The concept of entrainment broadly applies the locking of phases between 2 independent systems [17]. This physical phenomenon can be applied to modify neuromuscular movement in humans during bipedal locomotion. Gait entrainment to robotic devices have shown great success as alternatives to labor intensive methods of rehabilitation. By applying additional torque at the ankle joint, previous studies have exhibited consistent gait entrainment to both rigid and soft robotic devices. This entrainment is characterized by consistent phase locking of plantarflexion perturbations to the ‘push off’ event within the gait cycle. However, it is unclear whether such phase locking can be attributed to the plantarflexion assistance from the device or the sensory stimulus of movement at the ankle. To clarify the mechanism of entrainment, an experiment was designed to expose the user to a multitude of varying torques applied at the ankle to assist with plantar flexion. In this experiment, no significant difference in success of subject entrainment occurred when additional torque applied was greater than a detectable level. Force applied at the ankle varied from ~60N to ~130N. This resulted in successful entrainment ~88\% of the time at 98 N, with little to no increase in success as force increased thereafter. Alternatively, success of trials decreased significantly as force was reduced below this level, causing the perturbations to become undetectable by participants. Ultimately this suggests that higher levels of actuator pressure, and thus greater levels of torque applied to the foot, do not increase the likelihood of entrainment during walking. Rather, the results of this study suggest that proper detectable sensory stimulus is the true mechanism for entrainment.

This thesis presents the design and simulation of an energy efficient controller for a system of three drones transporting a payload in a net. The object ensnared in the net is represented as a mass connected by massless stiff springs to each drone. Both a pole-placement approach and an optimal control approach are used to design a trajectory controller for the system. Results are simulated for a single drone and the three drone system both without and with payload.

The future of driving is largely headed towards autonomous vehicles, and this is clear with companies such as Tesla, Waymo, and even tech giant Apple. Many professionals predict that autonomous vehicles will likely be commercially available and legal to use in some places by the late 2020s [15]. There are some benefits to the rapid development of autonomous vehicle controllers, such as more independence for those who can’t drive due to impairments, the potential for reduced traffic, as well as possibly decreasing the number of accidents. Though these are promising prospects, there are ethical concerns regarding the implementation of such technology. The goal of this thesis is to provide an introductory literature review that discusses the history of autonomous vehicles, different levels of autonomy, ethical considerations in autonomous systems, and prior work on characterizing human driving behaviors and implementing these behaviors with autonomous vehicle controllers. Finally, recommendations are proposed for data collection on human driving behaviors in an ongoing NSF-funded project at Arizona State University, “Embodiment of Human Values Profiles in Autonomous Vehicles via Psychomimetic Controller Design.”