This paper presents further developments, characterization and initial evaluation of a recently developed assistive soft robotic glove for individuals with hand pathologies. The glove technology utilizes a combination of elastomeric and inextensible materials to create soft actuators that conform to the user's hand and can generate sufficient hand closing force to assist with activities of daily living. User intent (i.e. desire to close or open hand) is detected by monitoring gross muscle activation signals with surface electromyography electrodes mounted on the user's forearm. In particular, we present an open-loop sEMG logic that distinguishes muscle contractions and feeds the information to a low-level fluidic pressure controller that regulates pressure in pre-selected groups of the glove's actuators. Experiments are conducted to determine the level of assistance provided by the glove by monitoring muscle effort and mapping the pressure distribution during a simple grasping task when the glove is worn. Lastly, quantitative and qualitative results are presented using the sEMG-controlled glove on a healthy participant and on a patient with muscular dystrophy.
In this work we investigate the influence of fiber angle on the deformation of fiber-reinforced soft fluidic actuators and examine the manner in which these actuators extend axially, expand radially and twist about their axis as a function of input pressure. We study the quantitative relationship between fiber angle and actuator deformation by performing finite element simulations for actuators with a range of different fiber angles, and we verify the simulation results by experimentally characterizing the actuators. By combining actuator segments in series, we can achieve combinations of motions tailored to specific tasks. We demonstrate this by using the results of simulations of separate actuators to design a segmented wormlike soft robot capable of propelling itself through a tube and performing an orientation-specific peg insertion task at the end of the tube. Understanding the relationship between fiber angle and pressurization response of these soft fluidic actuators enables rapid exploration of the design space, opening the door to the iteration of exciting soft robot concepts such as flexible and compliant endoscopes, pipe inspection devices, and assembly line robots.
This paper presents advancements in the design of a portable, soft robotic glove for individuals with functional grasp pathologies. The robotic glove leverages soft material actuator technology to safely distribute forces along the length of the finger and provide active flexion and passive extension. These actuators consist of molded elastomeric bladders with anisotropic fiber reinforcements that produce specific bending, twisting, and extending trajectories upon fluid pressurization. In particular, we present a method for customizing a soft actuator to a wearer's biomechanics and demonstrate in a motion capture system that the ranges of motion (ROM) of the two are nearly equivalent. The active ROM of the glove is further evaluated using the Kapandji test. Lastly, in a case study, we present preliminary results of a patient with very weak hand strength performing a timed Box-and-Block test with and without the soft robotic glove.
We report a new method for fabricating textile integrable capacitive soft strain sensors based on multicore–shell fiber printing. The fiber sensors consist of four concentric, alternating layers of conductor and dielectric, respectively. These wearable sensors provide accurate and hysteresis-free strain measurements under both static and dynamic conditions.
This paper presents a portable, assistive, soft robotic glove designed to augment hand rehabilitation for individuals with functional grasp pathologies. The robotic glove utilizes soft actuators consisting of molded elastomeric chambers with fiber reinforcements that induce specific bending, twisting and extending trajectories under fluid pressurization. These soft actuators were mechanically programmed to match and support the range of motion of individual fingers. They demonstrated the ability to generate significant force when pressurized and exhibited low impedance when un-actuated. To operate the soft robotic glove, a control hardware system was designed and included fluidic pressure sensors in line with the hydraulic actuators and a closed-loop controller to regulate the pressure. Demonstrations with the complete system were performed to evaluate the ability of the soft robotic glove to carry out gross and precise functional grasping. Compared to existing devices, the soft robotic glove has the potential to increase user freedom and independence through its portable waist belt pack and open palm design.
Soft fluidic actuators consisting of elastomeric matrices with embedded flexible materials are of particular interest to the robotics community because they are affordable and can be easily customized to a given application. However, the significant potential of such actuators is currently limited as their design has typically been based on intuition. In this paper, the principle of operation of these actuators is comprehensively analyzed and described through experimentally validated quasi-static analytical and finite-element method models for bending in free space and force generation when in contact with an object. This study provides a set of systematic design rules to help the robotics community create soft actuators by understanding how these vary their outputs as a function of input pressure for a number of geometrical parameters. Additionally, the proposed analytical model is implemented in a controller demonstrating its ability to convert pressure information to bending angle in real time. Such an understanding of soft multimaterial actuators will allow future design concepts to be rapidly iterated and their performance predicted, thus enabling new and innovative applications that produce more complex motions to be explored.
