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.

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).

Control algorithm development for quadrotor is usually based solely on rigid body dynamics neglecting aerodynamics. Recent work has demonstrated that such a model is suited only when operating at or near hover conditions and low-speed flight. When operating in confined spaces or during aggressive maneuvers destabilizing forces and moments are induced due to aerodynamic effects. Studies indicate that blade flapping, induced drag, and propeller drag influence forward flight performance while other effects like vortex ring state, ground effect affect vertical flight performance. In this thesis, an offboard data-driven approach is used to derive models for parasitic (bare-airframe) drag and propeller drag. Moreover, thrust and torque coefficients are identified from static bench tests. Among the two, parasitic drag is compensated for in the position controller module in the PX4 firmware. 2-D circular, straight line, and minimum snap rectangular trajectories with corridor constraints are tested exploiting differential flatness property wherein altitude and yaw angle are constant. Flight tests are conducted at ASU Drone Studio and results of tracking performance with default controller and with drag compensated position controller are presented. Root mean squared tracking error in individual axes is used as a metric to evaluate the model performance. Results indicate that, for circular trajectory, the root mean squared error in the x-axis has reduced by 44.54% and in the y-axis by 39.47%. Compensation in turn degrades the tracking in both axis by a maximum under 12% when compared to the default controller for rectangular trajectory case. The x-axis tracking error for the straight-line case has improved by 44.96% with almost no observable change in the y-axis.

Riding a bicycle requires accurately performing several tasks, such as balancing and navigation, which may be difficult or even impossible for persons with disabilities. These difficulties may be partly alleviated by providing active balance and steering assistance to the rider. In order to provide this assistance while maintaining free maneuverability, it is necessary to measure the position of the rider on the bicycle and to understand the rider's intent. Applying autonomy to bicycles also has the potential to address some of the challenges posed by traditional automobiles, including CO2 emissions, land use for roads and parking, pedestrian safety, high ownership cost, and difficulty traversing narrow or partially obstructed paths.

The Smart Bike research platform provides a set of sensors and actuators designed to aid in understanding human-bicycle interaction and to provide active balance control to the bicycle. The platform consists of two specially outfitted bicycles, one with force and inertial measurement sensors and the other with robotic steering and a control moment gyroscope, along with the associated software for collecting useful data and running controlled experiments. Each bicycle operates as a self-contained embedded system, which can be used for untethered field testing or can be linked to a remote user interface for real-time monitoring and configuration. Testing with both systems reveals promising capability for applications in human-bicycle interaction and robotics research.

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.

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.

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.

This paper describes the attempt of designing and building a two wheeled platform that is inherently unstable and discovering what tail design is suitable for stabilizing the platform. The platform is a 3D printed box that carries an Arduino, breadboard, MPU6050, a battery and a servo. This box is connected to two continuous servo motors (one on each side) that are attached to wheels, the breadboard and Arduino are mounted on the inside and the MPU6050 is mounted on the back of the base. The MPU6050 collects the data. In the program, that data will be the position of the accelerometer’s x-axis and that data will be sent to the servo motor with the tail for the controls aspect.

In the last decade, a large variety of algorithms have been developed for use in object tracking, environment mapping, and object classification. It is often difficult for beginners to fully predict the constraints that multirotors place on machine vision algorithms. The purpose of this paper is to explain some of the types of algorithms that can be applied to these aerial systems, why the constraints for these algorithms exist, and what could be done to mitigate them. This paper provides a summary of the processes involved in a popular filter-based tracking algorithm called MOSSE (Minimum Output Sum of Squared Error) and a particular implementation of SLAM (Simultaneous Localization and Mapping) called LSD SLAM.

In this article we present a program that is supplemental to a low-cost force-sensing quadrupedal laminate robot platform previously developed by Ben Shuch. The robot has four legs with two degrees of freedom apiece. Each leg is a four-bar mechanism controlled by two servo motors. The program that has been developed allows the user to predict the force distribution of the robot based on its configuration and the angle of the ground it is standing on. Through the use of this program, future students working on research involving this robot will be able to calculate the force distribution of the robot based on its configuration and generate ideal configurations of the robot using data gathered from force sensors attached to its feet.