3D printed pneumatic soft actuators and sensors: their modeling, performance quantification, control and applications in soft robotic systems

Continued technological progress in robotic systems has led to more applications where robots and humans operate in close proximity and even physical contact in some cases. Soft robots, which are primarily made of highly compliant and deformable materials, provide inherently safe features, unlike conventional robots that are made of stiff and rigid components. These robots are ideal for interacting safely with humans and operating in highly dynamic environments. Soft robotics is a rapidly developing field exploiting biomimetic design principles, novel sensor and actuation concepts, and advanced manufacturing techniques. This work presents novel soft pneumatic actuators and sensors that are directly 3D printed in one manufacturing step without requiring postprocessing and support materials using low-cost and open-source fused deposition modeling (FDM) 3D printers that employ an off-the-shelf commercially available soft thermoplastic poly(urethane) (TPU). The performance of the soft actuators and sensors developed is optimized and predicted using finite element modeling (FEM) analytical models in some cases. A hyperelastic material model is developed for the TPU based on its experimental stress-strain data for use in FEM analysis. The novel soft vacuum bending (SOVA) and linear (LSOVA) actuators reported can be used in diverse robotic applications including locomotion robots, adaptive grippers, parallel manipulators, artificial muscles, modular robots, prosthetic hands, and prosthetic fingers. Also, the novel soft pneumatic sensing chambers (SPSC) developed can be used in diverse interactive human-machine interfaces including wearable gloves for virtual reality applications and controllers for soft adaptive grippers, soft push buttons for science, technology, engineering, and mathematics (STEM) education platforms, haptic feedback devices for rehabilitation, game controllers and throttle controllers for gaming and bending sensors for soft prosthetic hands. These SPSCs are directly 3D printed and embedded in a monolithic soft robotic finger as position and touch sensors for real-time position and force control. One of the aims of soft robotics is to design and fabricate robotic systems with a monolithic topology embedded with its actuators and sensors such that they can safely interact with their immediate physical environment. The results and conclusions of this thesis have significantly contributed to the realization of this aim

MIT Space Engineering Research Center

The Space Engineering Research Center (SERC) at MIT, started in Jul. 1988, has completed two years of research. The Center is approaching the operational phase of its first testbed, is midway through the construction of a second testbed, and is in the design phase of a third. We presently have seven participating faculty, four participating staff members, ten graduate students, and numerous undergraduates. This report reviews the testbed programs, individual graduate research, other SERC activities not funded by the Center, interaction with non-MIT organizations, and SERC milestones. Published papers made possible by SERC funding are included at the end of the report

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Real-time performance of mechatronic PZT module using active vibration feedback control

This paper proposes an innovative mechatronic piezo-actuated module to control vibrations in modern machine tools. Vibrations represent one of the main issues that seriously compromise the quality of the workpiece. The active vibration control (AVC) device is composed of a host part integrated with sensors and actuators synchronized by a regulator; it is able to make a self-assessment and adjust to alterations in the environment. In particular, an innovative smart actuator has been designed and developed to satisfy machining requirements during active vibration control. This study presents the mechatronic model based on the kinematic and dynamic analysis of the AVC device. To ensure a real time performance, a H2-LQG controller has been developed and validated by simulations involving a machine tool, PZT actuator and controller models. The Hardware in the Loop (HIL) architecture is adopted to control and attenuate the vibrations. A set of experimental tests has been performed to validate the AVC module on a commercial machine tool. The feasibility of the real time vibration damping is demonstrated and the simulation accuracy is evaluate

Robust state estimation for the control of flexible robotic manipulators

In this thesis, a novel robust estimation strategy for observing the system state variables of robotic manipulators with distributed flexibility is established. Motivation for the derived approach stems from the observation that lightweight, high speed, and large workspace robotic manipulators often suffer performance degradation because of inherent structural compliance. This flexibility often results in persistent residual vibration, which must be damped before useful work can resume. Inherent flexibility in robotic manipulators, then, increases cycle times and shortens the operational lives of the robots. Traditional compensation techniques, those which are commonly used for the control of rigid manipulators, can only approach a fraction of the open-loop system bandwidth without inducing significant excitation of the resonant dynamics. To improve the performance of these systems, the structural flexibility cannot simply be ignored, as it is when the links are significantly stiff and approximate rigid bodies. One thus needs a model to design a suitable compensator for the vibration, but any model developed to correct this problem will contain parametric error. And in the case of very lightly damped systems, like flexible robotic manipulators, this error can lead to instability of the control system for even small errors in system parameters. This work presents a systematic solution for the problem of robust state estimation for flexible manipulators in the presence of parametric modeling error. The solution includes: 1) a modeling strategy, 2) sensor selection and placement, and 3) a novel, multiple model estimator. Modeling of the FLASHMan flexible gantry manipulator is accomplished using a developed hybrid transfer matrix / assumed modes method (TMM/AMM) approach to determine an accurate low-order state space representation of the system dynamics. This model is utilized in a genetic algorithm optimization in determining the placement of MEMs accelerometers for robust estimation and observability of the system’s flexible state variables. The initial estimation method applied to the task of determining robust state estimates under conditions of parametric modeling error was of a sliding mode observer type. Evaluation of the method through analysis, simulations and experiments showed that the state estimates produced were inadequate. This led to the development of a novel, multiple model adaptive estimator. This estimator utilizes a bank of similarly designed sub-estimators and a selection algorithm to choose the true value from a given set of possible system parameter values as well as the correct state vector estimate. Simulation and experimental results are presented which demonstrate the applicability and effectiveness of the derived method for the task of state variable estimation for flexible robotic manipulators.Ph.D

Identification of natural frequency components of articulated flexible structures

M.S.Wayne J. Boo

Study of robotics systems applications to the space station program

Applications of robotics systems to potential uses of the Space Station as an assembly facility, and secondarily as a servicing facility, are considered. A typical robotics system mission is described along with the pertinent application guidelines and Space Station environmental assumptions utilized in developing the robotic task scenarios. A functional description of a supervised dual-robot space structure construction system is given, and four key areas of robotic technology are defined, described, and assessed. Alternate technologies for implementing the more routine space technology support subsystems that will be required to support the Space Station robotic systems in assembly and servicing tasks are briefly discussed. The environmental conditions impacting on the robotic configuration design and operation are reviewed

Selecting an Appropriate Curvature Sensor for Fluidic Soft Robot and Modeling Sensor Reading vs Pressure vs Position

This research focuses on the study of a curvature sensor for a fluidic soft robot. Soft robot is a complete new dimension to traditional rigid robot. A soft robot is made up of materials like Silicon, PDMS and elastomeric polymers. The actuation method can be hydraulic, pneumatic or electric. Depending on its construction, it undergoes elongation, bending, twisting, or all of the three on actuation. It brings with it some important features like compliance with the object of interaction and robustness, which is an inspiration acquired from animals and plants. This results into useful applications in fields of rehabilitation, gripping delicate objects in food industries and allowing safe interaction for humans. The soft robot has large DOF, which allows it to maneuver in a way, which is difficult for the traditional robot. However, this large DOF makes the modeling of the soft robot for determining the robot state difficult and challenging. Another approach towards determining the robot state is using sensors. In this thesis, a thorough study is done to find out an appropriate curvature sensor to be embedded into the soft robot. The data from curvature sensor, pressure sensor and the vision system are collected in experiments undertaken with obstacles in the soft robot path. The collected data is used via machine learning technique to obtain trained model that determines the robot state and obstacle location