Soft sensors comprising a flexible matrix with embedded circuit elements can undergo large deformations while maintaining adequate performance. These devices have attracted considerable interest for their ability to be integrated with the human body and have enabled the design of skin-like health monitoring devices, sensing suits, and soft active orthotics. Numerical tools are needed to facilitate the development and optimization of these systems. In this letter, we introduce a 3D finite element-based numerical tool to simultaneously characterize the mechanical and electrical response of fluid-embedded soft sensors of arbitrary shape, subjected to any loading. First, we quantitatively verified the numerical approach by comparing simulation and experimental results of a dog-bone shaped sensor subjected to uniaxial stretch and local compression. Then, we demonstrate the power of the numerical tool by examining a number of different loading conditions. We expect this work will open the door for further design of complex and optimal soft sensors.
This article describes the development of the Soft Robotics Toolkit, a set of open access resources to support the design, fabrication, modeling, characterization, and control of soft robotic devices. The ultimate aim of the toolkit is to support researchers in building upon each other's work, and thereby advance the field of soft robotics. An additional aim is to support educators and encourage students to pursue careers in engineering and science by making the resources as accessible as possible. The toolkit was developed and refined through a series of pilot studies and user tests. Specifically, the resources were used by students in a project-based medical device design course; volunteers from a variety of backgrounds tested the toolkit and provided feedback, and soft robotics researchers used the collection of resources and contributed to its development. Throughout all user studies, qualitative data were collected and used to guide improvements to the toolkit. This process of testing and refinement has resulted in a website containing design documentation describing general hardware control platforms and specific soft robotic component designs. The online documentation includes downloadable computer-aided design (CAD) files, detailed multimedia protocols for the fabrication of soft devices, tutorials and scripts for modeling and analyzing soft actuators and sensors, and source code for controlling soft devices. Successive iterations of qualitative data gathering and redesign have confirmed that the toolkit documentation is sufficiently detailed to be useful for researchers from a wide range of backgrounds. To date, the focus of the toolkit has primarily been fluid-actuated robotic systems, but the plan is to expand it to support a wider range of soft robotic-enabling technologies. The toolkit is intended as a community resource, and all researchers working in this field are invited to guide its future development by providing feedback and contributing new content.
Wearable robots based on soft materials will augment mobility and performance of the host without restricting natural kinematics. Such wearable robots will need soft sensors to monitor the movement of the wearer and robot outside the lab. Until now wearable soft sensors have not demonstrated significant mechanical robustness nor been systematically characterized for human motion studies of walking and running. Here, we present the design and systematic characterization of a soft sensing suit for monitoring hip, knee, and ankle sagittal plane joint angles. We used hyper-elastic strain sensors based on microchannels of liquid metal embedded within elastomer, but refined their design with the use of discretized stiffness gradients to improve mechanical durability. We found that these robust sensors could stretch up to 396% of their original lengths, would restrict the wearer by less than 0.17% of any given joint’s torque, had gauge factor sensitivities of greater than 2.2, and exhibited less than 2% change in electromechanical specifications through 1500 cycles of loading–unloading. We also evaluated the accuracy and variability of the soft sensing suit by comparing it with joint angle data obtained through optical motion capture. The sensing suit had root mean square (RMS) errors of less than 5° for a walking speed of 0.89 m/s and reached a maximum RMS error of 15° for a running speed of 2.7 m/s. Despite the deviation of absolute measure, the relative repeatability of the sensing suit’s joint angle measurements were statistically equivalent to that of optical motion capture at all speeds. We anticipate that wearable soft sensing will also have applications beyond wearable robotics, such as in medical diagnostics and in human–computer interaction.