광섬유 힘 센서가 내장된 로봇 원격 및 무인 조작을 위한 모듈화 로봇 스킨

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2021.8. 박용래.Robots have been used to replace human workers for dangerous and difficult tasks that require human-like dexterity. To perform sophisticated tasks, force and tactile sensing is one of the key requirements to achieve dexterous manipulation. Robots equipped with sensitive skin that can play a role of mechanoreception in animals will be able to perform tasks with high levels of dexterity. In this research, we propose modularized robotic skin that is capable of not only localizing external contacts but also estimating the magnitudes of the contact forces. In order to acquire three pieces of key information on a contact, such as contact locations in horizontal and vertical directions and the magnitude of the force, each skin module requires three degrees of freedom in sensing. In the proposed skin, force sensing is achieved by a custom-designed triangular beam structure. A force applied to the outer surface of the skin module is transmitted to the beam structure underneath, and bending of the beam is detected by fiber optic strain sensors, called fiber Bragg gratings. The proposed skin shows resolutions of 1.45 N for force estimation and 1.85 mm and 1.91 mm for contact localization in horizontal and vertical directions, respectively. We also demonstrate applications of the proposed skin for remote and autonomous operations of commercial robotic arms equipped with an array of the skin modules.로봇은 인간과 같은 높은 조작성이 필요한 어려운 작업 환경이나 위험한 환경에서 인간을 대체할 수 있도록 연구되고 있다. 이를 위해 동물의 기계적 감응(mechanoreception) 역할과 같은 기능을 수행하면서 로봇에 부착될 수 있는 스킨을 연구하고 있고, 민감한 로봇 스킨이 부착된 로봇은 높은 수준의 조작성을 가지고 주어진 작업을 성공할 수 있다. 다시 말해 로봇의 힘 센싱과 촉각 센싱 기능은 정교한 로봇 조작의 핵심 요소들 중 하나로 로봇의 세밀한 작업들을 수행하기 필요로 하다. 따라서 우리는 이 연구에서 외부 접촉의 위치뿐만 아니라 외력의 크기도 추정할 수 있는 모듈화된 로봇 스킨을 제안한다. 접촉 힘의 크기, 접촉의 수직 및 수평 위치 등 접촉에 대한 3가지 정보를 얻기 위해서 각 스킨 모듈은 3 자유도를 가지도록 설계하였다. 제안한 스킨에서 힘 센싱은 새롭게 설계한 삼각형 형태의 빔 구조의 변형을 통해서 측정한다. 구체적으로 스킨 모듈의 외피에 가해진 힘은 빔 구조로 전달되고, 이로 인해 발생하는 빔 구조의 변형은 “fiber Bragg gratings” 이라고 불리는 광섬유 스트레인 센서들에 의해서 측정된다. 제안한 스킨은 1.45 N의 힘 추정 해상도를 가지고, 수평 및 수직 위치 추정은 각각 1.85 mm와 1.91 mm의 해상도를 가진다. 우리는 상용화된 로봇팔에 여러 개의 스킨 모듈을 배열 및 부착하여 로봇의 원격 조작 및 무인 조작을 실행하였고, 스킨의 활용성을 검증하였다.1. Introduction 1 2. Design 7 2.1. Skin Module . 2.2. Skin Array . 3. Modeling 12 3.1. FBG Sensing Principle and Temperature Compensation 25 3.2. Estimation of Beam Force and Deflection . 3.3. Estimation of Spring Force . 3.4. Estimation of Contact Locations and Force . 4. Experiments 25 4.1. Experimental Setup . 4.2. Initialization . 4.3. Parameter Optimization . 4.4. Result . 5. Application 32 5.1. Remote Robot Manipulation . 5.2. Autonomous Robot Control . 6. Discussion 46 7. Conclusion 48 8. Appendix 49 8.1. Beam Deflection . Bibliography 52 Abstract in Korean 60석

Soft Robotic Grippers

Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research

The TacTip Family : Soft Optical Tactile Sensors with 3D-Printed Biomimetic Morphologies

The authors thank Sam Coupland, Gareth Griffiths, and Samuel Forbes for their help with 3D printing and Jason Welsby for his assistance with electronics. N.L. was supported, in part, by a Leverhulme Trust Research Leadership Award on “A biomimetic forebrain for robot touch” (RL-2016-039), and N.L. and M.E.G. were supported, in part, by an EPSRC grant on Tactile Super-resolution Sensing (EP/M02993X/1). L.C. was supported by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE).Peer reviewedPublisher PD

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots

Fiber Bragg Gratings as e-Health Enablers: An Overview for Gait Analysis Applications

Nowadays, the fast advances in sensing technologies and ubiquitous wireless networking are reflected in medical practice. It provides new healthcare advantages under the scope of e-Health applications, enhancing life quality of citizens. The increase of life expectancy of current population comes with its challenges and growing health risks, which include locomotive problems. Such impairments and its rehabilitation require a close monitoring and continuous evaluation, which add financial burdens on an already overloaded healthcare system. Analysis of body movements and gait pattern can help in the rehabilitation of such problems. These monitoring systems should be noninvasive and comfortable, in order to not jeopardize the mobility and the day-to-day activities of citizens. The use of fiber Bragg gratings (FBGs) as e-Health enablers has presented itself as a new topic to be investigated, exploiting the FBGs’ advantages over its electronic counterparts. Although gait analysis has been widely assessed, the use of FBGs in biomechanics and rehabilitation is recent, with a wide field of applications. This chapter provides a review of the application of FBGs for gait analysis monitoring, namely its use in topics such as the monitoring of plantar pressure, angle, and torsion and its integration in rehabilitation exoskeletons and for prosthetic control

Anthropomorphic robot finger with multi-point tactile sensation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 84-95).The goal of this research is to develop the prototype of a tactile sensing platform for anthropomorphic manipulation research. We investigate this problem through the fabrication and simple control of a planar 2-DOF robotic finger inspired by anatomic consistency, self-containment, and adaptability. The robot is equipped with a tactile sensor array based on optical transducer technology whereby localized changes in light intensity within an illuminated foam substrate correspond to the distribution and magnitude of forces applied to the sensor surface plane [58]. The integration of tactile perception is a key component in realizing robotic systems which organically interact with the world. Such natural behavior is characterized by compliant performance that can initiate internal, and respond to external, force application in a dynamic environment. However, most of the current manipulators that support some form of haptic feedback, either solely derive proprioceptive sensation or only limit tactile sensors to the mechanical fingertips. These constraints are due to the technological challenges involved in high resolution, multi-point tactile perception. In this work, however, we take the opposite approach, emphasizing the role of full-finger tactile feedback in the refinement of manual capabilities. To this end, we propose and implement a control framework for sensorimotor coordination analogous to infant-level grasping and fixturing reflexes. This thesis details the mechanisms used to achieve these sensory, actuation, and control objectives, along with the design philosophies and biological influences behind them. The results of behavioral experiments with the tactilely-modulated control scheme are also described. The hope is to integrate the modular finger into an engineered analog of the human hand with a complete haptic system.by Jessica Lauren Banks.S.M

Soft Biomimetic Finger with Tactile Sensing and Sensory Feedback Capabilities

The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine (SVM) classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over sixteen independent parameters when tested on thirteen standardized textured surfaces. The sixteen parameters were the combination of four angles of flexion of the soft finger and four speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation (TENS) to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provides sensory feedback; furthermore, texture feedback has the potential to enhance the user experience when interacting with their surroundings. Additionally, this work showed that an inexpensive, soft biomimetic finger combined with a flexible tactile sensor array can potentially help users perceive their environment better