlas robot and DLRโ€™s humanoid robot TORO

    Simulation and Framework for the Humanoid Robot TigerBot

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    Walking humanoid robotics is a developing field. Different humanoid robots allow for different kinds of testing. TigerBot is a new full-scale humanoid robot with seven degrees-of-freedom legs and with its specifications, it can serve as a platform for humanoid robotics research. Currently TigerBot has encoders set up on each joint, allowing for position control, and its sensors and joints connect to Teensy microcontrollers and the ODroid XU4 single-board computer central control unit. The componentsโ€™ communication system used the Robot Operating System (ROS). This allows the user to control TigerBot with ROS. Itโ€™s important to have a simulation setup so a user can test TigerBotโ€™s capabilities on a model before using the real robot. A working walking gait in the simulation serves as a test of the simulator, proves TigerBotโ€™s capability to walk, and opens further development on other walking gaits. A model of TigerBot was set up using the simulator Gazebo, which allowed testing different walking gaits with TigerBot. The gaits were generated by following the linear inverse pendulum model and the basic zero-moment point (ZMP) concept. The gaits consisted of center of mass trajectories converted to joint angles through inverse kinematics. In simulation while the robot follows the predetermined joint angles, a proportional-integral controller keeps the model upright by modifying the flex joint angle of the ankles. The real robot can also run the gaits while suspended in the air. The model has shown the walking gait based off the ZMP concept to be stable, if slow, and the actual robot has been shown to air walk following the gait. The simulation and the framework on the robot can be used to continue work with this walking gait or they can be expanded on for different methods and applications such as navigation, computer vision, and walking on uneven terrain with disturbances

    Pattern Generation for Walking on Slippery Terrains

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    In this paper, we extend state of the art Model Predictive Control (MPC) approaches to generate safe bipedal walking on slippery surfaces. In this setting, we formulate walking as a trade off between realizing a desired walking velocity and preserving robust foot-ground contact. Exploiting this formulation inside MPC, we show that safe walking on various flat terrains can be achieved by compromising three main attributes, i. e. walking velocity tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient of Friction (RCoF) regulation. Simulation results show that increasing the walking velocity increases the possibility of slippage, while reducing the slippage possibility conflicts with reducing the tip-over possibility of the contact and vice versa.Comment: 6 pages, 7 figure

    Development of a Hybrid Powered 2D Biped Walking Machine Designed for Rough Terrain Locomotion

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    Biped robots hold promise as terrestrial explorers because they require a single discrete foothold to place their next step. However, biped robots are multi-input multi-output dynamically unstable machines. This makes walking on rough terrain difficult at best. Progress has been made with non-periodic rough terrain like stairs or inclines with fully active walking machines. Terrain that requires the walker to change its gait pattern from a standard walk is still problematic. Most walking machines have difficulty detecting or responding to the small perturbations induced by this type of terrain. These small perturbations can lead to unstable gait cycles and possibly a fall. The Intelligent Systems and Automation Lab at the University of Kansas has built a three legged 2D biped walking machine to be used as a test stand for studying rough terrain walking. The specific aim of this research is to investigate how biped walkers can best maintain walking stability when acted upon by small perturbations caused by periodic rough terrain. The first walking machine prototype, referred to as Jaywalker has two main custom actuation systems. The first is the hip ratchet system. It allows the walker to have either a passive or active hip swing. The second is the hybrid parallel ankle actuator. This new actuator uses a pneumatic ram and stepper motor in parallel to produce an easily controlled high torque output. In open loop control it has less than a 1ยฐ tracking error and 0.065 RPM velocity error compared to a standard stepper motor. Step testing was conducted using the Jaywalker, with a passive hip, to determine if a walker with significant leg mass could walk without full body actuation. The results of testing show the Jaywalker is ultimately not capable of walking with a passive hip. However, the walking motion is fine until the terminal stance phase. At this point the legs fall quickly towards the ground as the knee extends the shank. This quick step phenomenon is caused by increased speeds and forces about the leg and hip caused by the extension of the shank. This issue can be overcome by fully actuating the hip, or by adding counterbalances to the legs about the hip

    Augmented Linear Inverted Pendulum Model for Bipedal Gait Planning

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    Ph.DDOCTOR OF PHILOSOPH

    Locomoรงรฃo bรญpede adaptativa a partir de uma รบnica demonstraรงรฃo usando primitivas de movimento

