用手触屏控制机械臂，操作更准更快，负担更小。
Design and Evaluation of a Touchscreen-Based Teleoperation Interface for Robotic Manipulators
- 用手指滑动直接控制机械臂运动，速度更精细，操控更自然。
- 任务完成时间减少53.5%，路径覆盖率提升至90.7%，超调更少。
- 适合需要高精度、低负荷的远程操作场景，如核电站采样。
直观的遥操作界面对在复杂环境中安全高效操控机械臂至关重要。在核工业中，表面接触任务如拭子采样需精确路径与力控、避障及持续专注，传统操纵杆难以有效支持。本研究设计并评估了一种新型触屏遥操作界面，将连续指尖运动直接映射为机械臂动作，提供更精细的速度控制，并实现操控与可视化融合，相比传统控制器更自然、精准、直观。20名参与者参与对比实验，使用该触屏界面、传统操纵杆及单击自主模式完成任务。实验通过Franka Emika Panda机械臂远程操控，跨国模拟真实表面操作。记录了运动学、生理及行为数据，全面评估性能、认知负荷与信任度。结果显示，触屏界面下任务完成效率更高，平均耗时从5.38分钟降至2.50分钟（降幅53.5%），正弦路径覆盖率达90.7%（操纵杆为84.1%），路径超调更少。认知负荷（NASA-TLX评分）由操纵杆的均值52降至触屏的43（-9点，-17.3%），最低为单击自主模式的31（较操纵杆降21点，-40.4%）。本研究提出一种易实现的触屏接口，显著提升遥操作表面任务表现并降低认知负荷。
原文摘要 · Abstract (English)
Intuitive teleoperation interfaces are crucial for the safe and effective operation of robotic manipulators in challenging environments. In the nuclear industry, surface contact tasks such as swab sampling require precise path and force tracking, obstacle avoidance, and sustained operator attention, which conventional joystick interfaces struggle to support effectively. This study designs and evaluates a novel touchscreen teleoperation interface that maps continuous finger movements directly to robotic manipulator motions, provides finer velocity control, and integrates control with visualization, enabling more natural, precise, and intuitive surface interaction than conventional controllers. A comparative user study with 20 participants evaluated task performance and workload using the proposed touchscreen, a conventional joystick, and a single-click autonomous mode. Tasks simulated realistic surface manipulation using a Franka Emika Panda arm, remotely controlled from another country. Kinematic, physiological, and behavioral data were recorded to comprehensively assess task performance, cognitive load, and operator trust across each control condition. Participants completed teleoperation tasks more efficiently and accurately with the touchscreen interface, achieving a 53.5% reduction in completion time (median: 2.50 vs. 5.38 min), higher in-area coverage on the sinusoidal path (90.7% vs. 84.1%), and lower overshoot on both path geometries compared with the joystick. Cognitive load, quantified via NASA-TLX (0-100), decreased from joystick to touchscreen (mean TLX 52 to 43; -9 points, -17.3%) and was lowest under the autonomous one-click mode (31; -21 points vs. joystick, -40.4%; -12 vs. touchscreen, -27.9%). This research presents an easy-to-implement touchscreen interface that improves performance in teleoperated surface tasks while reducing cognitive load.
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