This thesis presents the design and testing of a soft robotic device for water utility pipeline inspection. The preliminary findings of this new approach to conventional methods of pipe inspection demonstrate that a soft inflatable robot can successfully traverse the interior space of a range of diameter pipes using pneumatic and without the need to adjust rigid, mechanical components. The robot utilizes inflatable soft actuators with an adjustable radius which, when pressurized, can provide a radial force, effectively anchoring the device in place. Additional soft inflatable actuators translate forces along the center axis of the device which creates forward locomotion when used in conjunction with the radial actuation. Furthermore, a bio-inspired control algorithm for locomotion allows the robot to maneuver through a pipe by mimicking the peristaltic gait of an inchworm. This thesis provides an examination and evaluation of the structure and behavior of the inflatable actuators through computational modeling of the material and design, as well as the experimental data of the forces and displacements generated by the actuators. The theoretical results are contrasted with/against experimental data utilizing a physical prototype of the soft robot. The design is anticipated to enable compliant robots to conform to the space offered to them and overcome occlusions from accumulated solids found in pipes. The intent of the device is to be used for inspecting existing pipelines owned and operated by Salt River Project, a Phoenix-area water and electricity utility provider.

Laminate devices have the potential to lower the cost and complexity of robots. Taking advantage of laminate materials' flexibility, a high-performance jumping platform has been developed with the goal of optimizing jump ground clearance. Four simulations are compared in order to understand which dynamic model elements (leg flexibility, motor dynamics, contact, joint damping, etc.) must be included to accurately model jumping performance. The resulting simulations have been validated with experimental data gathered from a small set of physical leg prototypes spanning design considerations such as gear ratio and leg length, and one in particular was selected for the fidelity of performance trends against experimental results. This simulation has subsequently been used to predict the performance of new leg designs outside the initial design set. The design predicted to achieve the highest jump ground clearance was then built and tested as a demonstration of the usefulness of this simulation.

Multi-material manufacturing combines multiple fabrication processes to produce individual parts that can be made up of several different materials. These processes can include both additive and subtractive manufacturing methods as well as embedding other components during manufacturing. This yields opportunities for creating single parts that can take the place of an assembly of parts produced using conventional techniques. Some example applications of multi-material manufacturing include parts that are produced using one process then machined to tolerance using another, parts with integrated flexible joints, or parts that contain discrete embedded components such as reinforcing materials or electronics.

Multi-material manufacturing has applications in robotics because, with it, mechanisms can be built into a design without adding additional moving parts. This allows for robot designs that are both robust and low cost, making it a particularly attractive method for education or research. 3D printing is of particular interest in this area because it is low cost, readily available, and capable of easily producing complicated part geometries. Some machines are also capable of depositing multiple materials during a single process. However, up to this point, planning the steps to create a part using multi-material manufacturing has been done manually, requiring specialized knowledge of the tools used. The difficulty of this planning procedure can prevent many students and researchers from using multi-material manufacturing.

This project studied methods of automating the planning of multi-material manufacturing processes through the development of a computational framework for processing 3D models and automatically generating viable manufacturing sequences. This framework includes solid operations and algorithms which assist the designer in computing manufacturing steps for multi-material models. This research is informing the development of a software planning tool which will simplify the planning needed by multi-material fabrication, making it more accessible for use in education or research.

In our paper, Voxel-Based Cad Framework for Planning Functionally Graded and Multi-Step Rapid Fabrication Processes, we present a new framework for representing and computing functionally-graded materials for use in rapid prototyping applications. We introduce the material description itself, low-level operations which can be used to combine one or more geometries together, and algorithms which assist the designer in computing manufacturing-compatible sequences. We then apply these techniques to several example scenarios. First, we demonstrate the use of a Gaussian blur to add graded material transitions to a model which can then be produced using a multi-material 3D printing process. Our second example highlights our solution to the problem of inserting a discrete, off-the-shelf part into a 3D printed model during the printing sequence. Finally, we implement this second example and manufacture two example components.

