Matching Items (16)
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- All Subjects: Robotics
- Creators: Computer Science and Engineering Program
Description
Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.
ContributorsSaldyt, Lucas P (Author) / Ben Amor, Heni (Thesis director) / Pavlic, Theodore (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Not enough students are earning bachelor’s degrees in Computer Science, which is shocking as computing jobs are growing by the thousands (Zampa, 2016). These jobs have high-paying salaries and are not going to fade from the future any time soon, that is why the falling rates of computer science graduates are alarming. The working hypothesis on why so few college students major in computer science is that most think that it is too hard to learn (Wang, 2017). But I believe the real reason lies in that computer science is not an educational subject that is taught before university, which is too late for most students because by ages 12 to 13 (about seventh to eighth grade) they have decided that computer science concepts are “too difficult” for them to learn (Learning, 2022). Implementing a computer science-based education at an earlier age can possibly circumvent this seen development where students begin to lose confidence and doubt their abilities to learn computer science. This can be done easily by integrating computer science into academic subjects that are already taught in elementary schools such as science, math, and language arts as computer science uses logic, syntax, and other skills that are broadly applicable. Thus, I have created a introductory lesson plan for an elementary school class that incorporates learning how to code with robotics to promote learning computer science principles and destigmatize that it is “too hard” to learn in university.
ContributorsWong, Erika (Author) / Hedges, Craig (Thesis director) / Fischer, Adelheid (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
Preventive maintenance is a practice that has become popular in recent years, largely due to the increased dependency on electronics and other mechanical systems in modern technologies. The main idea of preventive maintenance is to take care of maintenance-type issues before they fully appear or cause disruption of processes and daily operations. One of the most important parts is being able to predict and foreshadow failures in the system, in order to make sure that those are fixed before they turn into large issues. One specific area where preventive maintenance is a very big part of daily activity is the automotive industry. Automobile owners are encouraged to take their cars in for maintenance on a routine schedule (based on mileage or time), or when their car signals that there is an issue (low oil levels for example). Although this level of maintenance is enough when people are in charge of cars, the rise of autonomous vehicles, specifically self-driving cars, changes that. Now instead of a human being able to look at a car and diagnose any issues, the car needs to be able to do this itself. The objective of this project was to create such a system. The Electronics Preventive Maintenance System is an internal system that is designed to meet all these criteria and more. The EPMS system is comprised of a central computer which monitors all major electronic components in an autonomous vehicle through the use of standard off-the-shelf sensors. The central computer compiles the sensor data, and is able to sort and analyze the readings. The filtered data is run through several mathematical models, each of which diagnoses issues in different parts of the vehicle. The data for each component in the vehicle is compared to pre-set operating conditions. These operating conditions are set in order to encompass all normal ranges of output. If the sensor data is outside the margins, the warning and deviation are recorded and a severity level is calculated. In addition to the individual focus, there's also a vehicle-wide model, which predicts how necessary maintenance is for the vehicle. All of these results are analyzed by a simple heuristic algorithm and a decision is made for the vehicle's health status, which is sent out to the Fleet Management System. This system allows for accurate, effortless monitoring of all parts of an autonomous vehicle as well as predictive modeling that allows the system to determine maintenance needs. With this system, human inspectors are no longer necessary for a fleet of autonomous vehicles. Instead, the Fleet Management System is able to oversee inspections, and the system operator is able to set parameters to decide when to send cars for maintenance. All the models used for the sensor and component analysis are tailored specifically to the vehicle. The models and operating margins are created using empirical data collected during normal testing operations. The system is modular and can be used in a variety of different vehicle platforms, including underwater autonomous vehicles and aerial vehicles.
ContributorsMian, Sami T. (Author) / Collofello, James (Thesis director) / Chen, Yinong (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Education in computer science is a difficult endeavor, with learning a new programing language being a barrier to entry, especially for college freshman and high school students. Learning a first programming language requires understanding the syntax of the language, the algorithms to use, and any additional complexities the language carries. Often times this becomes a deterrent from learning computer science at all. Especially in high school, students may not want to spend a year or more simply learning the syntax of a programming language. In order to overcome these issues, as well as to mitigate the issues caused by Microsoft discontinuing their Visual Programming Language (VPL), we have decided to implement a new VPL, ASU-VPL, based on Microsoft's VPL. ASU-VPL provides an environment where users can focus on algorithms and worry less about syntactic issues. ASU-VPL was built with the concepts of Robot as a Service and workflow based development in mind. As such, ASU-VPL is designed with the intention of allowing web services to be added to the toolbox (e.g. WSDL and REST services). ASU-VPL has strong support for multithreaded operations, including event driven development, and is built with Microsoft VPL users in mind. It provides support for many different robots, including Lego's third generation robots, i.e. EV3, and any open platform robots. To demonstrate the capabilities of ASU-VPL, this paper details the creation of an Intel Edison based robot and the use of ASU-VPL for programming both the Intel based robot and an EV3 robot. This paper will also discuss differences between ASU-VPL and Microsoft VPL as well as differences between developing for the EV3 and for an open platform robot.
