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This thesis proposes the concept of soft robotic supernumerary limbs to assist the wearer in the execution of tasks, whether it be to share loads or replace an assistant. These controllable extra arms are made using soft robotics to reduce the weight and cost of the device, and are not

This thesis proposes the concept of soft robotic supernumerary limbs to assist the wearer in the execution of tasks, whether it be to share loads or replace an assistant. These controllable extra arms are made using soft robotics to reduce the weight and cost of the device, and are not limited in size and location to the user's arm as with exoskeletal devices. Soft robotics differ from traditional robotics in that they are made using soft materials such as silicone elastomers rather than hard materials such as metals or plastics. This thesis presents the design, fabrication, and testing of the arm, including the joints and the actuators to move them, as well as the design and fabrication of the human-body interface to unite man and machine. This prototype utilizes two types of pneumatically-driven actuators, pneumatic artificial muscles and fiber-reinforced actuators, to actuate the elbow and shoulder joints, respectively. The robotic limb is mounted at the waist on a backpack frame to avoid interfering with the wearer's biological arm. Through testing and evaluation, this prototype device proves the feasibility of soft supernumerary limbs, and opens up opportunities for further development into the field.
ContributorsOlson, Weston Roscoe (Author) / Polygerinos, Panagiotis (Thesis director) / Zhang, Wenlong (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
The mean age of the world’s population is rapidly increasing and with that growth in an aging population a large number of elderly people are in need of walking assistance. In addition, a number of medical conditions contribute to gait disorders that require gait rehabilitation. Wearable robotics can be used

The mean age of the world’s population is rapidly increasing and with that growth in an aging population a large number of elderly people are in need of walking assistance. In addition, a number of medical conditions contribute to gait disorders that require gait rehabilitation. Wearable robotics can be used to improve functional outcomes in the gait rehabilitation process. The ankle push-off phase of an individual’s gait is vital to their ability to walk and propel themselves forward. During the ankle push-off phase of walking, plantar flexors are required to providing a large amount of force to power the heel off the ground.

The purpose of this project is to improve upon the passive ankle foot orthosis originally designed in the ASU’s Robotics and Intelligent Systems Laboratory (RISE Lab). This device utilizes springs positioned parallel to the user’s Achilles tendon which store energy to be released during the push off phase of the user’s gait cycle. Goals of the project are to improve the speed and reliability of the ratchet and pawl mechanism, design the device to fit a wider range of shoe sizes, and reduce the overall mass and size of the device. The resulting system is semi-passive and only utilizes a single solenoid to unlock the ratcheting mechanism when the spring’s potential force is required. The device created also utilizes constant force springs rather than traditional linear springs which allows for a more predictable level of force. A healthy user tested the device on a treadmill and surface electromyography (sEMG) sensors were placed on the user’s plantar flexor muscles to monitor potential reductions in muscular activity resulting from the assistance provided by the AFO device. The data demonstrates the robotic shoe was able to assist during the heel-off stage and reduced activation in the plantar flexor muscles was evident from the EMG data collected. As this is an ongoing research project, this thesis will also recommend possible design upgrades and changes to be made to the device in the future. These upgrades include utilizing a carbon fiber or lightweight plastic frame such as many of the traditional ankle foot-orthosis sold today and introducing a system to regulate the amount of spring force applied as a function of the force required at specific times of the heel off gait phase.
ContributorsSchaller, Marcus Frank (Author) / Zhang, Wenlong (Thesis director) / Sugar, Thomas (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
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Description
This honors thesis project aims to design and develop the ideal design for a soft robotic grasper used in combination with a robotic supernumerary limb design for impaired individuals (i.e. a wearable robotic limb that branches out of the body), to help accomplish the tasks of daily living. Observations of

This honors thesis project aims to design and develop the ideal design for a soft robotic grasper used in combination with a robotic supernumerary limb design for impaired individuals (i.e. a wearable robotic limb that branches out of the body), to help accomplish the tasks of daily living. Observations of current grasper solutions for similar applications has led to a design that incorporates a soft, pneumatically controlled grasper which integrates with the existing limb. Computational models of the grasper design have been created which demonstrate the grasping capabilities of this proposal. Initial prototypes of this grasper approach have been fabricated for testing and analyses purposes to build a foundation for future implementation.
ContributorsThalman, Carly Megan (Author) / Polygerinos, Panagiotis (Thesis director) / Lande, Micah (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Description
Low back pain is a disorder which affects almost 80% of the American population at some time during their lifespan. Approximately, 90% of these episodes are resolved within six weeks to three months. As low back pain is usually a symptom of a medical condition; many cases cannot be given

