For a conventional quadcopter system with 4 planar rotors, flight times vary between 10 to 20 minutes depending on the weight of the quadcopter and the size of the battery used. In order to increase the flight time, either the weight of the quadcopter should be reduced or the battery size should be increased. Another way is to increase the efficiency of the propellers. Previous research shows that ducting a propeller can cause an increase of up to 94 % in the thrust produced by the rotor-duct system. This research focused on developing and testing a quadcopter having a centrally ducted rotor which produces 60 % of the total system thrust and 3 other peripheral rotors. This quadcopter will provide longer flight times while having the same maneuvering flexibility in planar movements.

Human walking is a complex and rhythmical activity that comprises of the brain, nerves and muscles. Neuromuscular disorder (NMD) is a broad term that refers to conditions that affect the proper use of muscles and nervous system, thus also impairing the walking or gait cycle of an individual. The improper gait cycle might be attributed to the lack of force produced at the toe-off stage. This project addresses if it is possible to create an OpenSim model to find the ideal time and force magnitude needed of an assistive force ankle device to improve gait patterns in individuals with NMD.

The global population over the age of 60 is estimated to rise to 23% by 2050 only increase the prevalence of functional neurological disorders and stroke. Increase in cases of functional neurological disorders and strokes will place a greater burden on the healthcare industry, specifically physical therapy. Physical therapy is vital for a patient’s recovery of motor function which is time demanding and taxing on the physical therapist. Wearable robotics have been proven to improve functional outcomes in gait rehabilitation by providing controlled high dosage and high-intensity training. Accurate control strategies for assistive robotic exoskeletons are vital for repetitive high precisions assistance for cerebral plasticity to occur.

This thesis presents a preliminary determination and design of a control algorithm for an assistive ankle device developed by the ASU RISE Laboratory. The assistive ankle device functions by compressing a spring upon heel strike during gait, remaining compressed during mid-stance and then releasing upon initiation of heel-off. The relationship between surface electromyography and ground reactions forces were used for identification of user-initiated heel-off. The muscle activation of the tibialis anterior combined with the ground reaction forces of the heel pressure sensor generated potential features that will be utilized in the revised control algorithm for the assistive ankle device. Work on this project must proceed in order to test and validate the revised control algorithm to determine its accuracy and precision.

Bicycles are already used for daily transportation by a large share of the world's population and provide a partial solution for many issues facing the world today. The low environmental impact of bicycling combined with the reduced requirement for road and parking spaces makes bicycles a good choice for transportation over short distances in urban areas. Bicycle riding has also been shown to improve overall health and increase life expectancy. However, riding a bicycle may be inconvenient or impossible for persons with disabilities due to the complex and coordinated nature of the task. Automated bicycles provide an interesting area of study for human-robot interaction, due to the number of contact points between the rider and the bicycle. The goal of the Smart Bike project is to provide a platform for future study of the physical interaction between a semi-autonomous bicycle robot and a human rider, with possible applications in rehabilitation and autonomous vehicle research.

This thesis presents the development of two balance control systems, which utilize actively controlled steering and a control moment gyroscope to stabilize the bicycle at high and low speeds. These systems may also be used to introduce disturbances, which can be useful for studying human reactions. The effectiveness of the steering balance control system is verified through testing with a PID controller in an outdoor environment. Also presented is the development of a force sensitive bicycle seat which provides feedback used to estimate the pose of the rider on the bicycle. The relationship between seat force distribution is demonstrated with a motion capture experiment. A corresponding software system is developed for balance control and sensor integration, with inputs from the rider, the internal balance and steering controller, and a remote operator.

For my thesis I worked in ASU’s Bio-Inspired Mechatronics lab on a project lead by PhD student Pham H. Nguyen (Berm) to develop an assistive soft robotic supernumerary limb. I contributed to the design and evaluation of two prototypes: the silicon based Soft Poly Limb (SPL) and one bladder-based fabric arm, the fabric Soft Poly Limb (fSPL). For both arms I was responsible for the design of 3D printed components (molds, end caps, etc.) as well as the evaluation of the completed prototypes by comparing the actual performance of the arms to the finite element predictions. I contributed to the writing of two published papers describing the design and evaluation of the two arms. After the completion of the fSPL I attempted to create a quasi-static model of the actuators driving the fSPL.

