The human shoulder plays an integral role in upper limb motor function. As the basis of arm motion, its performance is vital to the accomplishment of daily tasks. Impaired motor control, as a result of stroke or other disease, can cause errors in shoulder position to accumulate and propagate to the entire arm. This is why it is a highlight of concern for clinicians and why it is an important point of study. One of the primary causes of impaired shoulder motor control is abnormal mechanical joint impedance, which can be modeled as a 2nd order system consisting of mass, spring and damper. Quantifying shoulder stiffness and damping between healthy and impaired subjects could help improve our collective understanding of how many different neuromuscular diseases impact arm performance. This improved understanding could even lead to better rehabilitation protocols for conditions such as stroke through better identification and targeting of damping dependent spasticity and stiffness dependent hypertonicity. Despite its importance, there is a fundamental knowledge gap in the understanding of shoulder impedance, mainly due to a lack of appropriate characterization tools. Therefore, in order to better quantify shoulder stiffness and damping, a novel low-inertia shoulder exoskeleton is introduced in this work. The device was developed using a newly pioneered parallel actuated robot architecture specifically designed to interface with complex biological joints like the shoulder, hip, wrist and ankle. In addition to presenting the kinematics and dynamics of the shoulder exoskeleton, a series of validation experiments are performed on a human shoulder mock-up to quantify its ability to estimate known impedance properties. Finally, some preliminary data from human experiments is provided to demonstrate the device’s ability to collect the measurements needed to estimate shoulder stiffness and damping while worn by a subject.

Advancements in the field of design and control of lower extremity robotics requires a comprehensive understanding of the underlying mechanics of the human ankle. The ankle joint acts as an essential interface between the neuromuscular system of the body and the physical world, especially during locomotion. This paper investigates how the modulation of ankle stiffness is altered throughout the stance phase of the gait cycle depending on the environment the ankle is interacting with. Ten young healthy subjects with no neurological impairments or history of ankle injury were tested by walking over a robotic platform which collected torque and position data. The platform performed a perturbation on the ankle at 20%, 40%, and 60% of their stance phase in order to estimate ankle stiffness and evaluate if the environment plays a role on its modulation. The platform provided either a rigid environment or a compliant environment in which it was compliant and deflected according to the torque applied to the platform. Subjects adapted in different ways to achieve balance in the different environments. When comparing the environments, subjects modulated their stiffness to either increase, decrease, or remain the same. Notably, stiffness as well as the subjects’ center of pressure was found to increase with time as they transitioned from late loading to terminal stance (heel strike to toe-off) regardless of environmental conditions. This allowed for a model of ankle stiffness to be developed as a function of center of pressure, independent of whether a subject is walking on the rigid or compliant environment. The modulation of stiffness parameters characterized in this study can be used in the design and control of lower extremity robotics which focus on accurate biomimicry of the healthy human ankle. The stiffness characteristics can also be used to help identify particular ankle impairments and to design proper treatment for individuals such as those who have suffered from a stroke or MS. Changing environments is where a majority of tripping incidents occur, which can lead to significant injuries. For this reason, studying healthy ankle behavior in a variety of environments is of particular interest.

Current robotic systems have difficulties traversing and interacting with complex and deformable terrains, such as sand, mud, and water. This research intends to find hierarchical concepts that can be implemented into robotic systems for efficient locomotion by studying animal interactions with these terrains. Due to specific biological characteristics and environmental factors, the basilisk lizard is one animal that can transition easily between many types of terrain. This research will investigate the dynamics and kinematics of the basilisk lizard as it runs on the surface of water. Specifically, a fluid dynamic force platform has been designed and developed that will directly measure the forces exerted by the animal’s feet as it runs across the water. This platform will be used in conjunction with a motion capture system to characterize the basilisk lizard movements. This report examines the design and development of the force platform, the characterization of the frequencies of the platform leading to validation, and presents observations from preliminary lizard experiments with the setup.