Low back pain is a disorder which affects almost 80% of the American population at some time during their lifespan. Approximately, 90% of these episodes are resolved within six weeks to three months. As low back pain is usually a symptom of a medical condition; many cases cannot be given a definite diagnosis which renders the condition difficult to treat. The estimated annual cost for back pain treatment amounts to $50 billion, in the United States alone. Several devices have already been designed for low back pain assistance. However, in the majority, the main drawback appears to be the rigidity of the device, which limits flexibility and comfort. Soft pneumatic actuators have the potential to provide the appropriate applications for low back pain prior- and post-surgery rehabilitation purposes. In this work, the design and development of a soft robotic back orthotic device that has the capability to relieve back pain by assisting patients to fully achieve the upright position and stabilize the lumbosacral spine, is presented. Unlike conventional robotic assistive devices, this pneumatically actuated back orthosis provides dynamic support while being light weight, comfortable and cost affordable.
ContributorsGovin, Deven (Co-author) / Saenz, Luis (Co-author) / Polygerinos, Panagiotis (Thesis director) / Snyder, Laura (Committee member) / W. P. Carey School of Business (Contributor) / Harrington Bioengineering Program (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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Description
This work presents the design, modeling, analysis, and experimental characterization and testing of soft wearable robotics for lower limb rehabilitation for the ankle and hip. The Soft Robotic Ankle-Foot Orthosis (SR-AFO) is a wearable soft robot designed using multiple pneumatically-powered soft actuators to assist the ankle in multiple degrees-of-freedom during

This work presents the design, modeling, analysis, and experimental characterization and testing of soft wearable robotics for lower limb rehabilitation for the ankle and hip. The Soft Robotic Ankle-Foot Orthosis (SR-AFO) is a wearable soft robot designed using multiple pneumatically-powered soft actuators to assist the ankle in multiple degrees-of-freedom during standing and walking tasks. The flat fabric pneumatic artificial muscle (ff-PAM) contracts upon pressurization and assists ankle plantarflexion in the sagittal plane. The Multi-material Actuator for Variable Stiffness (MAVS) aids in supporting ankle inversion/eversion in the frontal plane. Analytical models of the ff-PAM and MAVS were created to understand how the changing of the design parameters affects tensile force generation and stiffness support, respectively. The models were validated by both finite element analysis and experimental characterization using a universal testing machine. A set of human experiments were performed with healthy participants: 1) to measure lateral ankle support during quiet standing, 2) to determine lateral ankle support during walking over compliant surfaces, and 3) to evaluate plantarflexion assistance at push-off during treadmill walking, and 4) determine if the SR-AFO could be used for gait entrainment. Group results revealed increased ankle stiffness during quiet standing with the MAVS active, reduced ankle deflection while walking over compliant surfaces with the MAVS active, and reduced muscle effort from the SOL and GAS during 40 - 60% of the gait cycle with the dual ff-PAM active. The SR-AFO shows promising results in providing lateral ankle support and plantarflexion assistance with healthy participants, and a drastically increased basin of entrainment, which suggests a capability to help restore the gait of impaired users in future trials. The ff-PAM actuators were used in an X-orientation to assist the hip in flexion and extension. The Soft Robotic Hip Exosuit (SR-HExo) was evaluated using the same set of actuators and trials with healthy participants showed reduction in muscle effort during hip flexion and extension to further enhance the study of soft fabric actuators on human gait assistance.
ContributorsThalman, Carly Megan (Author) / Lee, Hyunglae (Thesis advisor) / Artemiadis, Panagiotis (Thesis advisor) / Sugar, Thomas (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Physical and structural tree measurements are applied in forestry, precision agriculture and conservation for various reasons. Since measuring tree properties manually is tedious, measurements from only a small subset of trees present in a forest, agricultural land or survey site are often used. Utilizing robotics to autonomously estimate physical tree

