Design of a 3 DOFs parallel actuated mechanism for a biped hip joint

Proceedings of the 2002 IEEE International Conference on Robotics & Automation, Washington, DC, May 200

Design and Development of the Biped Prototype ROBIAN

Proceedings of the 2002 IEEE International Conference on Robotics & Automation, Washington, DC, May 200

The Penn Jerboa: A Platform for Exploring Parallel Composition of Templates

We have built a 12DOF, passive-compliant legged, tailed biped actuated by four brushless DC motors. We anticipate that this machine will achieve varied modes of quasistatic and dynamic balance, enabling a broad range of locomotion tasks including sitting, standing, walking, hopping, running, turning, leaping, and more. Achieving this diversity of behavior with a single under-actuated body, requires a correspondingly diverse array of controllers, motivating our interest in compositional techniques that promote mixing and reuse of a relatively few base constituents to achieve a combinatorially growing array of available choices. Here we report on the development of one important example of such a behavioral programming method, the construction of a novel monopedal sagittal plane hopping gait through parallel composition of four decoupled 1DOF base controllers. For this example behavior, the legs are locked in phase and the body is fastened to a boom to restrict motion to the sagittal plane. The platform's locomotion is powered by the hip motor that adjusts leg touchdown angle in flight and balance in stance, along with a tail motor that adjusts body shape in flight and drives energy into the passive leg shank spring during stance. The motor control signals arise from the application in parallel of four simple, completely decoupled 1DOF feedback laws that provably stabilize in isolation four corresponding 1DOF abstract reference plants. Each of these abstract 1DOF closed loop dynamics represents some simple but crucial specific component of the locomotion task at hand. We present a partial proof of correctness for this parallel composition of template reference systems along with data from the physical platform suggesting these templates are anchored as evidenced by the correspondence of their characteristic motions with a suitably transformed image of traces from the physical platform.Comment: Technical Report to Accompany: A. De and D. Koditschek, "Parallel composition of templates for tail-energized planar hopping," in 2015 IEEE International Conference on Robotics and Automation (ICRA), May 2015. v2: Used plain latex article, correct gap radius and specific force/torque number

A Variable Stiffness Actuator Module With Favorable Mass Distribution for a Bio-inspired Biped Robot

Achieving human-like locomotion with humanoid platforms often requires the use of variable stiffness actuators (VSAs) in multi-degree-of-freedom robotic joints. VSAs possess 2 motors for the control of both stiffness and equilibrium position. Hence, they add mass and mechanical complexity to the design of humanoids. Mass distribution of the legs is an important design parameter, because it can have detrimental effects on the cost of transport. This work presents a novel VSA module, designed to be implemented in a bio-inspired humanoid robot, Binocchio, that houses all components on the same side of the actuated joint. This feature allowed to place the actuator's mass to more proximal locations with respect to the actuated joint instead of concentrating it at the joint level, creating a more favorable mass distribution in the humanoid. Besides, it also facilitated it's usage in joints with centralized multi-degree of freedom (DoF) joints instead of cascading single DoF modules. The design of the VSA module is presented, including it's integration in the multi-DoFs joints of Binocchio. Experiments validated the static characteristics of the VSA module to accurately estimate the output torque and stiffness. The dynamic responses of the driving and stiffening mechanisms are shown. Finally, experiments show the ability of the actuation system to replicate the envisioned human-like kinematic, torque and stiffness profiles for Binocchio

Parallel architectures for humanoid robots

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The structure of humanoid robots can be inspired to human anatomy and operation with open challenges in mechanical performance that can be achieved by using parallel kinematic mechanisms. Parallel mechanisms can be identified in human anatomy with operations that can be used for designing parallel mechanisms in the structure of humanoid robots. Design issues are outlined as requirements and performance for parallel mechanisms in humanoid structures. The example of LARMbot humanoid design is presented as from direct authors’ experience to show an example of the feasibility and efficiency of using parallel mechanisms in humanoid structures. This work is an extension of a paper presented at ISRM 2019 conference (International Symposium on Robotics and Mechatronics)

