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Evaluation of Stick Compliance Effects on Drone Handling Qualities

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Author(s)
Semoo Shin
Type
Thesis
Degree
Master
Department
정보컴퓨팅대학 AI융합학과(지능로봇프로그램)
Advisor
Kim, SeungJun
Abstract
In this thesis, we investigate how the resistance characteristics of input devices and vibration- based compliance illusion feedback affect handling experience and performance in an RC-style transmitter form factor. To this end, we built prototypes that integrate elastic and isometric joysticks with vibrotactile feedback, and conducted three within-subjects experiments to examine how vibration mapping, grain density, and resistance conditions influence perceived drone handling, distance estimation, and input performance. In the first study, we compared different vibration mappings and resistance conditions across four flight scenarios, and found that vibrations conveying the drone’s motion exerted localized effects on usability measures, but no specific combination with resistance consistently emerged as a superior condition. In the second study, we analyzed combinations of grain density and resistance in a far-distance LOS (Line-of-Sight) control scenario, and observed that perceived movement magnitude and sense of distance were mainly explained by the presence and representational form of vibration, with stick resistance playing only a secondary role. In the third study, we showed that in velocity- and acceleration-based pointing tasks, an isometric joystick offered advantages over an elastic joystick in terms of movement time and trajectory stability, whereas the presence of vibration did not produce significant changes in objective performance or task workload. Finally, drawing on the three studies, we discuss design implications: the interaction between base resistance and vibration feedback is weaker than expected; rich motion expressions may entail a trade- off with comfort; and in drone control interfaces, vibrotactile feedback should be designed primarily to enhance user experience rather than to improve performance.
URI
https://scholar.gist.ac.kr/handle/local/33744
Fulltext
http://gist.dcollection.net/common/orgView/200000947427
Alternative Author(s)
신세무
Appears in Collections:
Department of AI Convergence > 3. Theses(Master)
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