Matching Items (70)
Description
As the world population continues to age, the demand for treatment and rehabilitation of long-term age-related ailments will rise. Healthcare technology must keep up with this demand, and existing solutions must become more readily available to the populace. Conditions such as impairment due to stroke currently take months or years of physical therapy to overcome, but rehabilitative exoskeletons can be used to greatly extend a physical therapist’s capabilities.
In this thesis, a rehabilitative knee exoskeleton was designed which is significantly lighter, more portable and less costly to manufacture than existing designs. It accomplishes this performance by making use of high-powered and weight-optimized brushless DC (BLDC) electric motors designed for drones, open-source hardware and software solutions for robotic motion control, and rapid prototyping technologies such as 3D printing and laser cutting.
The exoskeleton is made from a series of laser cut aluminum plates spaced apart with off-the-shelf standoffs. A drone motor with a torque of 1.32 Nm powers an 18.5:1 reduction two-stage belt drive, giving a maximum torque of 24.4 Nm at the output. The bearings for the belt drive are installed into 3D printed bearing mounts, which act as a snug intermediary between the bearing and the aluminum plate. The system is powered off a 24 volt, 1,500 MAh lithium battery, which can provide power for around an hour of walking activity.
The exoskeleton is controlled with an ODrive motor controller connected to a Raspberry Pi. Hip angle data is provided by an IMU, and the knee angle is provided by an encoder on the output shaft. A compact Rotary Series Elastic Actuator (cRSEA) device is mounted on the output shaft as well, to accurately measure the output torque going to the wearer. A Proportional-Derivative (PD) controller with feedforward relates the input current with the output torque. The device was tested on a treadmill and found to have an average backdrive torque of 0.39 Nm, significantly lower than the current state of the art. A gravity compensation controller and impedance controller were implemented to assist during swing and stance phases respectively. The results were compared to the muscular exertion of the knee measured via Electromyography (EMG).
In this thesis, a rehabilitative knee exoskeleton was designed which is significantly lighter, more portable and less costly to manufacture than existing designs. It accomplishes this performance by making use of high-powered and weight-optimized brushless DC (BLDC) electric motors designed for drones, open-source hardware and software solutions for robotic motion control, and rapid prototyping technologies such as 3D printing and laser cutting.
The exoskeleton is made from a series of laser cut aluminum plates spaced apart with off-the-shelf standoffs. A drone motor with a torque of 1.32 Nm powers an 18.5:1 reduction two-stage belt drive, giving a maximum torque of 24.4 Nm at the output. The bearings for the belt drive are installed into 3D printed bearing mounts, which act as a snug intermediary between the bearing and the aluminum plate. The system is powered off a 24 volt, 1,500 MAh lithium battery, which can provide power for around an hour of walking activity.
The exoskeleton is controlled with an ODrive motor controller connected to a Raspberry Pi. Hip angle data is provided by an IMU, and the knee angle is provided by an encoder on the output shaft. A compact Rotary Series Elastic Actuator (cRSEA) device is mounted on the output shaft as well, to accurately measure the output torque going to the wearer. A Proportional-Derivative (PD) controller with feedforward relates the input current with the output torque. The device was tested on a treadmill and found to have an average backdrive torque of 0.39 Nm, significantly lower than the current state of the art. A gravity compensation controller and impedance controller were implemented to assist during swing and stance phases respectively. The results were compared to the muscular exertion of the knee measured via Electromyography (EMG).
