Effective Human-Machine Interfaces for Aerial Telemanipulation
Garrett Nixon
- 发表年份
- 2015
- 引用次数
- 3
- 访问权限
- 开放获取
摘要
Most ground robots deployed in disaster areas have been of limited use mainly because of their reduced mobility in degraded environments. Unmanned Aerial Vehicles (UAVs) have considerable mobility benefits over ground robots, however they are currently only used for providing aerial views and often lack any manipulation capabilities, despite their proven ability to perform small force tasks. Research in ground robots has repeatedly stated that there is need for better human-machine interaction, while interface design for aerial robots is an unexplored area where generally primitive interfaces are used. The objective of this study is to understand what type of interface design is most beneficial for telemanipulation with a UAV. In a human factors experiment, subjects (N = 14) were instructed to open and flip a safety switch in a virtual reality environment using a bumper surrounding the UAV and to do so as fast and careful as possible. Five different interface designs were compared in this study. First, a 3 degrees-of-freedom (DoF) manipulator with position control was compared to the typical baseline interface: a gamepad with rate control. Second, the effect of having haptic feedback on the manipulator was tested. And finally performance on both the manipulator, with and without haptic feedback, was compared to having a fully actuated, grounded slave, with and without haptic feedback. Results show that a 3-DoF manipulator with position control substantially outperforms a gamepad with rate control. Haptic feedback had no significant influence on the task execution of the operator. Using a grounded slave was significantly more effective than using an aerial slave though there was no relative effect of haptic feedback between the grounded and the aerial slave. This study has shown that interface design can have a significant effect on performing remote manipulation tasks with UAVs.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002