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Description
The world population is aging. Age-related disorders such as stroke and spinal cord injury are increasing rapidly, and such patients often suffer from mobility impairment. Wearable robotic exoskeletons are developed that serve as rehabilitation devices for these patients. In this thesis, a knee exoskeleton design with higher torque output compared to the first version, is designed and fabricated.
A series elastic actuator is one of the many actuation mechanisms employed in exoskeletons. In this mechanism a torsion spring is used between the actuator and human joint. It serves as torque sensor and energy buffer, making it compact and
safe.
A version of knee exoskeleton was developed using the SEA mechanism. It uses worm gear and spur gear combination to amplify the assistive torque generated from the DC motor. It weighs 1.57 kg and provides a maximum assistive torque of 11.26 N·m. It can be used as a rehabilitation device for patients affected with knee joint impairment.
A new version of exoskeleton design is proposed as an improvement over the first version. It consists of components such as brushless DC motor and planetary gear that are selected to meet the design requirements and biomechanical considerations. All the other components such as bevel gear and torsion spring are selected to be compatible with the exoskeleton. The frame of the exoskeleton is modeled in SolidWorks to be modular and easy to assemble. It is fabricated using sheet metal aluminum. It is designed to provide a maximum assistive torque of 23 N·m, two times over the present exoskeleton. A simple brace is 3D printed, making it easy to wear and use. It weighs 2.4 kg.
The exoskeleton is equipped with encoders that are used to measure spring deflection and motor angle. They act as sensors for precise control of the exoskeleton.
An impedance-based control is implemented using NI MyRIO, a FPGA based controller. The motor is controlled using a motor driver and powered using an external battery source. The bench tests and walking tests are presented. The new version of exoskeleton is compared with first version and state of the art devices.
A series elastic actuator is one of the many actuation mechanisms employed in exoskeletons. In this mechanism a torsion spring is used between the actuator and human joint. It serves as torque sensor and energy buffer, making it compact and
safe.
A version of knee exoskeleton was developed using the SEA mechanism. It uses worm gear and spur gear combination to amplify the assistive torque generated from the DC motor. It weighs 1.57 kg and provides a maximum assistive torque of 11.26 N·m. It can be used as a rehabilitation device for patients affected with knee joint impairment.
A new version of exoskeleton design is proposed as an improvement over the first version. It consists of components such as brushless DC motor and planetary gear that are selected to meet the design requirements and biomechanical considerations. All the other components such as bevel gear and torsion spring are selected to be compatible with the exoskeleton. The frame of the exoskeleton is modeled in SolidWorks to be modular and easy to assemble. It is fabricated using sheet metal aluminum. It is designed to provide a maximum assistive torque of 23 N·m, two times over the present exoskeleton. A simple brace is 3D printed, making it easy to wear and use. It weighs 2.4 kg.
The exoskeleton is equipped with encoders that are used to measure spring deflection and motor angle. They act as sensors for precise control of the exoskeleton.
An impedance-based control is implemented using NI MyRIO, a FPGA based controller. The motor is controlled using a motor driver and powered using an external battery source. The bench tests and walking tests are presented. The new version of exoskeleton is compared with first version and state of the art devices.
ContributorsJhawar, Vaibhav (Author) / Zhang, Wenlong (Thesis advisor) / Sugar, Thomas G. (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2018
Description
Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity.
This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU.
This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.
This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU.
This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.
ContributorsChinimilli, Prudhvi Tej (Author) / Redkar, Sangram (Thesis advisor) / Zhang, Wenlong (Thesis advisor) / Sugar, Thomas G. (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2018
Description
Advanced material systems refer to materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to their superior properties over conventional materials. This dissertation is motivated by the grand challenge in accelerating the design of advanced material systems through systematic optimization with respect to material microstructures or processing settings. While optimization techniques have mature applications to a large range of engineering systems, their application to material design meets unique challenges due to the high dimensionality of microstructures and the high costs in computing process-structure-property (PSP) mappings. The key to addressing these challenges is the learning of material representations and predictive PSP mappings while managing a small data acquisition budget. This dissertation thus focuses on developing learning mechanisms that leverage context-specific meta-data and physics-based theories. Two research tasks will be conducted: In the first, we develop a statistical generative model that learns to characterize high-dimensional microstructure samples using low-dimensional features. We improve the data efficiency of a variational autoencoder by introducing a morphology loss to the training. We demonstrate that the resultant microstructure generator is morphology-aware when trained on a small set of material samples, and can effectively constrain the microstructure space during material design. In the second task, we investigate an active learning mechanism where new samples are acquired based on their violation to a theory-driven constraint on the physics-based model. We demonstrate using a topology optimization case that while data acquisition through the physics-based model is often expensive (e.g., obtaining microstructures through simulation or optimization processes), the evaluation of the constraint can be far more affordable (e.g., checking whether a solution is optimal or equilibrium). We show that this theory-driven learning algorithm can lead to much improved learning efficiency and generalization performance when such constraints can be derived. The outcomes of this research is a better understanding of how physics knowledge about material systems can be integrated into machine learning frameworks, in order to achieve more cost-effective and reliable learning of material representations and predictive models, which are essential to accelerate computational material design.