ContributorsParmentier, Robin W (Author) / Zhang, Wenlong (Thesis advisor) / Sugar, Thomas (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2020
Description
Traditionally, wearable exoskeletons for gait assistance have addressed the issue of high power requirement of providing support during walking. However, exoskeletons often are bulky, and suffer from misalignment of joints between the robot and the user. Soft robots in recent work have shown the ability to provide a high degree of compliance with a light weight and lower cost. This work presents the design, control, and evaluation of a soft inflatable exosuit to assist knee extension. First, the design of novel soft inflatable actuators of I cross-section and their application in the soft inflatable exosuit is presented. The actuators are applied to a soft and lightweight garment interface to assist in knee extension during the swing phase demonstrating reduced muscle activity for the quadriceps. Second, the control of the soft exosuit is presented with the introduction of a knee angle measurement system and smart shoe insole sensors. A new control method using human joint stiffness models as well as actuator models is developed. The new control method is evaluated with three users and a reduction in the sEMG activity of the quadriceps is observed with an increase in the activity of the hamstrings. Third, an improved version of the exosuit and a controller to assist knee extension in swing phase and initial stance are presented. The exosuit is applied to seven healthy and three impaired participants. Kinematics, muscle activity and gait compensations are studied. Reduced muscle activity for the quadriceps is seen in healthy participants with reduced execution times for functional activities such as timed up-and-go as well as sit-to-stand transitions in impaired participants. Finally, an untethered version of the soft exosuit using inflatable actuator composites and a portable pneumatic source are presented. Finite element models for the composites and inflatable actuators are generated and the actuators are characterized for performance. The design of a portable source for the exosuit is also presented. The inflatable actuator composites and the portable source are implemented in a portable exosuit system which demonstrated a reduction in the Vastus Lateralis activity during incline walking for three participants. Overall, this work investigated the feasibility of several versions of the soft exosuit for gait assistance.
ContributorsSridar, Saivimal (Author) / Zhang, Wenlong (Thesis advisor) / Sugar, Thomas (Committee member) / Lockhart, Thurmon (Committee member) / Arizona State University (Publisher)
Created2020
Description
Bicycles and motorcycles offer maneuverability, energy efficiency and acceleration that four wheeled vehicles cannot offer given similar budget for. Two wheeled vehicles have drastically different dynamics from four wheeled vehicles due to their instability and gyroscopic effect from their wheels.
This thesis focuses on self-stabilization of a motorcycle using an active control momentum gyroscope (CMG) and validation of this multi-degree-of-freedom system’s mathematical model. Physical platform was created to mimic the simulation as accurately as possible and all components used were justified. This process involves derivation of a 3 Degree-of-Freedom (DOF) system’s forward kinematics and its Jacobian matrix, simulation analysis of different controller algorithms, setting the system and subsystem specifications, and real system experimentation and data analysis.
A Jacobian matrix was used to calculate accurately decomposed resultant angular velocities which are used to create the dynamics model of the system torque using the Euler-Lagrange method. This produces a nonlinear second order differential equation that is modeled using MATLAB/Simulink. PID, and cascaded feedback loop are tested in this Simulink model. Cascaded feedback loop shows most promises in the simulation analysis. Therefore, system specifications are calculated according to the data produced by this controller method. The model validation is executed using the Vicon motion capture system which captured the roll angle of the motorcycle. This work contributes to creating a set of procedures for creating a validated dynamic model for a CMG stabilized motorcycle which can be used to create variants of other self-stabilizing motorcycle system.
This thesis focuses on self-stabilization of a motorcycle using an active control momentum gyroscope (CMG) and validation of this multi-degree-of-freedom system’s mathematical model. Physical platform was created to mimic the simulation as accurately as possible and all components used were justified. This process involves derivation of a 3 Degree-of-Freedom (DOF) system’s forward kinematics and its Jacobian matrix, simulation analysis of different controller algorithms, setting the system and subsystem specifications, and real system experimentation and data analysis.
A Jacobian matrix was used to calculate accurately decomposed resultant angular velocities which are used to create the dynamics model of the system torque using the Euler-Lagrange method. This produces a nonlinear second order differential equation that is modeled using MATLAB/Simulink. PID, and cascaded feedback loop are tested in this Simulink model. Cascaded feedback loop shows most promises in the simulation analysis. Therefore, system specifications are calculated according to the data produced by this controller method. The model validation is executed using the Vicon motion capture system which captured the roll angle of the motorcycle. This work contributes to creating a set of procedures for creating a validated dynamic model for a CMG stabilized motorcycle which can be used to create variants of other self-stabilizing motorcycle system.