This project is investigating the impact curvature, buckling, and anisotropy play when used passively to enhance jumping capability. In this paper we employ a curved structure to allow a rigid link to collapse preferentially in one direction when it encounters aerodynamic drag forces. A joint of this nature could be used for passively actuated jump gliding, where wings would collapse immediately on takeoff and passively redeploy during descent, allowing the jumping robot to extend its horizontal range via gliding. A passively actuated joint is simpler and more lightweight than active solutions, allowing for a lighter glider and higher jumps. To test this, several prototype collapsing gliding wings of different diameters were tested by dropping them from a consistent height above the ground and by launching them upwards and recording their initial velocity. A model was constructed in Python using the data gathered through the experiments and was tuned so that its outputs were as close as possible to the experimental results. As expected, increasing the wing diameter increased the total fall time, and increasing the payload mass decreased the total fall time. Orientation of the wings around the vertical axis of the glider relative to the direction of horizontal motion was also found to have an effect on the length of time between when the gliding platform was launched and when it made contact with the ground, with a configuration where the axis between the wings was parallel to the direction of motion granting added stability.

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.

In this article we present a low-cost force-sensing quadrupedal laminate robot platform. The robot has two degrees of freedom on each of four independent legs, allowing for a variety of motion trajectories to be created at each leg, thus creating a rich control space to explore on a relatively low-cost robot. This platform allows a user to research complex motion and gait analysis control questions, and use different concepts in computer science and control theory methods to permit it to walk. The motion trajectory of each leg has been modeled in Python. Critical design considerations are: the complexity of the laminate design, the rigidity of the materials of which the laminate is constructed, the accuracy of the transmission to control each leg, and the design of the force sensing legs.

With the advent of GPGPU, many applications are being accelerated by using CUDA programing paradigm. We are able to achieve around 10x -100x speedups by simply porting the application on to the GPU and running the parallel chunk of code on its multi cored SIMT (Single instruction multiple thread) architecture. But for optimal performance it is necessary to make sure that all the GPU resources are efficiently used, and the latencies in the application are minimized. For this, it is essential to monitor the Hardware usage of the algorithm and thus diagnose the compute and memory bottlenecks in the implementation. In the following thesis, we will be analyzing the mapping of CUDA implementation of BLIINDS-II algorithm on the underlying GPU hardware, and come up with a Kepler architecture specific solution of using shuffle instruction via CUB library to tackle the two major bottlenecks in the algorithm. Experiments were conducted to convey the advantage of using shuffle instru3ction in algorithm over only using shared memory as a buffer to global memory. With the new implementation of BLIINDS-II algorithm using CUB library, a speedup of around 13.7% was achieved.

The technological advances in the past few decades have made possible creation and consumption of digital visual content at an explosive rate. Consequently, there is a need for efficient quality monitoring systems to ensure minimal degradation of images and videos during various processing operations like compression, transmission, storage etc. Objective Image Quality Assessment (IQA) algorithms have been developed that predict quality scores which match well with human subjective quality assessment. However, a lot of research still remains to be done before IQA algorithms can be deployed in real world systems. Long runtimes for one frame of image is a major hurdle. Graphics Processing Units (GPUs), equipped with massive number of computational cores, provide an opportunity to accelerate IQA algorithms by performing computations in parallel. Indeed, General Purpose Graphics Processing Units (GPGPU) techniques have been applied to a few Full Reference IQA algorithms which fall under the. We present a GPGPU implementation of Blind Image Integrity Notator using DCT Statistics (BLIINDS-II), which falls under the No Reference IQA algorithm paradigm. We have been able to achieve a speedup of over 30x over the previous CPU version of this algorithm. We test our implementation using various distorted images from the CSIQ database and present the performance trends observed. We achieve a very consistent performance of around 9 milliseconds per distorted image, which made possible the execution of over 100 images per second (100 fps).