The sense of sight is arguably the most common method that our body uses for gathering data of the world around us. However, that primary tool is negated for those who are visually impaired, and thus must be replaced with a new bodily sense. Over the years there have been multiple attempts to determine the second best sense from which the brain can generate the most information, and to create a device that utilizes that sense to gather and relay the data quickly and efficiently. However, the sense that has gained the most favor among users and the most experimentation is that of touch. A haptic display device employs the sense of touch by breaking down an image viewed by the haptic display into pixels; each pixel is then translated to a certain vibrational frequency or electrical charge for the user to feel (depending on the brightness of the pixel). One can then distinguish the feeling of the square-like object through the device, however the main problem that exists among the current haptic display devices is the low-resolution output. The low resolution thus makes it difficult for a user to decipher between objects that share a similar shape, but are still completely different.

By considering a different method of delivering information to the brain via touch, it may become possible to create a haptic display that can relay environmental information to the brain in 64x64 resolution. The alternative solution is to replace the vibrating motors with vibrating cantilever beams, thus allowing more beams to take up a specific area in comparison to vibrating motors. Each beam will vary in length to establish its own natural frequency while also making it easier for each beam’s vibration to be controlled by a single microcontroller. Nathan Eastburn, a student who graduated in the spring of 2018, designed a wire-cutting machine that could pull the beams through a metal plate to strip the beam into smaller cross-sections and cut the beams into the very precise lengths. To further complete the machine, the mechanical aspects of the machine needed to be finalized and installed, specifically the air cylinder valve and blade attachments.

The following report provides the details and thought process in converting the given designs of the air pump and blade systems into the physical additions to the wire-cutting machine. Both systems have further parts that need to be purchased, components that must be manufactured, and/or redesigns to the functionality of the systems; these will be explained for those desiring to continue and complete the assembly of this machine.

This is a report on an experiment that examines if the principles of multimedia learning outlined in Richard E. Mayer’s journal article, “Using multimedia for e-learning”, located in the Journal of Computer Assisted Learning would apply to haptic feedback used for haptic robotic operation. This was tested by developing and using a haptic robotic manipulator known as the Haptic Testbed (HTB). The HTB is a manipulator designed to emulate human hand movement for haptic testing purposes and features an index finger and thumb for the right hand. Control is conducted through a Leap Motion Controller, a visual sensor that uses infrared lights and cameras to gather various data about hands it can see. The goal of the experiment was to have test subjects complete a task where they shifted objects along a circuit of positions where they were measured on time to complete the circuit as well as accuracy in reaching the individual points. Analysis of subject responses to surveys as well as performance during the experiment showed haptic feedback during training improving initial performance of individuals as well as lowering mental effort and mental demand during said training. The findings of this experiment showed support for the hypothesis that Mayer’s principles do apply to haptic feedback in training for haptic robotic manipulation. One of the implications of this experiment would be the possibility for haptics and tactile senses to be an applicable sense for Mayer’s principles of multimedia learning as most of the current work in the field is mostly focused on visual or auditory senses. If the results of the experiment were replicated in a future experiment it would provide support to the hypothesis that the principles of multimedia learning can be utilized to improve the training of haptic robotic operation.

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.

The world’s population is currently 9% visually impaired. Medical sciences do not have a biological fix that can cure this visual impairment. Visually impaired people are currently being assisted with biological fixes or assistive devices. The current assistive devices are limited in size as well as resolution. This thesis presents the development and experimental validation of a control system for a new vibrotactile haptic display that is currently in development. In order to allow the vibrotactile haptic display to be used to represent motion, the control system must be able to change the image displayed at a rate of at least 30 frames/second. In order to achieve this, this thesis introduces and investigates the use of three improvements: threading, change filtering, and wave libraries. Through these methods, it is determined that an average of 40 frames/second can be achieved.

