InDrone: a 2D-based drone flight behavior visualization platform for indoor building inspection
| Autor: | William Benda, Ricardo Eiris, Gilles Albeaino, Randi Faris, Masoud Gheisari |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Renewable Energy
Sustainability and the Environment Computer science Real-time computing 0211 other engineering and technologies Human Factors and Ergonomics 02 engineering and technology Building and Construction Management Monitoring Policy and Law Drone Visualization Urban Studies 021105 building & construction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Building inspection Civil and Structural Engineering |
| Zdroj: | Smart and Sustainable Built Environment. 10:438-456 |
| ISSN: | 2046-6099 |
| DOI: | 10.1108/sasbe-03-2021-0036 |
| Popis: | PurposeThe purpose of this research is to explore how to visually represent human decision-making processes during the performance of indoor building inspection flight operations using drones.Design/methodology/approachData from expert pilots were collected using a virtual reality drone flight simulator. The expert pilot data were studied to inform the development of an interactive 2D representation of drone flight spatial and temporal data – InDrone. Within the InDrone platform, expert pilot data were visually encoded to characterize key pilot behaviors in terms of pilots' approaches to view and difficulties encountered while detecting the inspection markers. The InDrone platform was evaluated using a user-center experimental methodology focusing on two metrics: (1) how novice pilots understood the flight approaches and difficulties contained within InDrone and (2) the perceived usability of the InDrone platform.FindingsThe results of the study indicated that novice pilots recognized inspection markers and difficult-to-inspect building areas in 63% (STD = 48%) and 75% (STD = 35%) of the time on average, respectively. Overall, the usability of InDrone presented high scores as demonstrated by the novice pilots during the flight pattern recognition tasks with a mean score of 77% (STD = 15%).Originality/valueThis research contributes to the definition of visual affordances that support the communication of human decision-making during drone indoor building inspection flight operations. The developed InDrone platform highlights the necessity of defining visual affordances to explore drone flight spatial and temporal data for indoor building inspections. |
| Databáze: | OpenAIRE |
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