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    Doutoramento em Engenharia EletrotรฉcnicaEste trabalho aborda o problema de capacidade de imitaรงรฃo da locomoรงรฃo humana atravรฉs da utilizaรงรฃo de trajetรณrias de baixo nรญvel codificadas com primitivas de movimento e utilizรก-las para depois generalizar para novas situaรงรตes, partindo apenas de uma demonstraรงรฃo รบnica. Assim, nesta linha de pensamento, os principais objetivos deste trabalho sรฃo dois: o primeiro รฉ analisar, extrair e codificar demonstraรงรตes efetuadas por um humano, obtidas por um sistema de captura de movimento de forma a modelar tarefas de locomoรงรฃo bรญpede. Contudo, esta transferรชncia nรฃo estรก limitada ร  simples reproduรงรฃo desses movimentos, requerendo uma evoluรงรฃo das capacidades para adaptaรงรฃo a novas situaรงรตes, assim como lidar com perturbaรงรตes inesperadas. Assim, o segundo objetivo รฉ o desenvolvimento e avaliaรงรฃo de uma estrutura de controlo com capacidade de modelaรงรฃo das aรงรตes, de tal forma que a demonstraรงรฃo รบnica apreendida possa ser modificada para o robรด se adaptar a diversas situaรงรตes, tendo em conta a sua dinรขmica e o ambiente onde estรก inserido. A ideia por detrรกs desta abordagem รฉ resolver o problema da generalizaรงรฃo a partir de uma demonstraรงรฃo รบnica, combinando para isso duas estruturas bรกsicas. A primeira consiste num sistema gerador de padrรตes baseado em primitivas de movimento utilizando sistemas dinรขmicos (DS). Esta abordagem de codificaรงรฃo de movimentos possui propriedades desejรกveis que a torna ideal para geraรงรฃo de trajetรณrias, tais como a possibilidade de modificar determinados parรขmetros em tempo real, tais como a amplitude ou a frequรชncia do ciclo do movimento e robustez a pequenas perturbaรงรตes. A segunda estrutura, que estรก embebida na anterior, รฉ composta por um conjunto de osciladores acoplados em fase que organizam as aรงรตes de unidades funcionais de forma coordenada. Mudanรงas em determinadas condiรงรตes, como o instante de contacto ou impactos com o solo, levam a modelos com mรบltiplas fases. Assim, em vez de forรงar o movimento do robรด a situaรงรตes prรฉ-determinadas de forma temporal, o gerador de padrรตes de movimento proposto explora a transiรงรฃo entre diferentes fases que surgem da interaรงรฃo do robรด com o ambiente, despoletadas por eventos sensoriais. A abordagem proposta รฉ testada numa estrutura de simulaรงรฃo dinรขmica, sendo que vรกrias experiรชncias sรฃo efetuadas para avaliar os mรฉtodos e o desempenho dos mesmos.This work addresses the problem of learning to imitate human locomotion actions through low-level trajectories encoded with motion primitives and generalizing them to new situations from a single demonstration. In this line of thought, the main objectives of this work are twofold: The first is to analyze, extract and encode human demonstrations taken from motion capture data in order to model biped locomotion tasks. However, transferring motion skills from humans to robots is not limited to the simple reproduction, but requires the evaluation of their ability to adapt to new situations, as well as to deal with unexpected disturbances. Therefore, the second objective is to develop and evaluate a control framework for action shaping such that the single-demonstration can be modulated to varying situations, taking into account the dynamics of the robot and its environment. The idea behind the approach is to address the problem of generalization from a single-demonstration by combining two basic structures. The first structure is a pattern generator system consisting of movement primitives learned and modelled by dynamical systems (DS). This encoding approach possesses desirable properties that make them well-suited for trajectory generation, namely the possibility to change parameters online such as the amplitude and the frequency of the limit cycle and the intrinsic robustness against small perturbations. The second structure, which is embedded in the previous one, consists of coupled phase oscillators that organize actions into functional coordinated units. The changing contact conditions plus the associated impacts with the ground lead to models with multiple phases. Instead of forcing the robotโ€™s motion into a predefined fixed timing, the proposed pattern generator explores transition between phases that emerge from the interaction of the robot system with the environment, triggered by sensor-driven events. The proposed approach is tested in a dynamics simulation framework and several experiments are conducted to validate the methods and to assess the performance of a humanoid robot