ContributorsDe Luca, Gennaro (Author) / Chen, Yinong (Thesis director) / Cheng, Calvin (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Visual Odometry is one of the key aspects of robotic localization and mapping. Visual Odometry consists of many geometric-based approaches that convert visual data (images) into pose estimates of where the robot is in space. The classical geometric methods have shown promising results; they are carefully crafted and built explicitly for these tasks. However, such geometric methods require extreme fine-tuning and extensive prior knowledge to set up these systems for different scenarios. Classical Geometric approaches also require significant post-processing and optimization to minimize the error between the estimated pose and the global truth. In this body of work, the deep learning model was formed by combining SuperPoint and SuperGlue. The resulting model does not require any prior fine-tuning. It has been trained to enable both outdoor and indoor settings. The proposed deep learning model is applied to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset along with other classical geometric visual odometry models. The proposed deep learning model has not been trained on the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. It is only during experimentation that the deep learning model is first introduced to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. Using the monocular grayscale images from the visual odometer files of the Karlsruhe Institute of Technology and Toyota Technological Institute dataset, through the experiment to test the viability of the models for different sequences. The experiment has been performed on eight different sequences and has obtained the Absolute Trajectory Error and the time taken for each sequence to finish the computation. From the obtained results, there are inferences drawn from the classical and deep learning approaches.
ContributorsVaidyanathan, Venkatesh (Author) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Thesis advisor) / Michael, Katina (Committee member) / Arizona State University (Publisher)
Created2022
Description
As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the
burden on society. It will be essential to develop technology that can age with the
individual. One solution is to keep older adults in their homes longer through smart
home and smart living technology, allowing them to age in place. People have many
choices when choosing where to age in place, including their own homes, assisted
living facilities, nursing homes, or family members. No matter where people choose to
age, they may face isolation and financial hardships. It is crucial to keep finances in
mind when developing Smart Home technology.
Smart home technologies seek to allow individuals to stay inside their homes for
as long as possible, yet little work looks at how we can use technology in different
life stages. Robots are poised to impact society and ease burns at home and in the
workforce. Special attention has been given to social robots to ease isolation. As
social robots become accepted into society, researchers need to understand how these
robots should mimic natural conversation. My work attempts to answer this question
within social robotics by investigating how to make conversational robots natural and
reciprocal.
I investigated this through a 2x2 Wizard of Oz between-subjects user study. The
study lasted four months, testing four different levels of interactivity with the robot.
None of the levels were significantly different from the others, an unexpected result. I
then investigated the robot’s personality, the participant’s trust, and the participant’s
acceptance of the robot and how that influenced the study.
ContributorsMiller, Jordan (Author) / McDaniel, Troy (Thesis advisor) / Michael, Katina (Committee member) / Cooke, Nancy (Committee member) / Bryan, Chris (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
For those interested in the field of robotics, there are not many options to get your hands on a physical robot without paying a steep price. This is why the folks at BCN3D Technologies decided to design a fully open-source 3D-printable robotic arm. Their goal was to reduce the barrier to entry for the field of robotics and make it exponentially more accessible for people around the world. For our honors thesis, we chose to take the design from BCN3D and attempt to build their robot, to see how accessible the design truly is. Although their designs were not perfect and we were forced to make some adjustments to the 3D files, overall the work put forth by the people at BCN3D was extremely useful in successfully building a robotic arm that is programmed with ease.
ContributorsCohn, Riley (Co-author) / Petty, Charles (Co-author) / Ben Amor, Hani (Thesis director) / Yong, Sze Zheng (Committee member) / Computer Science and Engineering Program (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The goal of this project is to use an open-source solution to implement a drone Cyber-Physical System that can fly autonomously and accurately. The proof-of-concept to analyze the drone's flight capabilities is to fly in a pattern corresponding to the outline of an image, a process that requires both stability and precision to accurately depict the image. In this project, we found that building a Cyber-Physical System is difficult because of the tedious and complex nature of designing and testing the hardware and software solutions of this system. Furthermore, we reflect on the difficulties that arose from using open-source hardware and software.
ContributorsDedinsky, Rachel (Co-author) / Lubbers, Harrison James (Co-author) / Shrivastava, Aviral (Thesis director) / Dougherty, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Classical planning is a field of Artificial Intelligence concerned with allowing autonomous agents to make reasonable decisions in complex environments. This work investigates
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
ContributorsNakhleh, Julia Blair (Author) / Srivastava, Siddharth (Thesis director) / Fainekos, Georgios (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In this update to the ESPBot, we have introduced new libraries for a small OLED display and a beeper. This functionality can be easily expanded to multiple beepers and displays, but requires more GPIO pins, or for the user to not use some of the infrared sensors or the ultrasonic sensor. We have also relocated some of the pins. The display can be updated to display 1 of 4 predefined shapes, or to display user-defined text. New shapes can be added by defining new methods within display.ino and calling the appropriate functions while parsing the JSON data in viple.ino. The beeper can be controlled by user-defined input to play any frequency for any amount of time. There is also a function added to play the happy birthday song. More songs can be added by defining new methods within beeper.ino and calling the appropriate functions while parsing the JSON data in viple.ino. More functionality can be added to allow the user to input a list of frequencies along with a list of time so the user can define their own songs or sequences on the fly.
ContributorsWelfert, Monica Michelle (Co-author) / Nguyen, Van (Co-author) / Chen, Yinong (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-12