ContributorsCang, Ruijin (Author) / Ren, Yi (Thesis advisor) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018
Description
Lower-limb wearable assistive robots could alter the users gait kinematics by inputting external power, which can be interpreted as mechanical perturbation to subject normal gait. The change in kinematics may affect the dynamic stability. This work attempts to understand the effects of different physical assistance from these robots on the gait dynamic stability.
A knee exoskeleton and ankle assistive device (Robotic Shoe) are developed and used to provide walking assistance. The knee exoskeleton provides personalized knee joint assistive torque during the stance phase. The robotic shoe is a light-weighted mechanism that can store the potential energy at heel strike and release it by using an active locking mechanism at the terminal stance phase to provide push-up ankle torque and assist the toe-off. Lower-limb Kinematic time series data are collected for subjects wearing these devices in the passive and active mode. The changes of kinematics with and without these devices on lower-limb motion are first studied. Orbital stability, as one of the commonly used measure to quantify gait stability through calculating Floquet Multipliers (FM), is employed to asses the effects of these wearable devices on gait stability. It is shown that wearing the passive knee exoskeleton causes less orbitally stable gait for users, while the knee joint active assistance improves the orbital stability compared to passive mode. The robotic shoe only affects the targeted joint (right ankle) kinematics, and wearing the passive mechanism significantly increases the ankle joint FM values, which indicates less walking orbital stability. More analysis is done on a mechanically perturbed walking public data set, to show that orbital stability can quantify the effects of external mechanical perturbation on gait dynamic stability. This method can further be used as a control design tool to ensure gait stability for users of lower-limb assistive devices.
A knee exoskeleton and ankle assistive device (Robotic Shoe) are developed and used to provide walking assistance. The knee exoskeleton provides personalized knee joint assistive torque during the stance phase. The robotic shoe is a light-weighted mechanism that can store the potential energy at heel strike and release it by using an active locking mechanism at the terminal stance phase to provide push-up ankle torque and assist the toe-off. Lower-limb Kinematic time series data are collected for subjects wearing these devices in the passive and active mode. The changes of kinematics with and without these devices on lower-limb motion are first studied. Orbital stability, as one of the commonly used measure to quantify gait stability through calculating Floquet Multipliers (FM), is employed to asses the effects of these wearable devices on gait stability. It is shown that wearing the passive knee exoskeleton causes less orbitally stable gait for users, while the knee joint active assistance improves the orbital stability compared to passive mode. The robotic shoe only affects the targeted joint (right ankle) kinematics, and wearing the passive mechanism significantly increases the ankle joint FM values, which indicates less walking orbital stability. More analysis is done on a mechanically perturbed walking public data set, to show that orbital stability can quantify the effects of external mechanical perturbation on gait dynamic stability. This method can further be used as a control design tool to ensure gait stability for users of lower-limb assistive devices.
ContributorsRezayat Sorkhabadi, Seyed Mostafa (Author) / Zhang, Wenlong (Thesis advisor) / Lee, Hyunglae (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2018
Description
Prosthetic sockets are a static interface for dynamic residual limbs. As the user's activity level increases, the volume of the residual limb decreases by up to 11% and increases by as much as 7% after activity. Currently, volume fluctuation is addressed by adding/removing prosthetic socks to change the profile of the residual limb. However, this is time consuming. These painful/functional issues demand a prosthetic socket with an adjustable interface that can adapt to the user's needs. This thesis presents a prototype design for a dynamic soft robotic interface which addresses this need. The actuators are adjustable depending on the user's activity level, and their structure provides targeted compression to the soft tissue which helps to limit movement of the bone relative to the socket. The engineering process was used to create this design by defining system level requirements, exploring the design space, selecting a design, and then using testing/analysis to optimize that design. The final design for the soft robotic interface meets the applicable requirements, while other requirements for the electronics/controls will be completed as future work. Testing of the prototype demonstrated promising potential for the design with further refinement. Work on this project should be continued in future research/thesis projects in order to create a viable consumer product which can improve lower limb amputee's quality of life.