    ์‚ฌ๋žŒ ๋ณดํ–‰ ๋ถ„์„ ์—ฐ๊ตฌ์™€ ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ํ™œ์šฉํ•œ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ํŒจํ„ด ์ƒ์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(์ง€๋Šฅํ˜•์œตํ•ฉ์‹œ์Šคํ…œ์ „๊ณต), 2020. 8. ๋ฐ•์žฌํฅ.๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์€ ๋ณดํ–‰์˜ ์•ˆ์ •์„ฑ์„ ๋–จ์–ดํŠธ๋ฆฌ๋Š” ์š”์ธ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ณดํ–‰ ์ค‘ ๋ฐœ์— ๋ฐœ์ƒํ•˜๋Š” ์ˆ˜ํ‰ ์ „๋‹จ๋ ฅ์ด ๋ฐœ๊ณผ ์ง€๋ฉด ์‚ฌ์ด์˜ ๋งˆ์ฐฐ๋ ฅ๋ณด๋‹ค ์ปค์ง€๋ฉด, ๋ฐœ์€ ์ ‘์ด‰์„ ์ƒ์‹คํ•˜๊ณ  ๋ฏธ๋„๋Ÿฌ์ง€๊ฒŒ ๋œ๋‹ค. ์—ฌ๊ธฐ์„œ, ๋ฐœ๊ณผ ์ง€๋ฉด ์‚ฌ์ด์˜ ๋งˆ์ฐฐ๋ ฅ์€ ๋ฐœ์— ์ž‘์šฉํ•˜๋Š” ์ˆ˜์ง๋ ฅ์— ์˜ํ•ด ๊ฒฐ์ •๋˜๊ฒŒ ๋œ๋‹ค. ์ฆ‰, ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ํŒจํ„ด ์ƒ์„ฑ์˜ ์ธก๋ฉด์—์„œ ๋ณด์ž๋ฉด, ๋กœ๋ด‡ ๋ฐœ์— ๋ฐœ์ƒํ•˜๋Š” ์ˆ˜ํ‰๋ ฅ๊ณผ ์ˆ˜์ง๋ ฅ์„ ์–ด๋–ป๊ฒŒ ์„ค๊ณ„ํ•˜๋Š”์ง€์— ๋”ฐ๋ผ ๋ณดํ–‰ ์ค‘ ๋ฏธ๋„๋Ÿฌ์ง์˜ ๊ฐ€๋Šฅ์„ฑ์ด ๋ฐ”๋€๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์€ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์  ์ƒ์„ฑ์„ ์œ„ํ•ด ์ž์ฃผ ์‚ฌ์šฉ๋˜์–ด์™”๋‹ค. ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์€ ๋กœ๋ด‡์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๋†’์ด๋ฅผ ์ผ์ •ํ•˜๊ฒŒ ์œ ์ง€ํ•˜๋„๋ก ์ œํ•œํ•œ๋‹ค. ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ๋†’์ด ์ œํ•œ ๋•Œ๋ฌธ์— ๋กœ๋ด‡์˜ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๊ฐ€์†๋„๋Š” ๋ณดํ–‰ ์†๋„์™€ ๊ด€๋ จ ์—†์ด ํ•ญ์ƒ ์ค‘๋ ฅ ๊ฐ€์†๋„๊ฐ€ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ˆ˜ํ‰ ๋ฐฉํ–ฅ์˜ ๊ฐ€์†๋„๋Š” ๋ณดํ–‰ ์†๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๋ฉด ๋น„๋ก€ํ•˜์—ฌ ์ฆ๊ฐ€ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋น ๋ฅธ ๋ณดํ–‰ ์†๋„์—์„œ๋Š” ์ˆ˜์ง๋ ฅ์— ๋น„๋ก€ํ•˜๋Š” ๋งˆ์ฐฐ๋ ฅ์— ๋น„ํ•ด ์ˆ˜ํ‰ ์ „๋‹จ๋ ฅ์ด ์ปค์ง€๋ฉด์„œ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์— ์˜ํ•œ ์ผ์ •ํ•œ ์ˆ˜์ง ๋†’์ด ๊ตฌ์† ์กฐ๊ฑด์ด ๋กœ๋ด‡ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์„ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์ ์ ˆํ•œ ์ˆ˜์ง ์›€์ง์ž„์„ ์ƒ์„ฑํ•จ์œผ๋กœ์จ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ์ค‘ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ์ธ๊ฐ„๊ณตํ•™ ๋ถ„์•ผ์—์„œ๋Š” Available Coefficient of Friction(aCOF)๊ณผ Utilized Coefficient of Friction(uCOF)์„ ์ด์šฉํ•˜์—ฌ ์‚ฌ๋žŒ ๋ณดํ–‰ ์ค‘ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง ๊ฐ€๋Šฅ์„ฑ์„ ์˜ˆ์ธกํ•˜๋Š” ์—ฐ๊ตฌ๋“ค์ด ์ˆ˜ํ–‰๋๋‹ค. ์—ฌ๊ธฐ์„œ, aCOF๋Š” ๋‘ ๋ฌผ์ฒด์˜ ์žฌ์งˆ์ด๋‚˜ ์ƒํƒœ์— ์˜ํ•ด ๊ฒฐ์ •๋˜๋Š” ๋งˆ์ฐฐ ๊ณ„์ˆ˜์ด๋‹ค. ๋ฐ˜๋ฉด, uCOF๋Š” ๋ณดํ–‰ ์ค‘ ์ง€์ง€ํ•˜๋Š” ๋ฐœ์— ๊ฐ€ํ•ด์ง€๋Š” ์ˆ˜ํ‰ ์ „๋‹จ๋ ฅ๊ณผ ์ˆ˜์ง๋ ฅ์˜ ๋น„์ด๋‹ค. ์ธ๊ฐ„๊ณตํ•™ ์—ฐ๊ตฌ๋“ค์— ๋”ฐ๋ฅด๋ฉด, uCOF๊ฐ€ aCOF๋ฅผ ์ดˆ๊ณผํ•  ๋•Œ ๋ฐœ์€ ์ ‘์ด‰์„ ์ƒ์‹คํ•˜๊ณ  ๋ฏธ๋„๋Ÿฌ์ง€๊ฒŒ ๋œ๋‹ค. ๋กœ๋ด‡ ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง ๊ฐ์†Œ๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋กœ๋ด‡ ๋ณดํ–‰ ์ค‘ ๋ฐœ์— ๋ฐœ์ƒํ•˜๋Š” uCOF๊ฐ€ ๋กœ๋ด‡ ๋ฐœ๊ณผ ์ง€๋ฉด ์‚ฌ์ด์˜ aCOF ๋ณด๋‹ค ์ž‘์•„์ง€๋„๋ก ์ ์ ˆํ•œ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ๋‹ค์–‘ํ•œ ํ˜•ํƒœ์˜ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์  ์ƒ์„ฑ์ด ๊ฐ€๋Šฅํ•œ๋ฐ, ๊ฐ„๋‹จํ•˜๋ฉด์„œ๋„ ํšจ์œจ์ ์ธ ๋ฐฉ๋ฒ•์€ ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์—๋„ˆ์ง€๊ฐ€ ๋ณด์กด๋˜๋„๋ก ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ธฐ์กด ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์„ ์ด์šฉํ•ด ์ˆ˜ํ‰ ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ƒ์„ฑํ•˜๊ณ , ์šด๋™ ์—๋„ˆ์ง€์™€ ์œ„์น˜ ์—๋„ˆ์ง€๊ฐ€ ๊ตํ™˜๋˜๋ฉด์„œ ์ „์ฒด ์—๋„ˆ์ง€๊ฐ€ ๋ณด์กด๋˜๋Š” ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ถ”๊ฐ€ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์—๋„ˆ์ง€ ๋ณด์กด ์›๋ฆฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์–‘์˜ ์ผ(Mechanical Work) ์ƒ์„ฑ์„ ์ตœ์†Œํ™”ํ•จ์œผ๋กœ์จ ๊ด€์ ˆ์˜ ์–‘์˜ ์ผ ์ƒ์„ฑ์„ ๊ฐ์†Œ์‹œํ‚ค๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๋ณดํ–‰ ์ค‘ ์—๋„ˆ์ง€ ํšจ์œจ์„ ๋†’์ด๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด ๋…ผ๋ฌธ์€ ๋ฐœ๊ณผ ์ง€๋ฉด ์‚ฌ์ด์˜ aCOF ๋ณด๋‹ค ์ž‘๋„๋ก ๋ณดํ–‰ ์ค‘ uCOF๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ ๋ฌด๊ฒŒ ์ค‘์‹ฌ์˜ ์–‘์˜ ์ผ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ์ ์ ˆํ•œ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋ฐœ์˜ ๋ฏธ๋„๋Ÿฌ์ง์ด ๊ฐ์†Œํ•˜๋ฉด์„œ ์—๋„ˆ์ง€ ํšจ์œจ์ด ๋†’์€ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ํŒจํ„ด ์ƒ์„ฑ์„ ์œ„ํ•ด, ๋จผ์ € ์‚ฌ๋žŒ ๋ณดํ–‰ ์ค‘ uCOF์— ๊ด€ํ•œ ์—ฐ๊ตฌ์™€ ์‚ฌ๋žŒ ๋ณดํ–‰ ์ค‘ ๊ด€์ ˆ์˜ ์ผ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์„ ํ–‰ํ•œ๋‹ค. ์‚ฌ๋žŒ ๋ณดํ–‰์— ๊ด€ํ•œ ๋ถ„์„ ์—ฐ๊ตฌ์™€ ์‚ฌ๋žŒ ๋ณดํ–‰์˜ ์›๋ฆฌ ์ดํ•ด๋ฅผ ํ†ตํ•ด ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ธฐ๋ฐ˜ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์  ์ƒ์„ฑ ๋ฐฉ๋ฒ•์ด ์ œ์‹œ๋œ๋‹ค. ์ œ์‹œ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ๊ตฌํ•ด์ง„ ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ์‹คํ—˜์— ์ ์šฉํ•œ๋‹ค. ๊ถ๊ทน์ ์œผ๋กœ ์ด ๋…ผ๋ฌธ์€, ์ˆ˜์ง ๋ฐฉํ–ฅ์˜ ๋ฌด๊ฒŒ ์ค‘์‹ฌ ๊ถค์ ์„ ์ถ”๊ฐ€ํ•จ์œผ๋กœ์จ ๊ธฐ์กด ์„ ํ˜• ์—ญ์ง„์ž ๋ชจ๋ธ์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜์—ฌ, ๋ฏธ๋„๋Ÿฌ์ง์˜ ๊ฐ€๋Šฅ์„ฑ์ด ๊ฐ์†Œํ•˜๊ณ  ์—๋„ˆ์ง€ ํšจ์œจ์ด ๋†’์€ ํœด๋จธ๋…ธ์ด๋“œ ๋กœ๋ด‡ ๋ณดํ–‰ ํŒจํ„ด์„ ์ƒ์„ฑํ•œ๋‹ค.Foot slippage is one of the factors responsible for the increasing instability during human walking. A slip occurs when the horizontal shear force acting on the foot becomes greater than the frictional force between the foot and the ground, which is proportional to the vertical force. For humanoid robot walking, the possibility of a slip depends upon how the horizontal shear force and vertical force both acting on the foot are designed. In the linear inverted pendulum model (LIPM), which is commonly used to generate the center of mass (COM) trajectory of humanoid robots, the vertical height of the COM is kept constant. The constant height of the COM restricts that the vertical force is always equal to the gravitational force at any walking speed. However, upon increasing the walking speed, the horizontal ground reaction force increases in proportion with the forward and lateral accelerations of the COM. This increase in the horizontal ground reaction force, while the vertical ground force is being constant, suggests that the robot-foot slippage can occur because of the restriction of the vertical motion by the LIPM constraint. By generating the appropriate vertical motion, the robot-foot slippage can be reduced during humanoid robot walking. Researchers in the field of ergonomics have been conducted studies on the relationship between the available coefficient of friction (aCOF) and the utilized coefficient of friction (uCOF) to predict the potential for a slip during human walking. The aCOF is both the static and dynamic coefficient of friction between two objects in contact, and it depends on the properties of the