Soft actuator and agile soft robot

Robots play an important part in many aspects of our society by doing repetitive, dangerous, or precision tasks. Most existing robots are made of rigid components, which lack passive compliance and pose a challenge in adapting to the environment and safe human-robot interaction. Rigid robots may be equipped with sensors and programmed with proprioceptive feedback control to achieve active compliance, but this may fail in the event of unforeseen situations or sensor failure. In contrast, animals have evolved flexible or soft body parts to help them adapt to changing environments. Soft robotics is an emerging field in robotics, drawing inspiration from nature by integrating soft material into the actuator and mechanical design. With the inclusion of soft material, soft actuators and robots can deform actively/passively, making it possible to sense, absorb impact, and adapt to its environment with deformation. However, while soft actuators/robots have superior properties to rigid ones, they are often challenging to manufacture and control precisely. In addition, they may suffer from slow speed and material degradation. Thus, in this thesis, we aim to address the issues in developing high-performance soft actuators and soft robots. The thesis is divided into two parts. In the first part, we focus on improving the manufacturability and performance of a self-contained soft actuator originated in the Creative Machines Lab. The soft actuator is composed of a cured silicone-ethanol mixture embedded with heating coils. When the coils are electrically actuated, ethanol trapped inside undergoes liquid-vapor transitions, and thus the actuator undergoes extreme volume change. While this actuator exhibits high strain and high stress, it is very slow to actuate, has limited life cycles, and requires molds to manufacture. The first part of the thesis will address these issues. Specifically, in chapter 2, we discuss using multi-material 3D printing to automate the manufacturing of silicone-ethanol composite. In chapter 3, we discuss using laser-cut flexible Kirigami patterns to improve the manufacturability of its heating element. Chapter 4 characterizes its actuation profile and addresses improvements to the thermal conductivity by infusing thermally conductive fillers. Soft actuation is an actively researched area; however, many high-performance soft actuators are challenging to manufacture and thus are less accessible to the general robotics community. Conventional actuators such as electric motors are widely available but lack flexibility. Therefore, the second part of the thesis aims at combining rigid motors with soft materials to design and control high-performance hybrid soft robots. Simulation is a good way to evaluate and optimize robot design and control. However, existing simulators that support motor-driven soft robots have limited features. Chapter 5 discusses this issue and presents a physically based real-time soft robot simulator capable of simulating motor-driven soft robots. In addition, chapter 5 presents the design and control of a 3D printed hybrid soft quadruped robot. Chapter 6 presents the design and control of a 3D printed hybrid soft humanoid robot. The two parts of the thesis aim to improve aspects in soft actuators and soft robots. In conclusion, we summarize the lessons learned in developing soft actuators/robots and new possibilities and challenges for advancing soft robotics research

Learning Terrain Dynamics: A Gaussian Process Modeling and Optimal Control Adaptation Framework Applied to Robotic Jumping

The complex dynamics characterizing deformable terrain presents significant impediments toward the real-world viability of locomotive robotics, particularly for legged machines. We explore vertical, robotic jumping as a model task for legged locomotion on presumed-uncharacterized, nonrigid terrain. By integrating Gaussian process (GP)-based regression and evaluation to estimate ground reaction forces as a function of the state, a 1-D jumper acquires the capability to learn forcing profiles exerted by its environment in tandem with achieving its control objective. The GP-based dynamical model initially assumes a baseline rigid, noncompliant surface. As part of an iterative procedure, the optimizer employing this model generates an optimal control strategy to achieve a target jump height. Experiential data recovered from execution on the true surface model are applied to train the GP, in turn, providing the optimizer a more richly informed dynamical model of the environment. The iterative control-learning procedure was rigorously evaluated in experiment, over different surface types, whereby a robotic hopper was challenged to jump to several different target heights. Each task was achieved within ten attempts, over which the terrain's dynamics were learned. With each iteration, GP predictions of ground forcing became incrementally refined, rapidly matching experimental force measurements. The few-iteration convergence demonstrates a fundamental capacity to both estimate and adapt to unknown terrain dynamics in application-realistic time scales, all with control tools amenable to robotic legged locomotion

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Design and analysis of jammable granular systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 102-110).Jamming--the mechanism by which granular media can transition between liquid-like and solid-like states-has recently been demonstrated as a variable strength and stiffness mechanism in a range of applications. As a low-cost and simple means for achieving tunable mechanical properties, jamming has been used in systems ranging from architectural to medical ones. This thesis explores the utility of jamming for robotic manipulation applications, both at a fundamental level of understanding how granular properties affect the performance of jammed systems, and at a more applied level of designing functional robotic components. Specifically, the purpose of this thesis was to enable engineers to design jammable robotic systems in a principled manner. Three parallel yet related studies were conducted to work towards this goal. First, an experimental analysis was conducted to determine whether the bulk shear strength of granular systems can be correlated with grain properties-such as ones concerning shape, size distribution, and surface texture-extracted from 2D silhouettes of grains. Second, a novel medium composed of a mixture of hard and soft spheres was proposed to achieve variable strength and stiffness properties as a function of confining pressure; experimental analysis was conducted on this system with not only varying confining pressures but also varying mixing ratios of hard and soft spheres. Finally, the design and analysis of a novel jammable robotic manipulator-with the goal of maximizing both the strength and articulation of the system-is presented.by Nadia G. Cheng.Ph.D

Analysis of the Workspace of Tendon-based Stewart Platforms

Tendon-based Stewart platforms are a concept for innovative manipulators where the load to move almost coincides with the payload. After an overview over the state of research and some concepts of kinematics (singularity and redundancy), the thesis discusses aspects of the technically usable workspace (positive tendon forces, limits of tension, singularity, stiffness, collisions between tendens). A representation of the controllablwe workspace by means of polynomial inequalities is developed. Optimal solutions are provided to the problem of finding appropriate force distributions in the tendons. These solutions can be discontinuous in time, but they can be approximated with continuous ones. An algorithm is given for this. From these results, a quality measure for workspace is derived and used to state design rules which help achieving good workspaces. For some systems, sample trajectories are simulated.</p