ContributorsMoon, Hansol (Author) / Zhang, Wenlong (Thesis advisor) / Frank, Daniel (Committee member) / Delp, Deana (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2020
Description
The term Poly-Limb stems from the rare birth defect syndrome, called Polymelia. Although Poly-Limbs in nature have often been nonfunctional, humans have had the fascination of functional Poly-Limbs. Science fiction has led us to believe that having Poly-Limbs leads to augmented manipulation abilities and higher work efficiency. To bring this to life however, requires a synergistic combination between robot manipulation and wearable robotics. Where traditional robots feature precision and speed in constrained environments, the emerging field of soft robotics feature robots that are inherently compliant, lightweight, and cost effective. These features highlight the applicability of soft robotic systems to design personal, collaborative, and wearable systems such as the Soft Poly-Limb.
This dissertation presents the design and development of three actuator classes, made from various soft materials, such as elastomers and fabrics. These materials are initially studied and characterized, leading to actuators capable of various motion capabilities, like bending, twisting, extending, and contracting. These actuators are modeled and optimized, using computational models, in order to achieve the desired articulation and payload capabilities. Using these soft actuators, modular integrated designs are created for functional tasks that require larger degrees of freedom. This work focuses on the development, modeling, and evaluation of these soft robot prototypes.
In the first steps to understand whether humans have the capability of collaborating with a wearable Soft Poly-Limb, multiple versions of the Soft Poly-Limb are developed for assisting daily living tasks. The system is evaluated not only for performance, but also for safety, customizability, and modularity. Efforts were also made to monitor the position and orientation of the Soft Poly-Limbs components through embedded soft sensors and first steps were taken in developing self-powered compo-nents to bring the system out into the world. This work has pushed the boundaries of developing high powered-to-weight soft manipulators that can interact side-by-side with a human user and builds the foundation upon which researchers can investigate whether the brain can support additional limbs and whether these systems can truly allow users to augment their manipulation capabilities to improve their daily lives.
This dissertation presents the design and development of three actuator classes, made from various soft materials, such as elastomers and fabrics. These materials are initially studied and characterized, leading to actuators capable of various motion capabilities, like bending, twisting, extending, and contracting. These actuators are modeled and optimized, using computational models, in order to achieve the desired articulation and payload capabilities. Using these soft actuators, modular integrated designs are created for functional tasks that require larger degrees of freedom. This work focuses on the development, modeling, and evaluation of these soft robot prototypes.
In the first steps to understand whether humans have the capability of collaborating with a wearable Soft Poly-Limb, multiple versions of the Soft Poly-Limb are developed for assisting daily living tasks. The system is evaluated not only for performance, but also for safety, customizability, and modularity. Efforts were also made to monitor the position and orientation of the Soft Poly-Limbs components through embedded soft sensors and first steps were taken in developing self-powered compo-nents to bring the system out into the world. This work has pushed the boundaries of developing high powered-to-weight soft manipulators that can interact side-by-side with a human user and builds the foundation upon which researchers can investigate whether the brain can support additional limbs and whether these systems can truly allow users to augment their manipulation capabilities to improve their daily lives.
