Problem
Research questions and friction points this paper is trying to address.
First-person-view
aerial swarm
teleoperation
body-motion control
navigation efficiency
Body-Motion Control of a Simulated Aerial Swarm from a First-Person View
🤖 AI Summary
研究通过将身体动作映射为控制指令，解决了FPV视角下无人机群操控问题，提高了导航效率但增加了体力需求。
📝 Abstract
First-person-view (FPV) teleoperation of aerial swarms requires an operator to coordinate collective translation, viewing direction, and formation spacing. We present an upper-body interface that maps torso inclination, hand position, and head rotation to five continuous command dimensions. Neutral postures and motion ranges are calibrated for each participant. In a within-subject study, 14 participants navigated a simulated 15-agent swarm through three-dimensional obstacle courses using this interface and a conventional transmitter. Body-motion control reduced completion time by 19.4% and centroid path length by 7.0%, and increased path directness. Delivered-command variation was 88.8% lower, and concurrent command changes were more frequent. These command measures characterize the complete interfaces, which differed in calibration and filtering. No differences were detected in gate-centering error, collection yield, crash or disconnection counts, overall workload, or usability. All participants reported higher physical demand with body-motion control. The implemented interface therefore improved FPV navigation efficiency at the cost of greater physical demand.
Innovation
Methods, ideas, or system contributions that make the work stand out.
body-motion control
FPV teleoperation
aerial swarm
torso inclination
command dimensions
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