We present V.Ra, a visual and spatial programming system for robot-IoT task authoring. In V.Ra, programmable mobile robots serve as binding agents to link the stationary IoTs and perform collaborative tasks. We establish an ecosystem that coherently connects the three key elements of robot task planning , the human, robot and IoT, with one single mobile AR device. Users can perform task authoring with the Augmented Reality (AR) handheld interface, then placing the AR device onto the mobile robot directly transfers the task plan in a what-you-do-is-what-robot-does (WYDWRD) manner. The mobile device mediates the interactions between the user, robot, and the IoT oriented tasks, and guides the path planning execution with the embedded simultaneous localization and mapping (SLAM) capability. We demonstrate that V.Ra enables instant, robust and intuitive room-scale navigatory and interactive task authoring through various use cases and preliminary studies.
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V.Ra: An In-Situ Visual Authoring System for Robot-IoT Task Planning with Augmented Reality
We present V.Ra, a visual and spatial programming system for robot-IoT task authoring. In V.Ra, programmable mobile robots serve as binding agents to link the stationary IoTs and perform collaborative tasks. We establish an ecosystem that coherently connects the three key elements of robot task planning (human-robot-IoT) with one single AR-SLAM device. Users can perform task authoring in an analogous manner with the Augmented Reality (AR) interface. Then placing the device onto the mobile robot directly transfers the task plan in a what-you-do-is-what-robot-does (WYDWRD) manner. The mobile device mediates the interactions between the user, robot and IoT oriented tasks, and guides the path planning execution with the SLAM capability.
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- Award ID(s):
- 1839971
- PAR ID:
- 10128236
- Date Published:
- Journal Name:
- Conference on Human Factors in Computing Systems - Proceedings, 2 May 2019
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3290607.3312797