ContributorsHolmes, Breanna Swift (Author) / Zhang, Wenlong (Thesis director) / Polygerinos, Panagiotis (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The thesis covers the development and modeling of the supervisory hybrid controller using two different methods to achieve real-world optimization and power split of a parallel hybrid vehicle with a fixed shaft connecting the Internal Combustion Engine (ICE) and Electric Motor (EM). The first strategy uses a rule based controller to determine modes the vehicle should operate in. This approach is well suited for real-world applications. The second approach uses Sequential Quadratic Programming (SQP) approach in conjunction with an Equivalent Consumption Minimization Strategy (ECMS) strategy to keep the vehicle in the most efficient operating regions. This latter method is able to operate the vehicle in various drive cycles while maintaining the SOC with-in allowed charge sustaining (CS) limits. Further, the overall efficiency of the vehicle for all drive cycles is increased. The limitation here is the that process is computationally expensive; however, with advent of the low cost high performance hardware this method can be used for the hybrid vehicle control.
ContributorsMaady, Rashad Kamal (Author) / Redkar, Sangram (Thesis advisor) / Mayyas, Abdel R (Thesis advisor) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2017
Description
Stiffness and flexibility are essential in many fields, including robotics, aerospace, bioengineering, etc. In recent years, origami-based mechanical metamaterials were designed for better mechanical properties including tunable stiffness and tunable collapsibility. However, in existing studies, the tunable stiffness is only with limited range and limited controllability. To overcome these challenges, two objectives were proposed and achieved in this dissertation: first, to design mechanical metamaterials with metamaterials with selective stiffness and collapsibility; second, to design mechanical metamaterials with in-situ tunable stiffness among positive, zero, and negative.In the first part, triangulated cylinder origami was employed to build deployable mechanical metamaterials through folding and unfolding along the crease lines. These deployable structures are flexible in the deploy direction so that it can be easily collapsed along the same way as it was deployed. An origami-inspired mechanical metamaterial was designed for on-demand deployability and selective collapsibility: autonomous deployability from the collapsed state and selective collapsibility along two different paths, with low stiffness for one path and substantially high stiffness for another path. The created mechanical metamaterial yields unprecedented load bearing capability in the deploy direction while possessing great deployability and collapsibility. The principle in this prospectus can be utilized to design and create versatile origami-inspired mechanical metamaterials that can find many applications.
In the second part, curved origami patterns were designed to accomplish in situ stiffness manipulation covering positive, zero, and negative stiffness by activating predefined creases on one curved origami pattern. This elegant design enables in situ stiffness switching in lightweight and space-saving applications, as demonstrated through three robotic-related components. Under a uniform load, the curved origami can provide universal gripping, controlled force transmissibility, and multistage stiffness response. This work illustrates an unexplored and unprecedented capability of curved origami, which opens new applications in robotics for this particular family of origami patterns.
ContributorsZhai, Zirui (Author) / Nian, Qiong (Thesis advisor) / Zhuang, Houlong (Committee member) / Huang, Huei-Ping (Committee member) / Zhang, Wenlong (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2021
Description
A Compact Linear Fresnel Reflector (CLFR) is a simple, cost-effective, and scalable option for generating solar power by concentrating the sun rays. To make a most feasible application, design parameters of the CLFR, such as solar concentrator design parameters, receiver design parameters, heat transfer, power block parameters, etc., should be optimized to achieve optimum efficiency. Many researchers have carried out modeling and optimization of CLFR with various numerical or analytical methods. However, often computational time and cost are significant in these existing approaches. This research attempts to address this issue by proposing a novel computational approach with the help of increased computational efficiency and machine learning. The approach consists of two parts: the algorithm and the machine learning model. The algorithm has been created to fulfill the requirement of the Monte Carlo Ray tracing method for CLFR collector simulation, which is a simplified version of the conventional ray-tracing method. For various configurations of the CLFR system, optical losses and optical efficiency are calculated by employing these design parameters, such as the number of mirrors, mirror length, mirror width, space between adjacent mirrors, and orientation angle of the CLFR system. Further, to reduce the computational time, a machine learning method is used to predict the optical efficiency for the various configurations of the CLFR system. This entire method is validated using an existing approach (SolTrace) for the optical losses and optical efficiency of a CLFR system. It is observed that the program requires 6.63 CPU-hours of computational time are required by the program to calculate efficiency. In contrast, the novel machine learning approach took only seconds to predict the optical efficiency with great accuracy. Therefore, this method can be used to optimize a CLFR system based on the location and land configuration with reduced computational time. This will be beneficial for CLFR to be a potential candidate for concentrating solar power option.