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.

Coordination and control of Intelligent Agents as a team is considered in this thesis.

Intelligent agents learn from experiences, and in times of uncertainty use the knowl-

edge acquired to make decisions and accomplish their individual or team objectives.

Agent objectives are defined using cost functions designed uniquely for the collective

task being performed. Individual agent costs are coupled in such a way that group ob-

jective is attained while minimizing individual costs. Information Asymmetry refers

to situations where interacting agents have no knowledge or partial knowledge of cost

functions of other agents. By virtue of their intelligence, i.e., by learning from past

experiences agents learn cost functions of other agents, predict their responses and

act adaptively to accomplish the team’s goal.

Algorithms that agents use for learning others’ cost functions are called Learn-

ing Algorithms, and algorithms agents use for computing actuation (control) which

drives them towards their goal and minimize their cost functions are called Control

Algorithms. Typically knowledge acquired using learning algorithms is used in con-

trol algorithms for computing control signals. Learning and control algorithms are

designed in such a way that the multi-agent system as a whole remains stable during

learning and later at an equilibrium. An equilibrium is defined as the event/point

where cost functions of all agents are optimized simultaneously. Cost functions are

designed so that the equilibrium coincides with the goal state multi-agent system as

a whole is trying to reach.

In collective load transport, two or more agents (robots) carry a load from point

A to point B in space. Robots could have different control preferences, for example,

different actuation abilities, however, are still required to coordinate and perform

load transport. Control preferences for each robot are characterized using a scalar

parameter θ i unique to the robot being considered and unknown to other robots.

With the aid of state and control input observations, agents learn control preferences

of other agents, optimize individual costs and drive the multi-agent system to a goal

state.

Two learning and Control algorithms are presented. In the first algorithm(LCA-

1), an existing work, each agent optimizes a cost function similar to 1-step receding

horizon optimal control problem for control. LCA-1 uses recursive least squares as

the learning algorithm and guarantees complete learning in two time steps. LCA-1 is

experimentally verified as part of this thesis.

A novel learning and control algorithm (LCA-2) is proposed and verified in sim-

ulations and on hardware. In LCA-2, each agent solves an infinite horizon linear

quadratic regulator (LQR) problem for computing control. LCA-2 uses a learning al-

gorithm similar to line search methods, and guarantees learning convergence to true

values asymptotically.

Simulations and hardware implementation show that the LCA-2 is stable for a

variety of systems. Load transport is demonstrated using both the algorithms. Ex-

periments running algorithm LCA-2 are able to resist disturbances and balance the

assumed load better compared to LCA-1.

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.

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.

This work presents the integration of user intent detection and control in the development of the fluid-driven, wearable, and continuum, Soft Poly-Limb (SPL). The SPL utilizes the numerous traits of soft robotics to enable a novel approach to provide safe and compliant mobile manipulation assistance to healthy and impaired users. This wearable system equips the user with an additional limb made of soft materials that can be controlled to produce complex three-dimensional motion in space, like its biological counterparts with hydrostatic muscles. Similar to the elephant trunk, the SPL is able to manipulate objects using various end effectors, such as suction adhesion or a soft grasper, and can also wrap its entire length around objects for manipulation. User control of the limb is demonstrated using multiple user intent detection modalities. Further, the performance of the SPL studied by testing its capability to interact safely and closely around a user through a spatial mobility test. Finally, the limb’s ability to assist the user is explored through multitasking scenarios and pick and place tests with varying mounting locations of the arm around the user’s body. The results of these assessments demonstrate the SPL’s ability to safely interact with the user while exhibiting promising performance in assisting the user with a wide variety of tasks, in both work and general living scenarios.