Design and Evaluation of a Touchscreen-Based Teleoperation Interface for Robotic Manipulators

2026-08-06

AI Summary

This study addresses the limitations of conventional joystick-based teleoperation in scenarios such as nuclear industry applications, where precise path tracking, force control, and obstacle avoidance during surface contact tasks are critical, yet impose high cognitive load on operators. To overcome these challenges, this work proposes a touchscreen-based teleoperation interface that directly maps continuous finger motion to the Franka Emika Panda robotic arm, integrating control and visualization for more intuitive motion mapping and fine-grained velocity modulation. User studies demonstrate that, compared to both joystick and one-button autonomous modes, the proposed method reduces median task completion time by 53.5% (2.50 vs. 5.38 minutes), achieves a 90.7% coverage rate on sinusoidal paths with lower overshoot, and decreases NASA-TLX cognitive workload scores by 17.3%, significantly enhancing operational efficiency and naturalness.

Abstract

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.

Problem

• teleoperation
• robotic manipulators
• surface contact tasks
• cognitive load
• human-robot interaction

Innovation

• touchscreen teleoperation
• robotic manipulator
• surface interaction
• cognitive load reduction
• intuitive interface