사람의 자연스러운 보행 동작 생성을 위한 물리 시뮬레이션 기반 휴머노이드 제어 방법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 8. 이제희.휴머노이드를 제어하여 사람의 자연스러운 이동 동작을 만들어내는 것은 컴퓨터그래픽스 및 로봇공학 분야에서 중요한 문제로 생각되어 왔다. 하지만, 이는 사람의 이동에서 구동기가 부족한 (underactuated) 특성과 사람의 몸의 복잡한 구조를 모방하고 시뮬레이션해야 한다는 점 때문에 매우 어려운 문제로 알려져왔다. 본 학위논문은 물리 시뮬레이션 기반 휴머노이드가 외부의 변화에 안정적으로 대응하고 실제 사람처럼 자연스럽고 다양한 이동 동작을 만들어내도록 하는 제어 방법을 제안한다. 우리는 실제 사람으로부터 얻을 수 있는 관찰 가능하고 측정 가능한 데이터를 최대한으로 활용하여 문제의 어려움을 극복했다. 우리의 접근 방법은 모션 캡처 시스템으로부터 획득한 사람의 모션 데이터를 활용하며, 실제 사람의 측정 가능한 물리적, 생리학적 특성을 복원하여 사용하는 것이다. 우리는 토크로 구동되는 이족 보행 모델이 다양한 스타일로 걸을 수 있도록 제어하는 데이터 기반 알고리즘을 제안한다. 우리의 알고리즘은 모션 캡처 데이터에 내재된 이동 동작 자체의 강건성을 활용하여 실제 사람과 같은 사실적인 이동 제어를 구현한다. 구체적으로는, 참조 모션 데이터를 재현하는 자연스러운 보행 시뮬레이션을 위한 관절 토크를 계산하게 된다. 알고리즘에서 가장 핵심적인 아이디어는 간단한 추종 제어기만으로도 참조 모션을 재현할 수 있도록 참조 모션을 연속적으로 조절하는 것이다. 우리의 방법은 모션 블렌딩, 모션 와핑, 모션 그래프와 같은 기존에 존재하는 데이터 기반 기법들을 이족 보행 제어에 활용할 수 있게 한다. 우리는 보다 사실적인 이동 동작을 생성하기 위해 사람의 몸을 세부적으로 모델링한, 근육에 의해 관절이 구동되는 인체 모델을 제어하는 이동 제어 시스템을 제안한다. 시뮬레이션에 사용되는 휴머노이드는 실제 사람의 몸에서 측정된 수치들에 기반하고 있으며 최대 120개의 근육을 가진다. 우리의 알고리즘은 최적의 근육 활성화 정도를 계산하여 시뮬레이션을 수행하며, 참조 모션을 충실히 재현하거나 혹은 새로운 상황에 맞게 모션을 적응시키기 위해 주어진 참조 모션을 수정하는 방식으로 동작한다. 우리의 확장가능한 알고리즘은 다양한 종류의 근골격 인체 모델을 최적의 근육 조합을 사용하며 균형을 유지하도록 제어할 수 있다. 우리는 다양한 스타일로 걷기 및 달리기, 모델의 변화 (근육의 약화, 경직, 관절의 탈구), 환경의 변화 (외력), 목적의 변화 (통증의 감소, 효율성의 최대화)에 대한 대응, 방향 전환, 회전, 인터랙티브하게 방향을 바꾸며 걷기 등과 같은 보다 난이도 높은 동작들로 이루어진 예제를 통해 우리의 접근 방법이 효율적임을 보였다.Controlling artificial humanoids to generate realistic human locomotion has been considered as an important problem in computer graphics and robotics. However, it has been known to be very difficult because of the underactuated characteristics of the locomotion dynamics and the complex human body structure to be imitated and simulated. In this thesis, we presents controllers for physically simulated humanoids that exhibit a rich set of human-like and resilient simulated locomotion. Our approach exploits observable and measurable data of a human to effectively overcome difficulties of the problem. More specifically, our approach utilizes observed human motion data collected by motion capture systems and reconstructs measured physical and physiological properties of a human body. We propose a data-driven algorithm to control torque-actuated biped models to walk in a wide range of locomotion skills. Our algorithm uses human motion capture data and realizes an human-like locomotion control facilitated by inherent robustness of the locomotion motion. Concretely, it takes reference motion and generates a set of joint torques to generate human-like walking simulation. The idea is continuously modulating the reference motion such that even a simple tracking controller can reproduce the reference motion. A number of existing data-driven techniques such as motion blending, motion warping, and motion graph can facilitate the biped control with this framework. We present a locomotion control system that controls detailed models of a human body with the musculotendon actuating process to create more human-like simulated locomotion. The simulated humanoids are based on measured properties of a human body and contain maximum 120 muscles. Our algorithm computes the optimal coordination of muscle activations and actively modulates the reference motion to fathifully reproduce the reference motion or adapt the motion to meet new conditions. Our scalable algorithm can control various types of musculoskeletal humanoids while seeking harmonious coordination of many muscles and maintaining balance. We demonstrate the strength of our approach with examples that allow simulated humanoids to walk and run in various styles, adapt to change of models (e.g., muscle weakness, tightness, joint dislocation), environments (e.g., external pushes), goals (e.g., pain reduction and efficiency maximization), and perform more challenging locomotion tasks such as turn, spin, and walking while steering its direction interactively.Contents Abstract i Contents iii List of Figures v 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.1 Computer Graphics Perspective . . . . . . . . . . . . . . . . . 3 1.1.2 Robotics Perspective . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.3 Biomechanics Perspective . . . . . . . . . . . . . . . . . . . . 7 1.2 Aim of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Previous Work 16 2.1 Biped Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.1 Controllers with Optimization . . . . . . . . . . . . . . . . . . 18 2.1.2 Controllers with Motion Capture Data . . . . . . . . . . . . . 20 2.2 Simulation of Musculoskeletal Humanoids . . . . . . . . . . . . . . . 21 2.2.1 Simulation of Specic Body Parts . . . . . . . . . . . . . . . . 21 2.2.2 Simulation of Full-Body Models . . . . . . . . . . . . . . . . . 22 2.2.3 Controllers for Musculoskeletal Humanoids . . . . . . . . . . . 23 3 Data-Driven Biped Control 24 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Data-Driven Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.2 Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4 Locomotion Control for Many-Muscle Humanoids 56 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2 Humanoid Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.2.1 Muscle Force Generation . . . . . . . . . . . . . . . . . . . . . 61 4.2.2 Muscle Force Transfer . . . . . . . . . . . . . . . . . . . . . . 64 4.2.3 Equation of Motion . . . . . . . . . . . . . . . . . . . . . . . . 66 4.3 Muscle Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.3.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.3.2 Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.3.3 Quadratic Programming Formulation . . . . . . . . . . . . . . 70 4.4 Trajectory Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5 Conclusion 84 A Mathematical Definitions 88 A.1 Definitions of Transition Function . . . . . . . . . . . . . . . . . . . . 88 B Humanoid Models 89 B.1 Torque-Actuated Biped Models . . . . . . . . . . . . . . . . . . . . . 89 B.2 Many-Muscle Humanoid Models . . . . . . . . . . . . . . . . . . . . . 91 C Dynamics of Musculotendon Actuators 94 C.1 Contraction Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 94 C.2 Initial Muscle States . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Glossary for Medical Terms 99 Bibliography 102 초록 113Docto