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research article

Arm-wrist haptic sleeve for drone teleoperation

Ramachandran, Vivek  
•
Macchini, Matteo  
•
Floreano, Dario  
2022
IEEE Robotics and Automation Letters

Teleoperators rely on both visual and haptic feedback to perform drone teleoperation tasks, such as obstacle avoidance. Haptic feedback becomes essential when visual feedback is compromised, either due to visual occlusions or poor depth perception. However, haptic interfaces, are often bulky because they require heavy actuators to provide force feedback. The bulkiness reduces user mobility and makes these interfaces unsuitable for prolonged use. Here, we propose a wearable haptic sleeve that encompasses the wrist and elbow joints of a human arm. The two joint rotations control the motion of a drone in a simulated environment along a horizontal plane. The sleeve is composed of modular electroadhesive clutches that block the joint movement when the drone is in the vicinity of an obstacle. The clutches are lightweight (27 g), require low power (~1 mW) to operate, and can be mounted on the user without affecting the user's mobility. A motor learning subject study is conducted to navigate a drone through a hole in a wall where the depth perception of visual feedback is compromised. The results of the study show that subjects trained with the wearable haptic sleeve learnt the drone obstacle avoidance task and retained the necessary motor skills after haptic training, compared to subjects who received only visual feedback and were unable to learn the motor task.

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Type
research article
DOI
10.1109/LRA.2021.3122107
Author(s)
Ramachandran, Vivek  
Macchini, Matteo  
Floreano, Dario  
Date Issued

2022

Published in
IEEE Robotics and Automation Letters
Volume

7

Issue

4

Start page

12054

End page

12061

Subjects

Haptic interfaces

•

Drones Visualization

•

Elbow

•

Wrist

•

Task analysis

•

Wearable haptics

•

Textile devices

•

Human-robot interfaces

•

drone teleoperation

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIS  
FunderGrant Number

H2020

871479

Available on Infoscience
September 28, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/191103
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