RobotGraffiti: An AR tool for semi-automated construction of workcell models to optimize robot deployment
<|reference_start|>RobotGraffiti: An AR tool for semi-automated construction of workcell models to optimize robot deployment: Improving robot deployment is a central step towards speeding up robot-based automation in manufacturing. A main challenge in robot deployment is how to best place the robot within the workcell. To tackle this challenge, we combine two knowledge sources: robotic knowledge of the system and workcell context awareness of the user, and intersect them with an Augmented Reality interface. RobotGraffiti is a unique tool that empowers the user in robot deployment tasks. One simply takes a 3D scan of the workcell with their mobile device, adds contextual data points that otherwise would be difficult to infer from the system, and receives a robot base position that satisfies the automation task. The proposed approach is an alternative to expensive and time-consuming digital twins, with a fast and easy-to-use tool that focuses on selected workcell features needed to run the placement optimization algorithm. The main contributions of this paper are the novel user interface for robot base placement data collection and a study comparing the traditional offline simulation with our proposed method. We showcase the method with a robot base placement solution and obtain up to 16 times reduction in time.<|reference_end|>
@article{zieliński2024robotgraffiti:,
    title={RobotGraffiti: An AR tool for semi-automated construction of workcell
  models to optimize robot deployment},
    author={Krzysztof Zieli'nski, Ryan Penning, Bruce Blumberg, Christian
  Schlette, Mikkel Baun Kj{ae}rgaard},
    journal={arXiv preprint arXiv:2410.00484},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00484},
    primaryClass={cs.RO cs.HC}
}