Physical and structural tree measurements are applied in forestry, precision agriculture and conservation for various reasons. Since measuring tree properties manually is tedious, measurements from only a small subset of trees present in a forest, agricultural land or survey site are often used. Utilizing robotics to autonomously estimate physical tree dimensions would speed up the measurement or data collection process and allow for a much larger set of trees to be used in studies. In turn, this would allow studies to make more generalizable inferences about areas with trees. To this end, this thesis focuses on developing a system that generates a semantic representation of the topology of a tree in real-time. The first part describes a simulation environment and a real-world sensor suite to develop and test the tree mapping pipeline proposed in this thesis. The second part presents details of the proposed tree mapping pipeline. Stage one of the mapping pipeline utilizes a deep learning network to detect woody and cylindrical portions of a tree like trunks and branches based on popular semantic segmentation networks. Stage two of the pipeline proposes an algorithm to separate the detected portions of a tree into individual trunk and branch segments. The third stage implements an optimization algorithm to represent each segment parametrically as a cylinder. The fourth stage formulates a multi-sensor factor graph to incrementally integrate and optimize the semantic tree map while also fusing two forms of odometry. Finally, results from all the stages of the tree mapping pipeline using simulation and real-world data are presented. With these implementations, this thesis provides an end-to-end system to estimate tree topology through semantic representations for forestry and precision agriculture applications.
ContributorsVishwanatha, Rakshith (Author) / Das, Jnaneshwar (Thesis advisor) / Martin, Roberta (Committee member) / Throop, Heather (Committee member) / Zhang, Wenlong (Committee member) / Ehsani, Reza (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Soft robotics has garnered attention for its substantial prospective in various domains, such as manipulation and interactions with humans, by offering competitive advantages against rigid robotic systems, including inherent compliance and variable stiffness. Despite these benefits, their theoretically infinite degrees of freedom and prominent nonlinearities pose significant challenges in developing

Soft robotics has garnered attention for its substantial prospective in various domains, such as manipulation and interactions with humans, by offering competitive advantages against rigid robotic systems, including inherent compliance and variable stiffness. Despite these benefits, their theoretically infinite degrees of freedom and prominent nonlinearities pose significant challenges in developing dynamic models and guiding the robots along desired paths. Additionally, soft robots may exhibit rigid behaviors and potentially collide with their surroundings during path tracking tasks, particularly when possible contact points are unknown. In this dissertation, reduced-order models are used to describe the behaviors of three different soft robot designs, including both linear parameter varying (LPV) and augmented rigid robot (ARR) models. While the reduced-order model captures the majority of the soft robot's dynamics, modeling uncertainties notably remain. Non-repeated modeling uncertainties are addressed by categorizing them as a lumped disturbance, employing two methodologies, $H_\infty$ method and nonlinear disturbance observer (NDOB) based sliding mode control, for its rejection. For repeated disturbances, an iterative learning control (ILC) with a P-type learning function is implemented to enhance trajectory tracking efficacy. Furthermore,for non-repeated disturbances, the NDOB facilitates the contact estimation, and its results are jointly used with a switching algorithm to modify the robot trajectories. The stability proof of all controllers and corresponding simulation and experimental results are provided. For a path tracking task of a soft robot with multi-segments, a robust control strategy that combines a LPV model with an innovative improved nonlinear disturbance observer-based adaptive sliding mode control (INASMC). The control framework employs a first-order LPV model for dynamic representation, leverages an improved disturbance observer for accurate disturbance forecasting, and utilizes adaptive sliding mode control to effectively counteract uncertainties. The tracking error under the proposed controller is proven to be asymptotically stable, and the controller's effectiveness is is validated with simulation and experimental results. Ultimately, this research mitigates the inherent uncertainty in soft robot modeling, thereby enhancing their functionality in contact-intensive tasks.
ContributorsQIAO, ZHI (Author) / Zhang, Wenlong (Thesis advisor) / Marvi, Hamidreza (Committee member) / Lee, Hyunglae (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
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Description
Navigation and mapping in GPS-denied environments, such as coal mines ordilapidated buildings filled with smog or particulate matter, pose a significant challenge due to the limitations of conventional LiDAR or vision systems. Therefore there exists a need for a navigation algorithm and mapping strategy which do not use vision systems but are still

Navigation and mapping in GPS-denied environments, such as coal mines ordilapidated buildings filled with smog or particulate matter, pose a significant challenge due to the limitations of conventional LiDAR or vision systems. Therefore there exists a need for a navigation algorithm and mapping strategy which do not use vision systems but are still able to explore and map the environment. The map can further be used by first responders and cave explorers to access the environments. This thesis presents the design of a collision-resilient Unmanned Aerial Vehicle (UAV), XPLORER that utilizes a novel navigation algorithm for exploration and simultaneous mapping of the environment. The real-time navigation algorithm uses the onboard Inertial Measurement Units (IMUs) and arm bending angles for contact estimation and employs an Explore and Exploit strategy. Additionally, the quadrotor design is discussed, highlighting its improved stability over the previous design. The generated map of the environment can be utilized by autonomous vehicles to navigate the environment. The navigation algorithm is validated in multiple real-time experiments in different scenarios consisting of concave and convex corners and circular objects. Furthermore, the developed mapping framework can serve as an auxiliary input for map generation along with conventional LiDAR or vision-based mapping algorithms. Both the navigation and mapping algorithms are designed to be modular, making them compatible with conventional UAVs also. This research contributes to the development of navigation and mapping techniques for GPS-denied environments, enabling safer and more efficient exploration of challenging territories.
ContributorsPandian Saravanakumaran, Aravind Adhith (Author) / Zhang, Wenlong (Thesis advisor) / Das, Jnaneshwar (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2023
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Description
While wearable soft robots have successfully addressed many inherent design limitations faced by wearable rigid robots, they possess a unique set of challenges due to their soft and compliant nature. Some of these challenges are present in the sensing, modeling, control and evaluation of wearable soft robots. Machine learning algorithms