ContributorsLunagariya, Shyam (Author) / Phelan, Patrick (Thesis advisor) / Kwon, Beomjin (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Laser powder bed fusion (LPBF) additive manufacturing (AM) has received widespread attention due to its ability to produce parts with complicated design and better surface finish compared to other additive techniques. LPBF uses a laser heat source to melt layers of powder particles and manufactures a part based on the CAD design. This process can benefit significantly through computational modeling. The objective of this thesis was to understand the thermal transport, and fluid flow phenomena of the process, and to optimize the main process parameters such as laser power and scan speed through a combination of computational, experimental, and statistical analysis. A multi-physics model was built using to model temperature profile, bead geometry and elemental evaporation in powder bed process using a non-gaussian interaction between laser heat source and metallic powder. Owing to the scarcity of thermo-physical properties of metallic powders in literature, thermal conductivity, diffusivity, and heat capacity was experimentally tested up to a temperature of 1400 degrees C. The values were used in the computational model, which improved the results significantly. The computational work was also used to assess the impact of fluid flow around melt pool. Dimensional analysis was conducted to determine heat transport mode at various laser power/scan speed combinations. Convective heat flow proved to be the dominant form of heat transfer at higher energy input due to violent flow of the fluid around the molten region, which can also create keyhole effect. The last part of the thesis focused on gaining useful information about several features of the bead area such as contact angle, porosity, voids and melt pool that were obtained using several combinations of laser power and scan speed. These features were quantified using process learning, which was then used to conduct a full factorial design that allows to estimate the effect of the process parameters on the output features. Both single and multi-response analysis are applied to analyze the output response. It was observed that laser power has more influential effect on all the features. Multi response analysis showed 150 W laser power and 200 mm/s produced bead with best possible features.
ContributorsAhsan, Faiyaz (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Kwon, Beomjin (Committee member) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Magnetic liquids called ferrofluids have been used in applications ranging from audio speaker cooling and rotary pressure seals to retinal detachment surgery and implantable artificial glaucoma valves. Recently, ferrofluids have been investigated as a material for use in magnetically controllable liquid droplet robotics. Liquid droplet robotics is an emerging technology that aims to apply control theory to manipulate fluid droplets as robotic agents to perform a wide range of tasks. Furthermore, magnetically controlled micro-robotics is another popular area of study where manipulating a magnetic field allows for the control of magnetized micro-robots. Both of these emerging fields have potential for impact toward medical applications: liquid characteristics such as being able to dissolve various compounds, be injected via a needle, and the potential for the human body to automatically filter and remove a liquid droplet robot, make liquid droplet robots advantageous for medical applications; while the ability to remotely control the torques and forces on an untethered microrobot via modulating the magnetic field and gradient is also highly advantageous. The research described in this dissertation explores applications and methods for the electromagnetic control of ferrofluid droplet robots. First, basic electrical components built from fluidic channels containing ferrofluid are made remotely tunable via the placement of ferrofluid within the channel. Second, a ferrofluid droplet is shown to be fully controllable in position, stretch direction, and stretch length in two dimensions using proportional-integral-derivative (PID) controllers. Third, control of a ferrofluid’s position, stretch direction, and stretch length is extended to three dimensions, and control gains are optimized via a Bayesian optimization process to achieve higher accuracy. Finally, magnetic control of both single and multiple ferrofluid droplets in two dimensions is investigated via a visual model predictive control approach based on machine learning. These achievements take both liquid droplet robotics and magnetic micro-robotics fields several steps closer toward real-world medical applications such as embedded soft electronic health monitors, liquid-droplet-robot-based drug delivery, and automated magnetically actuated surgeries.
ContributorsAhmed, Reza James (Author) / Marvi, Hamidreza (Thesis advisor) / Espanol, Malena (Committee member) / Rajagopalan, Jagannathan (Committee member) / Zhuang, Houlong (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2022