In this thesis, we develop a new method for Additive Manufacturing of felt to make three dimensional objects. Felting is a method of intertwining fibers to make a piece of textile. In this work, a 6 DOF UR-5 robotic arm equipped with a 3 DOF tool head to test various approaches to using felting. Due to the novelty of this approach several different control architectures and methodologies are presented. We created felted test samples using a range of processing conditions, and tested them in an Instron machine. Samples were tested parallel to the roving fiber direction and perpendicular to the roving fiber direction. Additionally, two pieces of felt were attached to each other with needling, and these were tested with T-peel tests, pulling both in the direction of the roving fibers and perpendicular to the fibers. We present results for the Young's Modulus and Ultimate Strength of each of these samples. It is anticipated that given the appropriate combination of materials and robotic tooling, this method could be used to make parts for a multitude of applications ranging from custom footwear to advanced composites. / Master of Science / In this paper a new approach to Additive Manufacturing centered on mechanically binding fibers together into a cohesive part is presented. This is accomplished via a robotic system and a process called felting, whereby needles push fibers into each other, entangling them. To validate this approach each system and method was tested individually. We present the results of mechanical tests of a variety of felted samples. Given the results, it is believed that robotic needle felting may be a beneficial method of manufacture for several fields, and it has the potential to easily make customized products.

As power demand across communities increases, focus has been given to the maintenance of power lines against harsh environments such as wind-induced vibration (WIV). Currently, Inspection robots are used for maintenance efforts while fixed tuned mass dampers (FTMDs) are used to prevent structural damages. However, both solutions are facing many challenges. Inspection robots are limited by their size and considerable power demand, while FTMDs are narrowband and unable to adapt to changing wind characteristics, and thus are unable to reposition themselves at the antinodes of the vibrating loop. In view of these shortcomings, we propose a mobile damping robot (MDR) that integrates inspection robots' mobility and FTMDs WIV vibration control to help maintain power lines. In this effort, we model the conductor and the MDR by using Hamilton's principle and we consider the two-way nonlinear interaction between the MDR and the cable. The MDR is driven by a Proportional-Derivative controller to the optimal vibration location (i.e, antinodes) as the wind characteristics vary. The numerical simulations suggest that the MDR outperforms FTMDs for vibration mitigation. Furthermore, the key parameters that influence the performance of the MDR are identified through a parametric study. The findings could set up a platform to design a prototype and experimentally evaluate the performance of the MDR. / Master of Science / Power lines are civil structures that span more than 160000 miles across the United States. They help electrify businesses, factories and homes. However, power lines are subject to harsh environments with strong winds, which can cause Aeolian vibration. Vibration in this context corresponds to the oscillation of power lines in response to the wind. Aeolian vibration can cause significant structural damages that impact public safety and result in a significant economic loss. Today, different solutions have been explored to limit the damages to these key structures. For example, the lines are commonly inspected by foot patrol, helicopters, or inspection robots. These inspection techniques are labor intensive and expensive. Furthermore, Stockbridge dampers, mechanical vibration devices, can be used to reduce the vibration of the power line. However, Stockbridge dampers can get stuck at location called nodes, where they have zero efficiency. To tackle this issue, we propose a mobile damping robot that can re-adjust itself to points of maximum vibration to maximize vibration reduction. In this thesis, we explore the potential of this proposed solution and draw some conclusions of the numerical simulations.

Robots can use a collection of auditory, visual, or haptic interfaces to convey information to human collaborators. The way these interfaces select signals typically depends on the task that the human is trying to complete: for instance, a haptic wristband may vibrate when the human is moving quickly and stop when the user is stationary. But people interpret the same signals in different ways, so what one user finds intuitive another user may not understand. In the absence of task knowledge, conveying signals is even more difficult: without knowing what the human wants to do, how should the robot select signals that helps them accomplish their task? When paired with the seemingly infinite ways that humans can interpret signals, designing an optimal interface for all users seems impossible. This thesis presents an information-theoretic approach to communication in task-agnostic settings: a unified algorithmic formalism for learning co-adaptive interfaces from scratch without task knowledge. The resulting approach is user-specific and not tied to any interface modality. This method is further improved by introducing symmetrical properties using priors on communication. Although we cannot anticipate how a human will interpret signals, we can anticipate interface properties that humans may like. By integrating these functional priors in the aforementioned learning scheme, we achieve performance far better than baselines that have access to task knowledge. The results presented here indicate that users subjectively prefer interfaces generated from the presented learning scheme while enabling better performance and more efficient interactions. / Master of Science / This thesis presents a novel interface for robot-to-human communication that personalizes to the current user without either task-knowledge nor an interpretative model of the human. Suppose that you are trying to find the location of buried treasure in a sandbox. You don't know the location of the treasure, but a robotic assistant does. Unfortunately, the only way the assistant can communicate the position of the treasure to you is through two LEDs of varying intensity --- and neither you nor the robot have a mutually understood interpretation of those signals. Without knowing the robot's convention for communication, how should you interpret the robot's signals? There are infinitely many viable interpretations: perhaps a brighter signal means that the treasure is towards the center of the sandbox -- or something else entirely. The robot has a similar problem: how should it interpret your behavior? Without knowing what you want to do with the hidden information (i.e., your task) or how you behave (i.e., your interpretative model), there is an infinite number pairs for either that fit your behavior. This work presents an interface optimizer that maximizes the correlation between the human's behavior and the hidden information. Testing with real humans indicates that this learning scheme can produce useful communicative mappings --- without knowing the users' tasks or their interpretative models. Furthermore, we recognize that humans have common biases in their interpretation of the world (leading to biases in their interpretations of robot communication). Although we cannot assume how a specific user will interpret an interface's signal, we can assume user-friendly interface designs that most humans find intuitive. We leverage these biases to further improve the aforementioned learning scheme across several user studies. As such, the findings presented in this thesis have a direct impact on human-robot co-adaptation in task-agnostic settings.