objects. The uCOF is the ratio of the horizontal shear force to the vertical force applied by the supporting foot. Foot slippage occurs when the uCOF exceeds the aCOF. Various types of vertical motion can set the maximum value of the uCOF to be less than the aCOF between the foot and floor for humanoid robot walking. One of the simple and energy-efficient methods is to minimize the mechanical work of the COM by introducing added vertical motion. Therefore, the COM pattern would become more energy efficient by exchanging kinetic energy and potential energy. This thesis aims to generate the appropriate vertical motion of the COM to maintain the utilized coefficient of friction (uCOF) less than the available coefficient of friction between the foot and the ground, and to minimize the mechanical work during humanoid robot walking. Before generating a slip-safe and energy-efficient COM trajectory for humanoid robot walking, studies on analyzing the COM patterns, mechanical work, and uCOF during human walking are conducted to understand the principle of walking. Vertical motions at various speeds are generated using an optimization method. Subsequently, the generated COM motion patterns are used as reference trajectories of the COM for humanoid robot walking. This thesis suggests a way to generate slip-safe and energy-efficient COM patterns, which, in turn, overcome the limitations of the LIPM by adding vertical COM motion.Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Contributions of Thesis 3 1.3 Overviews of Thesis 4 Chapter 2 Dynamics of Walking 5 2.1 Walking Model 5 2.1.1 Linear Inverted Pendulum Model 5 2.1.2 Spring-Loaded Inverted Pendulum Model 6 2.1.3 Extrapolated Center of Mass Dynamics 9 2.2 Walking Theory 11 2.2.1 Step-to-Step Transition 11 Chapter 3 HumanWalking Analysis 13 3.1 Motion Capture for Walking 13 3.1.1 Motion Capture Technology 13 3.1.2 Joint Kinematics and Kinetics 15 3.2 Joint and COM During Human Walking 17 3.2.1 Introduction 17 3.2.2 Methods 19 3.2.3 Change of Joint Angle and the COM 20 3.2.4 Discussion 26 3.3 Slipping During Human Walking 27 3.3.1 Introduction 27 3.3.2 Methods 31 3.3.3 Change of uCOF and GRF 34 3.3.4 Interaction Effect Between Heel Area and Speed 36 3.3.5 Discussion 39 3.4 Mechanical Work During Human Walking 44 3.4.1 Introduction 44 3.4.2 Methods 46 3.4.3 Calculation for Joint Mechanical Work 48 3.4.4 Change of Joint Mechanical Work 51 3.4.5 Change of Stride Parameters 53 3.4.6 Discussion 54 Chapter 4 Robot Walking Pattern Generation 59 4.1 Introduction 59 4.2 Forward and Lateral COM 61 4.2.1 XcoM Method 61 4.2.2 Preview Control Method 63 4.3 Vertical COM 64 4.3.1 Calculation for uCOF 64 4.3.2 Calculation for ZMP 65 4.3.3 Calculation for COM Mechanical Work 66 4.3.4 Optimization for Vertical COM Generation 68 4.3.5 Results of Optimization for Vertical COM 73 4.4 Slipping During Robot Walking 75 4.4.1 Robot Simulation 75 4.4.2 Robot Experiments 77 4.5 Mechanical Work During Robot Walking 81 4.5.1 Robot Simulation 81 4.5.2 Robot Experiments 82 4.6 Discussion 87 4.6.1 Tracking Errors in Robot Experiments 87 4.6.2 Effect of Vertical Motions on Real Net Power 91 4.6.3 Trade-Off Between Efficiency and Stability 92 4.6.4 Difference Between Human and Robot 93 Chapter 5 Conclusions 95 Bibliography 97 Abstract (Korean) 111Docto

