Overtwisting and Coiling Highly Enhances Strain Generation of Twisted String Actuators

Twisted string actuators (TSAs) have exhibited great promise in robotic applications by generating high translational force with low input torque. To further facilitate their robotic applications, it is strongly desirable but challenging to enhance their consistent strain generation while maintaining compliance. Existing studies predominantly considered overtwisting and coiling after the regular twisting stage to be undesirable non-uniform and unpredictable knots, entanglements, and coils formed to create an unstable and failure-prone structure. Overtwisting would work well for TSAs when uniform coils can be consistently formed. In this study, we realize uniform and consistent coil formation in overtwisted TSAs, which greatly increases their strain. Furthermore, we investigate methods for enabling uniform coil formation upon overtwisting the strings in a TSA and present a procedure to systematically "train" the strings. To the authors' best knowledge, this is the first study to experimentally investigate overtwisting for TSAs with different stiffnesses and realize consistent uniform coil formation. Ultra-high molecular-weight polyethylene (UHMWPE) strings form the stiff TSAs whereas compliant TSAs are realized with stretchable and conductive supercoiled polymer (SCP) strings. The strain, force, velocity, and torque of each overtwisted TSA was studied. Overtwisting and coiling resulted in approximately 70% strain in stiff TSAs and approximately 60% strain in compliant TSAs. This is more than twice the strain achieved through regular twisting. Lastly, the overtwisted TSA was successfully demonstrated in a robotic bicep

Towards Human-like Walking with Biomechanical and Neuromuscular Control Features: Personalized Attachment Point Optimization Method of Cable-Driven Exoskeleton

The cable-driven exoskeleton can avoid joint misalignment, and is substantial alterations in the pattern of muscle synergy coordination, which arouse more attention in recent years to facilitate exercise for older adults and improve their overall quality of life. This study leverages principles from neuroscience and biomechanical analysis to select attachment points for cable-driven soft exoskeletons. By extracting key features of human movement, the objective is to develop a subject-specific design methodology that provides precise and personalized support in the attachment points optimization of cable-driven exoskeleton to achieve natural gait, energy efficiency, and muscle coordination controllable in the domain of human mobility and rehabilitation. To achieve this, the study first analyzes human walking experimental data and extracts biomechanical features. These features are then used to generate trajectories, allowing better natural movement under complete cable-driven exoskeleton control. Next, a genetic algorithm-based method is employed to minimize energy consumption and optimize the attachment points of the cable-driven system. This process identifies connections that are better suited for the human model, leading to improved efficiency and natural movement. By comparing the calculated elderly human model driven by exoskeleton with experimental subject in terms of joint angles, joint torques and muscle forces, the human model can successfully replicate subject movement and the cable output forces can mimic human muscle coordination. The optimized cable attachment points facilitate more natural and efficient collaboration between humans and the exoskeleton, making significant contributions to the field of assisting the elderly in rehabilitation

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

Towards Safe Autonomy in Assistive Robots

Robots have the potential to support older adults and persons with disabilities on a direct and personal level. For example, a wearable robot may help a person stand up from a chair, or a robotic manipulator may aid a person with meal preparation and housework. Assistive robots can autonomously make decisions about how best to support a person. However, this autonomy is potentially dangerous; robots can cause collisions or falls which may lead to serious injury. Therefore, guaranteeing that assistive robots operate safely is imperative. This dissertation advances safe autonomy in assistive robots by developing a suite of tools for the tasks of perception, monitoring, manipulation and all prevention. Each tool provides a theoretical guarantee of its correct performance, adding a necessary layer of trust and protection when deploying assistive robots. The topic of interaction, or how a human responds to the decisions made by assistive robots, is left for future work. Perception: Assistive robots must accurately perceive the 3D position of a person's body to avoid collisions and build predictive models of how a person moves. This dissertation formulates the problem of 3D pose estimation from multi-view 2D pose estimates as a sum-of-squares optimization problem. Sparsity is leveraged to efficiently solve the problem, which includes explicit constraints on the link lengths connecting any two joints. The method certifies the global optimality of its solutions over 99 percent of the time, and matches or exceeds state-of-the-art accuracy while requiring less computation time and no 3D training data. Monitoring: Assistive robots may mitigate fall risk by monitoring changes to a person’s stability over time and predicting instabilities in real time. This dissertation presents Stability Basins which characterize stability during human motion, with a focus on sit-to-stand. An 11-person experiment was conducted in which subjects were pulled by motor-driven cables as they stood from a chair. Stability Basins correctly predicted instability (stepping or sitting) versus task success with over 90 percent accuracy across three distinct sit-to-stand strategies. Manipulation: Robotic manipulators can support many common activities like feeding, dressing, and cleaning. This dissertation details ARMTD (Autonomous Reachability-based Manipulator Trajectory Design) for receding-horizon planning of collision-free manipulator trajectories. ARMTD composes reachable sets of the manipulator through workspace from low dimensional trajectories of each joint. ARMTD creates strict collision-avoidance constraints from these sets, which are enforced within an online trajectory optimization. The method is demonstrated for real-time planning in simulation and on hardware on a Fetch Mobile Manipulator robot, where it never causes a collision. Fall Prevention: Wearable robots may prevent falls by quickly reacting when a user trips or slips. This dissertation presents TRIP-RTD (Trip Recovery in Prostheses via Reachability-based Trajectory Design), which extends the ARMTD framework to robotic prosthetic legs. TRIP-RTD uses predictions of a person’s response to a trip to plan recovery trajectories of a prosthetic leg. TRIP-RTD creates constraints for an online trajectory optimization which ensure the prosthetic foot is placed correctly across a range of plausible human responses. The approach is demonstrated in simulation using data of non-amputee subjects being tripped.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169822/1/pdholmes_1.pd