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- Genre: Masters Thesis
Description
The current study aims to explore factors affecting trust in human-drone collaboration. A current gap exists in research surrounding civilian drone use and the role of trust in human-drone interaction and collaboration. Specifically, existing research lacks an explanation of the relationship between drone pilot experience, trust, and trust-related behaviors as well as other factors. Using two dimensions of trust in human-automation team—purpose and performance—the effects of experience on drone design and trust is studied to explore factors that may contribute to such a model. An online survey was conducted to examine civilian drone operators’ experience, familiarity, expertise, and trust in commercially available drones. It was predicted that factors of prior experience (familiarity, self-reported expertise) would have a significant effect on trust in drones. The choice to use or exclude the drone propellers in a search-and-identify scenario, paired with the pilots’ experience with drones, would further confirm the relevance of the trust dimensions of purpose versus performance in the human-drone relationship. If the pilot has a positive sense of purpose and benevolence with the drone, the pilot trusts the drone has a positive intent towards them and the task. If the pilot has trust in the performance of the drone, they ascertain that the drone has the skill to do the task. The researcher found no significant differences between mean trust scores across levels of familiarity, but did find some interaction between self-report expertise, familiarity, and trust. Future research should further explore more concrete measures of situational participant factors such as self-confidence and expertise to understand their role in civilian pilots’ trust in their drone.
ContributorsNiichel, Madeline Kathleen (Author) / Chiou, Erin (Thesis advisor) / Cooke, Nancy J. (Committee member) / Craig, Scotty (Committee member) / Arizona State University (Publisher)
Created2019
Description
Unmanned Aerial Vehicles (UAVs) have become readily available for both the average consumer and professional due to decreases in price and increases in technological capabilities. This work ventured to explore the feasible use of UAV-technology in the area of roof analysis for facilities management purposes and contrast it to traditional techniques of inspection. An underlying goal of this work was two-fold. First, it was to calculate the upfront cost of investing in appropriate UAV equipment and training for a typical staff member to become proficient at doing such maintenance work in the practice of actual roof inspections on a sample set of roofs. Secondly, it was to compare the value of using this UAV method of investigation to traditional practices of inspecting roofs manually by personally viewing and walking roofs. The two methods for inspecting roofs were compared using various metrics, including time, cost, value, safety, and other relevant measurables. In addition to the study goals, this research was able to identify specific benefits and hazards for both methods of inspection through empirical trials. These points illustrate the study as Lessons Learned from the experience, which may be of interest to those Facilities Managers who are considering investing resources in UAV training and equipment for industrial purposes. Overall, this study helps to identify the utility of UAV technology in a well-established professional field in a way that has not been previously conducted in academia.
ContributorsBodily, Jordan (Author) / Sullivan, Kenneth (Thesis advisor) / Smithwick, Jake (Committee member) / Stone, Brian (Committee member) / Arizona State University (Publisher)
Created2020
Description
Multi-robot systems show great promise in performing complex tasks in areas ranging from search and rescue to interplanetary exploration. Yet controlling and coordinating the behaviors of these robots effectively is an open research problem. This research investigates techniques to control a multi-drone system where the drones learn to act in a physics-based simulator using demonstrations from artificially generated motion data that simulate flocking behavior in biological swarms. Using these demonstrations enables faster training than approaches where the agents start learning from scratch. The Graph Neural Network (GNN) controller used for the drones learns an efficient representation of low-level interactions in the system, allowing the proposed method to scale to more agents than in training data. This work also discusses techniques to improve performance in the face of real-world challenges such as sensor noise.
ContributorsKhopkar, Parth (Author) / Ben Amor, Heni H (Thesis advisor) / Pavlic, Theodore T (Committee member) / Zhou, Siyu S (Committee member) / Arizona State University (Publisher)
Created2021