Selecting haptic guidance models in teleoperation: guidelines from a comparative user study
2026-06-02 • Robotics
Robotics
AI summaryⓘ
The authors studied how different haptic guidance methods help people control robots remotely by feeling forces through a device. They compared three main models—spring-damper, potential field, and guiding tube—in tasks like vertical farming with different environments. Their study found no one model works best everywhere: spring-damper helps most in crowded spaces, potential fields work well in open areas but can be risky near obstacles, and guiding tubes offer a good balance. They also created new ways to measure how comfortable and trustworthy these models feel based on the strength of the guiding forces.
haptic guidanceteleoperationforce feedbackspring-damper modelpotential fieldguiding tubestiffness-damping systemuser studyvertical farminginteraction metrics
Authors
Alexis Boulay, Margot Vulliez, David Daney
Abstract
Haptic guidance in teleoperation enhances operator performance through force feedback. This paper presents guidelines to select the most appropriate model considering the task, the environment and the operator. We define a unified formulation expressing most common models (spring-damper, potential field, and guiding tube) as variations of a stiffness-damping system with model-specific guiding functions. We conducted a user study comparing the three classical models across six scenarios with varying environmental conditions in a vertical farming task. Results show no universally superior model: spring-damper excels in cluttered environments, potential field in free spaces (but it shows risks near obstacles), and guiding tube offers a balanced compromise. We propose novel objective metrics to evaluate the interaction, and show that guiding force magnitude correlates with comfort and trust scores. These findings provide practical model selection guidelines through environmental characteristics and real-time evaluation metrics.