ContributorsNguyen, Pham Huy (Author) / Zhang, Wenlong (Thesis advisor) / Sugar, Thomas G. (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2020
Description
This thesis introduces a new robotic leg design with three degrees of freedom that
can be adapted for both bipedal and quadrupedal locomotive systems, and serves as
a blueprint for designers attempting to create low cost robot legs capable of balancing
and walking. Currently, bipedal leg designs are mostly rigid and have not strongly
taken into account the advantages/disadvantages of using an active ankle, as opposed
to a passive ankle, for balancing. This design uses low-cost compliant materials, but
the materials used are thick enough to mimic rigid properties under low stresses, so
this paper will treat the links as rigid materials. A new leg design has been created
that contains three degrees of freedom that can be adapted to contain either a passive
ankle using springs, or an actively controlled ankle using an additional actuator. This
thesis largely aims to focus on the ankle and foot design of the robot and the torque
and speed requirements of the design for motor selection. The dynamics of the system,
including height, foot width, weight, and resistances will be analyzed to determine
how to improve design performance. Model-based control techniques will be used to
control the angle of the leg for balancing. In doing so, it will also be shown that it
is possible to implement model-based control techniques on robots made of laminate
materials.
can be adapted for both bipedal and quadrupedal locomotive systems, and serves as
a blueprint for designers attempting to create low cost robot legs capable of balancing
and walking. Currently, bipedal leg designs are mostly rigid and have not strongly
taken into account the advantages/disadvantages of using an active ankle, as opposed
to a passive ankle, for balancing. This design uses low-cost compliant materials, but
the materials used are thick enough to mimic rigid properties under low stresses, so
this paper will treat the links as rigid materials. A new leg design has been created
that contains three degrees of freedom that can be adapted to contain either a passive
ankle using springs, or an actively controlled ankle using an additional actuator. This
thesis largely aims to focus on the ankle and foot design of the robot and the torque
and speed requirements of the design for motor selection. The dynamics of the system,
including height, foot width, weight, and resistances will be analyzed to determine
how to improve design performance. Model-based control techniques will be used to
control the angle of the leg for balancing. In doing so, it will also be shown that it
is possible to implement model-based control techniques on robots made of laminate
materials.
ContributorsShafa, Taha A (Author) / Aukes, Daniel M (Thesis advisor) / Rogers, Bradley (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2020
Description
In large modern urban areas, traffic congestion and fatality have become two serious problems. To improve the safety and efficiency of ground mobility, one promising solution is the cooperative control of connected and automated vehicle (CAV) systems, which can avoid human drivers’ incapability and errors. Taking advantage of two-dimensional (2D) vehicular control, this dissertation intends to conduct a thorough investigation of the modeling, control, and optimization of CAV systems with flocking control. Flocking is a dynamic swarm congregating behavior of a group of agents with self-organizing features, and flocking control of CAV systems attempts to achieve the maintenance of a small and nearly constant distance among vehicles, speed match, destination cohesion, and collision and obstacle avoidance.
Concerning artificial multi-agent systems, such as mobile robots and CAV systems, a set of engineering performance requirements should be considered in flocking theory for practical applications. In this dissertation, three novel flocking control protocols are studied, which consider convergence speed, permanent obstacle avoidance, and energy efficiency. Furthermore, considering nonlinear vehicle dynamics, a novel hierarchical flocking control framework is proposed for CAV systems to integrate high-level flocking coordination planning and low-level vehicle dynamics control together. On one hand, using 2D flocking theory, the decision making and motion planning of engaged vehicles are produced in a distributed manner based on shared information. On the other hand, using the proposed framework, many advanced vehicle dynamics control methods and tools are applicable. For instance, in the low-level vehicle dynamics control, in addition to path trajectory tracking, the maintenance of vehicle later/yaw stability and rollover propensity mitigation are achieved by using additional actuators, such as all-wheel driving and four-wheel steering, to enhance vehicle safety and efficiency with over-actuated features.
Co-simulations using MATLAB/Simulink and CarSim are conducted to illustrate the performances of the proposed flocking framework and all controller designs proposed in this dissertation. Moreover, a scaled CAV system is developed, and field experiments are also completed to further demonstrate the feasibility of the proposed flocking framework. Consequently, the proposed flocking framework can successfully complete a 2D vehicular flocking coordination. The novel flocking control protocols are also able to accommodate the practical requirements of artificial multi-agent systems by enhancing convergence speed, saving energy consumption, and avoiding permanent obstacles. In addition, employing the proposed control methods, vehicle stability is guaranteed as expected.