The Basilisk lizard is known for its agile locomotion capabilities on granular and aquatic media making it an impressive model organism for studying multi-terrain locomotion mechanics. The work presented here is aimed at understanding locomotion characteristics of Basilisk lizards through a systematic series of robotic and animal experiments. In this work, a Basilisk lizard inspired legged robot with bipedal and quadrupedal locomotion capabilities is presented. A series of robot experiments are conducted on dry and wet (saturated) granular media to determine the effects of gait parameters and substrate saturation, on robot velocity and energetics. Gait parameters studied here are stride frequency and stride length. Results of robot experiments are compared with previously obtained animal data. It is observed that for a fixed robot stride frequency, velocity and stride length increase with increasing saturation, confirming the locomotion characteristics of the Basilisk lizard. It is further observed that with increasing saturation level, robot cost of transport decreases. An identical series of robot experiments are performed with quadrupedal gait to determine effects of gait parameters on robot performance. Generally, energetics of bipedal running is observed to be higher than quadrupedal operation. Experimental results also reveal how gait parameters can be varied to achieve different desired velocities depending on the substrate saturation level. In addition to robot experiments on granular media, a series of animal experiments are conducted to determine and characterize strategies

exhibited by Basilisk lizards when transitioning from granular to aquatic media.

The entirely soft-tissue anatomy of the octopus arm provides the animal with a large amount of freedom of movement, while still allowing the specimen to support itself despite the lack of a skeletal system. This is made possible through the use of various muscle layers within the octopus arm, which act as muscular hydrostats. Magnetic Resonance imaging of the octopus arm was employed to view the muscle layers within the octopus arm and observe trends and differences in these layers at the proximal, middle, and distal portions of the arms. A total of 39 arms from 6 specimens were imaged to give 112 total imaged sections (38 proximal, 37 middle, 37 distal). Significant increases in both the internal longitudinal muscle layer and the nervous core were found between the proximal and middle, proximal and distal, and middle and distal sections of the arms. This could reflect selection for these structures distally in the octopus arm for predator or other noxious stimuli avoidance. A significant decrease in the transverse muscle layer was found in the middle and distal sections of the arms. This could reflect selection for elongation in the proximal portion of the octopus arm or could be the result of selection for the internal longitudinal muscle layer and nervous core distally. Previous studies on Octopus vulgaris showed a preference for using the proximal arms in the pushing movement of crawling and a preference for using the anterior arms in exploring behaviors (Levy et al., 2015 and Byrne et al., 2006). Differences between the anterior and posterior arms for the transverse muscle layer, internal longitudinal muscle layer, and the nervous core were insignificant, reflecting a lack of structure-function relationships. This could also be due to a low sample size. Differences between the left and right arms for the transverse muscle layer, internal longitudinal muscle layer, and the nervous core were insignificant, supporting previous evidence that left versus right eye and arm preferences in octopus are not population-wide, but individual. Some slight trends can be found for individual arms, but the sample size was too small to make definitive statements regarding differences among specific arms.