This study focuses on the design and implementation of a humanoid robot for disaster response. In particular, this thesis investigates the lower body design in detail with the upper body discussed at a higher level. The Tactical Hazardous Operations Robot (THOR) was designed to compete in the DARPA Robotics Challenge where it needs to complete tasks based on first-responder operations. These tasks, ranging from traversing rough terrain through driving a utility vehicle, suggest a versatile platform in a human sized form factor. A physical experiment of the proposed tasks generated a set of joint range of motions (RoM). Desired limb lengths were determined by comparing existing robots, the test subject in the experiment of proposed tasks, and an average human. Simulations using the desired RoM and limb lengths were used to calculate baseline joint torques. Based on the generated design constraints, THOR is a 34 degree of freedom humanoid that stands 1.78 [m] tall and weighs 65 [kg]. The 12 lower body joints are driven by series elastic linear actuators with multiple joints actuated in parallel. The parallel actuation mimics the human body, where multiple muscles pull on the same joint cooperatively. The legs retain high joint torques throughout their large RoM with some joints achieving torques as high as 289 [Nm]. The upper body uses traditional rotary actuators to drive the waist, arms, and head. The proprioceptive sensor selection was influenced by past experience on humanoid platforms, and perception sensors were selected to match the competition. / Master of Science

A novel rotary series elastic actuator (RSEA) with a two-mode, or bimodal, elastic element was designed and tested. This device was developed to eliminate the compromise between human safety and robot performance. Rigid actuators can be dangerous to humans within a robot's workspace due to impacts or pinning scenarios. To increase safety, elastic elements can soften impacts and allow for escape should pinning occur. However, adding elasticity increases the complexity of the system, lowers the bandwidth, and can make control of the actuator more difficult. To get the best of both types of actuators, a bimodal clutch was designed to switch between rigid actuation for performance and elastic actuation for human safety. The actuator consisted of two main parts, a rigid rotary actuator using a harmonic gearhead and a drum brake designed to act as a clutch. The 200 W rotary actuator provides 54.7 Nm of torque with a maximum speed of 41.4 rpm. The measured efficiency was 0.797 due to a timing belt speed reduction that was then speed reduced with a harmonic gearhead. The clutch was a drum brake actuated with a pantograph linkage and ACME lead screw. This configuration produced 11 Nm of holding torque experimentally but was theoretically shown to produce up to 51.4 Nm with larger motors. The elastic element was designed using finite element analysis (FEA) and tested experimentally to find a measured stiffness of 290 Nm/rad. / Master of Science

This thesis discusses the use of Bayesian Inference in inferring over the human's objective for Human-Robot Interaction, more specifically, it focuses upon the adaptation of methods to better utilize the information for inferring upon the human's objective for Reward Learning and Communicative Shared Autonomy settings. To accomplish this, we first examine state-of-the-art methods for approaching Bayesian Inverse Reinforcement learning where we explore the strengths and weaknesses of current approaches. After which we explore alternative methods for approaching the problem, borrowing similar approaches to those of the statistics community to apply alternative methods to improve the sampling process over the human's belief. After this, I then move to a discussion on the setting of Shared Autonomy in the presence and absence of communication. These differences are then explored in our method for inferring upon an environment where the human is aware of the robot's intention and how this can be used to dramatically improve the robot's ability to cooperate and infer upon the human's objective. In total, I conclude that the use of these methods to better infer upon the human's objective significantly improves the performance and cohesion between the human and robot agents within these settings. / Master of Science / This thesis discusses the use of various methods to allow robots to better understand human actions so that they can learn and work with those humans. In this work we focus upon two areas of inferring the human's objective: The first is where we work with learning what things the human prioritizes when completing certain tasks to better utilize the information inherent in the environment to best learn those priorities such that a robot can replicate the given task. The second body of work surrounds Shared Autonomy where we work to have the robot better infer what task a human is going to do and thus better allow the robot to assist with this goal through using communicative interfaces to alter the information dynamic the robot uses to infer upon that human intent. Collectively, the work of the thesis works to push that the current inference methods for Human-Robot Interaction can be improved through the further progression of inference to best approximate the human's internal model in a given setting.