    Postural stability during standing and walking and the effects of ageing

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    The postural stability during quiet stance and during walking was investigated in 22 elderly and 20 young subjects. A motion analysis system was used to simultaneously record movements of 14 markers on the body while a force plate recorded movement of the centre of pressure (COP) during stance (but not during walking). The movements of the body during stance could be well described (> 90 % of the variance explained) as a simple inverted pendulum moving about the ankles in the anterior-posterior and medial-lateral directions. This model was applicable to both young and elderly subjects and also predicted the records of COP movement well (r > 0.90). When account was taken of the ground reaction forces the prediction was further improved. The greater COP movements commonly observed in the elderly are shown to be due to increased pendulum sway in the medial-lateral direction, compared to young subjects. The inverted pendulum model also gave an adequate description of the deviations from the mean path ("sway") during walking which are larger than those during stance. The static measurement that best predicts sway during walking is medial-lateral movements of the COP when standing on a compliant surface with the eyes closed. The relationship between muscle strength and COP displacement was examined in a larger group of elderly subjects (N = 56). Maximum voluntary force per cross-sectional area was found not to be correlated with COP movements during quiet stance. This suggests that muscle weakness and increased sway in the elderly have separate physiological causes. A method was developed for inducing a trip-like perturbation of gait as subjects walked on a treadmill. Muscle activation patterns and body kinematics were recorded in 9 young subjects to establish the normative response to such a perturbation with a view to investigating these responses in the elderly
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