This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
This thesis proposes a new steering system for agricultural machinery with the aim of improving the automation capabilities of farming robots. Accurate and reliable autonomous machinery has the potential to provide significant benefits to the efficiency of farming operations, but the existing systems for performing one of the most essential…
This thesis proposes a new steering system for agricultural machinery with the aim of improving the automation capabilities of farming robots. Accurate and reliable autonomous machinery has the potential to provide significant benefits to the efficiency of farming operations, but the existing systems for performing one of the most essential automation functions, autonomous steering to keep machinery on the proper course, each have drawbacks that impact their usability in various scenarios. In order to address these issues, a new lidar-based system was developed for automatic steering in a typical farm field. This approach uses a two-dimensional lidar unit to scan the ground in front of the robot to detect and steer based on farm tracks, a common feature in many farm fields. This system was implemented and evaluated, with results demonstrating that the system is capable of providing accurate steering corrections.
In the past several years, the long-standing debate over freedom and responsibility has been applied to artificial intelligence (AI). Some such as Raul Hakli and Pekka Makela argue that no matter how complex robotics becomes, it is impossible for any robot to become a morally responsible agent. Hakli and Makela…
In the past several years, the long-standing debate over freedom and responsibility has been applied to artificial intelligence (AI). Some such as Raul Hakli and Pekka Makela argue that no matter how complex robotics becomes, it is impossible for any robot to become a morally responsible agent. Hakli and Makela assert that even if robots become complex enough that they possess all the capacities required for moral responsibility, their history of being programmed undermines the robot’s autonomy in a responsibility-undermining way. In this paper, I argue that a robot’s history of being programmed does not undermine that robot’s autonomy in a responsibility-undermining way. I begin the paper with an introduction to Raul and Hakli’s argument, as well as an introduction to several case studies that will be utilized to explain my argument throughout the paper. I then display why Hakli and Makela’s argument is a compelling case against robots being able to be morally responsible agents. Next, I extract Hakli and Makela’s argument and explain it thoroughly. I then present my counterargument and explain why it is a counterexample to that of Hakli and Makela’s.
The use of Artificial Intelligence in assistive systems is growing in application and efficiency. From self-driving cars, to medical and surgical robots and industrial tasked unsupervised co-robots; the use of AI and robotics to eliminate human error in high-stress environments and perform automated tasks is something that is advancing society’s…
The use of Artificial Intelligence in assistive systems is growing in application and efficiency. From self-driving cars, to medical and surgical robots and industrial tasked unsupervised co-robots; the use of AI and robotics to eliminate human error in high-stress environments and perform automated tasks is something that is advancing society’s status quo. Not only has the understanding of co-robotics exploded in the industrial world, but in research as well. The National Science Foundation (NSF) defines co-robots as the following: “...a robot whose main purpose is to work with people or other robots to accomplish a goal” (NSF, 1). The latest iteration of their National Robotics Initiative, NRI-2.0, focuses on efforts of creating co-robots optimized for ‘scalability, customizability, lowering barriers to entry, and societal impact’(NSF, 1). While many avenues have been explored for the implementation of co-robotics to create more efficient processes and sustainable lifestyles, this project’s focus was on societal impact co-robotics in the field of human safety and well-being. Introducing a co-robotics and computer vision AI solution for first responder assistance would help bring awareness and efficiency to public safety. The use of real-time identification techniques would create a greater range of awareness for first responders in high-stress situations. A combination of environmental features collected through sensors (camera and radar) could be used to identify people and objects within certain environments where visual impairments and obstructions are high (eg. burning buildings, smoke-filled rooms, ect.). Information about situational conditions (environmental readings, locations of other occupants, etc.) could be transmitted to first responders in emergency situations, maximizing situational awareness. This would not only aid first responders in the evaluation of emergency situations, but it would provide useful data for the first responder that would help materialize the most effective course of action for said situation.