Concerning artificial multi-agent systems, such as mobile robots and CAV systems, a set of engineering performance requirements should be considered in flocking theory for practical applications. In this dissertation, three novel flocking control protocols are studied, which consider convergence speed, permanent obstacle avoidance, and energy efficiency. Furthermore, considering nonlinear vehicle dynamics, a novel hierarchical flocking control framework is proposed for CAV systems to integrate high-level flocking coordination planning and low-level vehicle dynamics control together. On one hand, using 2D flocking theory, the decision making and motion planning of engaged vehicles are produced in a distributed manner based on shared information. On the other hand, using the proposed framework, many advanced vehicle dynamics control methods and tools are applicable. For instance, in the low-level vehicle dynamics control, in addition to path trajectory tracking, the maintenance of vehicle later/yaw stability and rollover propensity mitigation are achieved by using additional actuators, such as all-wheel driving and four-wheel steering, to enhance vehicle safety and efficiency with over-actuated features.
Co-simulations using MATLAB/Simulink and CarSim are conducted to illustrate the performances of the proposed flocking framework and all controller designs proposed in this dissertation. Moreover, a scaled CAV system is developed, and field experiments are also completed to further demonstrate the feasibility of the proposed flocking framework. Consequently, the proposed flocking framework can successfully complete a 2D vehicular flocking coordination. The novel flocking control protocols are also able to accommodate the practical requirements of artificial multi-agent systems by enhancing convergence speed, saving energy consumption, and avoiding permanent obstacles. In addition, employing the proposed control methods, vehicle stability is guaranteed as expected.
ContributorsWang, Fengchen (Author) / Chen, Yan (Thesis advisor) / Nam, Changho (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2020
Description
Current exosuit technologies utilizing soft inflatable actuators for gait assistance have drawbacks of having slow dynamics and limited portability. The first part of this thesis focuses on addressing the aforementioned issues by using inflatable actuator composites (IAC) and a portable pneumatic source. Design, fabrication and finite element modeling of the IAC are presented. Volume optimization of the IAC is done by varying its internal volume using finite element methods. A portable air source for use in pneumatically actuated wearable devices is also presented. Evaluation of the system is carried out by analyzing its maximum pressure and flow output. Electro-pneumatic setup, design and fabrication of the developed air source are also shown. To provide assistance to the user using the exosuit in appropriate gait phases, a gait detection system is needed. In the second part of this thesis, a gait sensing system utilizing soft fabric based inflatable sensors embedded in a silicone based shoe insole is developed. Design, fabrication and mechanical characterization of the soft gait detection sensors are given. In addition, integration of the sensors, each capable of measuring loads of 700N in a silicone based shoe insole is also shown along with its possible application in detection of various gait phases. Finally, a possible integration of the actuators, air source and gait detection shoes in making of a portable soft exosuit for knee assistance is given.
Contributorspoddar, souvik (Author) / Zhang, Wenlong (Thesis advisor) / Lee, Hyunglae (Committee member) / Marvi, Hamidreza (Committee member) / Arizona State University (Publisher)
Created2020
Description
The ubiquity of single camera systems in society has made improving monocular depth estimation a topic of increasing interest in the broader computer vision community. Inspired by recent work in sparse-to-dense depth estimation, this thesis focuses on sparse patterns generated from feature detection based algorithms as opposed to regular grid sparse patterns used by previous work. This work focuses on using these feature-based sparse patterns to generate additional depth information by interpolating regions between clusters of samples that are in close proximity to each other. These interpolated sparse depths are used to enforce additional constraints on the network’s predictions. In addition to the improved depth prediction performance observed from incorporating the sparse sample information in the network compared to pure RGB-based methods, the experiments show that actively retraining a network on a small number of samples that deviate most from the interpolated sparse depths leads to better depth prediction overall.