While wearable soft robots have successfully addressed many inherent design limitations faced by wearable rigid robots, they possess a unique set of challenges due to their soft and compliant nature. Some of these challenges are present in the sensing, modeling, control and evaluation of wearable soft robots. Machine learning algorithms have shown promising results for sensor fusion with wearable robots, however, they require extensive data to train models for different users and experimental conditions. Modeling soft sensors and actuators require characterizing non-linearity and hysteresis, which complicates deriving an analytical model. Experimental characterization can capture the characteristics of non-linearity and hysteresis but requires developing a synthesized model for real-time control. Controllers for wearable soft robots must be robust to compensate for unknown disturbances that arise from the soft robot and its interaction with the user. Since developing dynamic models for soft robots is complex, inaccuracies that arise from the unmodeled dynamics lead to significant disturbances that the controller needs to compensate for. In addition, obtaining a physical model of the human-robot interaction is complex due to unknown human dynamics during walking. Finally, the performance of soft robots for wearable applications requires extensive experimental evaluation to analyze the benefits for the user. To address these challenges, this dissertation focuses on the sensing, modeling, control and evaluation of soft robots for wearable applications. A model-based sensor fusion algorithm is proposed to improve the estimation of human joint kinematics, with a soft flexible robot that requires compact and lightweight sensors. To overcome limitations with rigid sensors, an inflatable soft haptic sensor is developed to enable gait sensing and haptic feedback. Through experimental characterization, a mathematical model is derived to quantify the user's ground reaction forces and the delivered haptic force. Lastly, the performance of a wearable soft exosuit in assisting human users during lifting tasks is evaluated, and the benefits obtained from the soft robot assistance are analyzed.
ContributorsQuiñones Yumbla, Emiliano (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
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Description
Robotic technology can be broadly categorized into two main approaches based on the compliance of the robot's materials and structure: hard and soft. Hard, traditional robots, with mechanisms to transmit forces, provide high degrees of freedom (DoFs) and precise manipulation, making them commonly used in industry and academic research. The

Robotic technology can be broadly categorized into two main approaches based on the compliance of the robot's materials and structure: hard and soft. Hard, traditional robots, with mechanisms to transmit forces, provide high degrees of freedom (DoFs) and precise manipulation, making them commonly used in industry and academic research. The field of soft robotics, on the other hand, is a new trend from the past three decades of robotics that uses soft materials such as silicone or textiles as the body or material base instead of the rigid bodies used in traditional robots. Soft robots are typically pre-programmed with specific geometries, and perform well at tasks such as human-robot interaction, locomotion in complex environments, and adaptive reconfiguration to the environment, which reduces the cost of future programming and control. However, full soft robotic systems are often less mobile due to their actuation --pneumatics, high-voltage electricity or magnetics -- even if the robot itself is at a millimeter or centimeter scale. Rigid or hard robots, on the other hand, can often carry the weight of their own power, but with a higher burden of cost for control and sensing. A middle ground is thus sought, to combine soft robotics technologies with rigid robots, by implementing mechanism design principles with soft robots to embed functionalities or utilize soft robots as the actuator on a rigid robotic system towards an affordable robotic system design. This dissertation showcases five examples of this design principle with two main research branches: locomotion and wearable robotics. In the first research case, an example of how a miniature swimming robot can navigate through a granular environment using compliant plates is presented, compared to other robots that change their shape or use high DoF mechanisms. In the second pipeline, mechanism design is implemented using soft robotics concepts in a wearable robot. An origami-inspired, soft "exo-shell", that can change its stiffness on demand, is introduced. As a follow-up to this wearable origami-inspired robot, a geometry-based, ``near" self-locking modular brake is then presented. Finally, upon combining the origami-inspired wearable robot and brake design, a concept of a modular wearable robot is showcased for the purpose of answering a series of biomechanics questions.
ContributorsLi, Dongting (Author) / Aukes, Daniel M (Thesis advisor) / Sugar, Thomas G (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2023