As robotics technology advances, robots are being created for use in situations where they collaborate with humans on complex tasks.  For this to be safe and successful, it is important to understand what causes humans to trust robots more or less during a collaborative task.  This research project aims to investigate human-robot trust through a collaborative game of logic that can be played with a human and a robot together. This thesis details the development of a game of logic that could be used for this purpose. The game of logic is based upon a popular game in AI research called ‘Wumpus World’. The original Wumpus World game was a low-interactivity game to be played by humans alone. In this project, the Wumpus World game is modified for a high degree of interactivity with a human player, while also allowing the game to be played simultaneously by an AI algorithm.

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.

The field of robotics is rapidly expanding, and with it, the methods of teaching and introducing students must also advance alongside new technologies. There is a challenge in robotics education, especially at high school levels, to expose them to more modern and practical robots. One way to bridge this gap is human-robot interaction for a more hands-on and impactful experience that will leave students more interested in pursuing the field. Our project is a Robotic Head Kit that can be used in an educational setting to teach about its electrical, mechanical, programming, and psychological concepts. We took an existing robot head prototype and further advanced it so it can be easily assembled while still maintaining human complexity. Our research for this project dove into the electronics, mechanics, software, and even psychological barriers present in order to advance the already existing head design. The kit we have developed combines the field of robotics with psychology to create and add more life-like features and functionality to the robot, nicknamed "James Junior." The goal of our Honors Thesis was to initially fix electrical, mechanical, and software problems present. We were then tasked to run tests with high school students to validate our assembly instructions while gathering their observations and feedback about the robot's programmed reactions and emotions. The electrical problems were solved with custom PCBs designed to power and program the existing servo motors on the head. A new set of assembly instructions were written and modifications to the 3D printed parts were made for the kit. In software, existing code was improved to implement a user interface via keypad and joystick to give students control of the robot head they construct themselves. The results of our tests showed that we were not only successful in creating an intuitive robot head kit that could be easily assembled by high school students, but we were also successful in programming human-like expressions that could be emotionally perceived by the students.

Motion simulators are a common feature in everything from high end science museums to amusement parks, allowing a full ride experience on a small footprint and at a comparatively low cost relative to full size rides. The rapidly advancing field of virtual reality provides a potential increase in this desire for motion simulators, by combing virtual reality with motion simulation, total immersions can be created that is competitive with theme parks. While there exists a small number of commercially available consumer motion simulators, these tend to not have a wide enough range of motion to provide flexibility for use cases. This report is the documentation of an attempt to create a low cost consumer grade motion simulator prototype to determine to what extent an adequate motion simulation experience can be created in the home environment. This design made use of a two degree of freedom platform mounted on a universal joint as a trade off between flexibility of use and affordability of the end product. Ultimately, although the design and motor selection was sound, structural issues prevented the design from being capable of withstanding the necessary forces. However, as a prototype, important lessons were learned that could apply to a better-constructed second generation design. The results definitely show that motion simulators will, in the near future, become feasible for in-home amusement park recreation, at least for some amusement park rides.

Understanding the necessary skills required to work in an industry is a difficult task with many potential uses. By being able to predict the industry of a person based on their skills, professional social networks could make searching better with automated tagging, advertisers can target more carefully, and students can better find a career path that fits their skillset. The aim in this project is to apply deep learning to the world of professional networking. Deep Learning is a type of machine learning that has recently been making breakthroughs in the analysis of complex datasets that previously were not of much use. Initially the goal was to apply deep learning to the skills-to-company relationship, but a lack of quality data required a change to the skills-to-industry relationship. To accomplish the new goal, a database of LinkedIn profiles that are part of various industries was gathered and processed. From this dataset a model was created to take a list of skills and output an industry that people with those skills work in. Such a model has value in the insights that it forms allowing candidates to: determine what industry fits a skillset, identify key skills for industries, and locate which industries possible candidates may best fit in. Various models were trained and tested on a skill to industry dataset. The model was able to learn similarities between industries, and predict the most likely industries for each profiles skillset.