This thesis also introduces a new metric, titled Edge, to quantify model performance in regions of an image that show the highest change in ground truth depth values along either the x-axis or the y-axis. Existing metrics in depth estimation like Root Mean Square Error(RMSE) and Mean Absolute Error(MAE) quantify model performance across the entire image and don’t focus on specific regions of an image that are hard to predict. To this end, the proposed Edge metric focuses specifically on these hard to classify regions. The experiments also show that using the Edge metric as a small addition to existing loss functions like L1 loss in current state-of-the-art methods leads to vastly improved performance in these hard to classify regions, while also improving performance across the board in every other metric.
This thesis also introduces a new metric, titled Edge, to quantify model performance in regions of an image that show the highest change in ground truth depth values along either the x-axis or the y-axis. Existing metrics in depth estimation like Root Mean Square Error(RMSE) and Mean Absolute Error(MAE) quantify model performance across the entire image and don’t focus on specific regions of an image that are hard to predict. To this end, the proposed Edge metric focuses specifically on these hard to classify regions. The experiments also show that using the Edge metric as a small addition to existing loss functions like L1 loss in current state-of-the-art methods leads to vastly improved performance in these hard to classify regions, while also improving performance across the board in every other metric.
ContributorsRai, Anshul (Author) / Yang, Yezhou (Thesis advisor) / Zhang, Wenlong (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2019
Description
Touch plays a vital role in maintaining human relationships through social andemotional communications. This research proposes a multi-modal haptic display
capable of generating vibrotactile and thermal haptic signals individually and simultaneously.
The main objective for creating this device is to explore the importance
of touch in social communication, which is absent in traditional communication
modes like a phone call or a video call. By studying how humans interpret
haptically generated messages, this research aims to create a new communication
channel for humans. This novel device will be worn on the user's forearm and has
a broad scope of applications such as navigation, social interactions, notifications,
health care, and education. The research methods include testing patterns in the
vibro-thermal modality while noting its realizability and accuracy. Different patterns
can be controlled and generated through an Android application connected to
the proposed device via Bluetooth. Experimental results indicate that the patterns
SINGLE TAP and HOLD/SQUEEZE were easily identifiable and more relatable to
social interactions. In contrast, other patterns like UP-DOWN, DOWN-UP, LEFTRIGHT,
LEFT-RIGHT, LEFT-DIAGONAL, and RIGHT-DIAGONAL were less
identifiable and less relatable to social interactions. Finally, design modifications
are required if complex social patterns are needed to be displayed on the forearm.
ContributorsGharat, Shubham Shriniwas (Author) / McDaniel, Troy (Thesis advisor) / Redkar, Sangram (Thesis advisor) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
In a pursuit-evasion setup where one group of agents tracks down another adversarial group, vision-based algorithms have been known to make use of techniques such as Linear Dynamic Estimation to determine the probable future location of an evader in a given environment. This helps a pursuer attain an edge over the evader that has conventionally benefited from the uncertainty of the pursuit. The pursuer can utilize this knowledge to enable a faster capture of the evader, as opposed to a pursuer that only knows the evader's current location. Inspired by the function of dorsal anterior cingulate cortex (dACC) neurons in natural predators, the use of a predictive model that is built using an encoder-decoder Long Short-Term Memory (LSTM) Network and can produce a more accurate estimate of the evader's future location is proposed. This enables an even quicker capture of a target when compared to previously used filtering-based methods. The effectiveness of the approach is evaluated by setting up these agents in an environment based in the Modular Open Robots Simulation Engine (MORSE). Cross-domain adaptability of the method, without the explicit need to retrain the prediction model is demonstrated by evaluating it in another domain.
ContributorsGodbole, Sumedh (Author) / Yang, Yezhou (Thesis advisor) / Srivastava, Siddharth (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021