Robotic swarms can potentially perform complicated tasks such as exploration and mapping at large space and time scales in a parallel and robust fashion. This thesis presents strategies for mapping environmental features of interest – specifically obstacles, collision-free paths, generating a metric map and estimating scalar density fields– in an unknown domain using data obtained by a swarm of resource-constrained robots. First, an approach was developed for mapping a single obstacle using a swarm of point-mass robots with both directed and random motion. The swarm population dynamics are modeled by a set of advection-diffusion-reaction partial differential equations (PDEs) in which a spatially-dependent indicator function marks the presence or absence of the obstacle in the domain. The indicator function is estimated by solving an optimization problem with PDEs as constraints. Second, a methodology for constructing a topological map of an unknown environment was proposed, which indicates collision-free paths for navigation, from data collected by a swarm of finite-sized robots. As an initial step, the number of topological features in the domain was quantified by applying tools from algebraic topology, to a probability function over the explored region that indicates the presence of obstacles. A topological map of the domain is then generated using a graph-based wave propagation algorithm. This approach is further extended, enabling the technique to construct a metric map of an unknown domain with obstacles using uncertain position data collected by a swarm of resource-constrained robots, filtered using intensity measurements of an external signal. Next, a distributed method was developed to construct the occupancy grid map of an unknown environment using a swarm of inexpensive robots or mobile sensors with limited communication. In addition to this, an exploration strategy which combines information theoretic ideas with Levy walks was also proposed. Finally, the problem of reconstructing a two-dimensional scalar field using observations from a subset of a sensor network in which each node communicates its local measurements to its neighboring nodes was addressed. This problem reduces to estimating the initial condition of a large interconnected system with first-order linear dynamics, which can be solved as an optimization problem.

Existing robotic excavation research has been primarily focused on lunar mining missions or simple traffic control in confined tunnels, however little work attempts to bring collective excavation into the realm of human infrastructure. This thesis explores a decentralized approach to excavation processes, where traffic laws are borrowed from swarms of fire ants (Solenopsis invicta) or termites (Coptotermes formosanus) to create decision rules for a swarm of robots working together and organizing effectively to create a desired final excavated pattern.

First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation.

To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications.

The role of robotics mobility is essential in the world of research because it allows humans to perform jobs that are dull, dirty, or dangerous without being physically present. A typical robot environment is one that is smooth and predictable. Screw-powered vehicles (SPV's) have commonly been used in these predictable environment situations such as terrestrial applications like mud and snow. However, a gap remains in SPV's traversing complex environments, particularly debris and granular material. The goal is to study the characteristics of how a SPV might move and generate force in such a granular environment for Earth and space. In our study, the chosen granular environment is soda-lime glass beads for easy characterization. This study with glass beads focuses on two separate approaches. The first approach is using a single screw rotating while the apparatus remains static and analyzing the forces that impact the screw. The second approach includes using a full body craft with two double helix screws and analyzing the translational velocity of the craft. This study presents both experimental and computational results using simulations with Multi-Body Dynamics (MBD) and Discrete Element Method (DEM) software packages to investigate the trends of SPV's in a granular environment.

In this work, different passive prosthetic ankles are studied. It is observed that complicated designs increase the cost of production, but simple designs have limited functionality. A new design for a passive prosthetic ankle is presented that is simple to manufacture while having superior functionality. This prosthetic ankle design has two springs: one mimicking Achilles tendon and the other mimicking Anterior-Tibialis tendon. The dynamics of the prosthetic ankle is discussed and simulated using Working model 2D. The simulation results are used to optimize the springs stiffness. Two experiments are conducted using the developed ankle to verify the simulation It is found that this novel ankle design is better than Solid Ankle Cushioned Heel (SACH) foot. The experimental data is used to find the tendon and muscle activation forces of the subject wearing the prosthesis using OpenSim. A conclusion is included along with suggested future work.

This thesis presents an approach to design and implementation of an adaptive boundary coverage control strategy for a swarm robotic system. Several fields of study are relevant to this project, including; dynamic modeling, control theory, programming, and robotic design. Tools and techniques from these fields were used to design and implement a model simulation and an experimental testbed. To achieve this goal, a simulation of the boundary coverage control strategy was first developed. This simulated model allowed for concept verification for different robot groups and boundary designs. The simulation consisted of a single, constantly expanding circular boundary with a modeled swarm of robots that autonomously allocate themselves around the boundary. Ultimately, this simulation was implemented in an experimental testbed consisting of mobile robots and a moving boundary wall to exhibit the behaviors of the simulated robots. The conclusions from this experiment are hoped to help make further advancements to swarm robotic technology. The results presented show promise for future progress in adaptive control strategies for robotic swarms.