The work seeks toward further developing a novel robotic platform capable of stable three legged locomotion. This will be accomplished by creating a robust and adaptable robotic platform capable of executing different walking strategies and taking multiple continuous steps. Previous iterations of this platform have been developed, all of which have used a single gait strategy. This study will seek to develop two new strategies. The first of which is a modification of the original strategy with theoretically improved gate robustness. A second strategy will seek to implement more advanced control techniques to create a fully stable balanced gait. / Master of Science

The following study focuses on the design and validation of an underactuated robotic gripper built for the Tactical Hazardous Operations Robot (THOR). THOR is a humanoid robot designed for use in the DARPA Robotics Challenge (DRC) and the Shipboard Autonomous Fire Fighting Robot (SAFFiR) project, both of which pertain to completing tasks associated with disaster response. The gripper was designed to accomplish a list of specific tasks outlined by the DRC and SAFFiR project. Underactuation was utilized in the design of the gripper to keep its complexity low while acquiring the level of dexterity needed to complete the required tasks. The final gripper contains two actuators, two underactuated fingers and a fixed finger resulting in four total degrees of freedom (DOF). The gripper weighs 0.68 kg and is capable of producing up to 38 N and 62 N on its proximal and distal phalanges, respectively. The gripper was put through a series of tests to validate its performance pertaining to the specific list of tasks it was designed to complete. The results of these tests show the gripper is in fact capable of completing all the necessary actions but does so within some limitations. / Master of Science

In this thesis, we propose algorithms designed for monitoring hazardous agents. Because hazardous environmental monitoring is either tedious or dangerous for human operators, we seek a fully automated robotic system that can help humans. However, there are still many challenges from hardware design to algorithm design that restrict robots to be applied to practical applications. Among these challenges, we are particularly interested in dealing with algorithmic challenges primarily caused by sensing and communication limitations of robots. We develop algorithms with provable guarantees that map and track hazards using a team of robots. Our contributions are as follows. First, we address a situation where the number of hazardous agents is unknown and varies over time. We propose a search and tracking framework that can extract individual target tracks as well as estimate the number and the spatial density of targets. Second, we consider a team of robots tracking individual targets under limited bandwidth. We develop distributed algorithms that can find solutions in bounded amount of time. Third, we propose an algorithm for aerial robots that explores a translating hazardous plume of unknown size and shape. We present a recursive depth-first search-based algorithm that yields a constant competitive ratio for exploring a translating plume. Last, we take into account a heterogeneous team of robots to map and sample a translating plume. These contributions can be applied to a team of aerial robots and a robotic boat monitoring and sampling a translating hazardous plume over a lake. In this application, the aerial robots coordinate with each other to explore the plume and to inform the robotic boat while the robotic boat collects water samples for offline analysis. We demonstrate the performance of our algorithms through simulations and proof-of-concept field experiments for real-world environmental monitoring. / Doctor of Philosophy / Quick response to hazards is crucial as the hazards may put humans at risk and thorough removal of hazards may take a substantial amount of time. Our vision is that the introduction of a robotic solution would be beneficial for hazardous environmental monitoring. Not only the fact that humans can be released from dangerous or tedious tasks, but we also can take advantage of the robot's agile maneuverability and its precise sensing. However, the development on both hardware and software is not yet ripe to be able to deploy autonomous robots in real-world scenarios. Moreover, partial and uncertain information of hazards impose further challenges. In this these, we present various research problems addressing these challenges in hazardous environmental monitoring. Particularly, we are interested in overcoming challenges from the perspective of software by designing planning and decision-making algorithms for robots. We validate our proposed algorithms through extensive simulations and real-world experiments.

Connected and Automated Vehicles (CAVs) represent a growing area of study in robotics and automotive research. Their potential benefits of increased traffic flow, reduced on-road accident, and improved fuel economy make them an attractive option. While some autonomous features such as Adaptive Cruise Control and Lane Keep Assist are already integrated into consumer vehicles, they are limited in scope and require innovation to realize fully autonomous vehicles. This work addresses the design problems of perception and planning in CAVs. A decentralized sensor fusion system is designed using Multi-target tracking to identify targets within a vehicle's field of view, enumerate each target with the lane it occupies, and highlight the most important object (MIO) for Adaptive cruise control. Its performance is tested using the Optimal Sub-pattern Assignment (OSPA) metric and correct assignment rate of the MIO. The system has an average accuracy assigning the MIO of 98%. The rest of this work considers the coordination of multiple CAVs from a multi-agent motion planning perspective. A centralized planning algorithm is applied to a space similar to a traffic intersection and is demonstrated empirically to be twice as fast as existing multi-agent planners., making it suitable for real-time planning environments. / Master of Science / Connected and Automated Vehicles are an emerging area of research that involve integrating computational components to enable autonomous driving. This work considers two of the major challenges in this area of research. The first half of this thesis considers how to design a perception system in the vehicle that can correctly track other vehicles and assess their relative importance in the environment. A sensor fusion system is designed which incorporates information from different sensor types to form a list of relevant target objects. The rest of this work considers the high-level problem of coordination between autonomous vehicles. A planning algorithm which plans the paths of multiple autonomous vehicles that is guaranteed to prevent collisions and is empirically faster than existing planning methods is demonstrated.