ROS 2の動作計画を複合現実で可視化するROS2MR
ROS2MR: Connecting ROS 2 Motion Planning to Mixed-Reality Trajectory Visualization
ROS 2の関節軌道（MoveItの計画含む）をMRアプリ用JSONに変換し、Apple Vision ProやMeta Quest 3で軌道を可視化できるようにした。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Santiago Berrezueta-Guzman, Habiba-Loai Khalil, Andrei Koshelev, Stefan Wagner
分類: cs.HC, cs.RO
原文アブストラクト
Mixed reality (MR) allows users to inspect planned robot motion within their physical surroundings, but displaying trajectories from robotics software requires converting their joint configurations and timing into a format the MR application can play back. This paper presents ROS2MR, an extension of the MR-Robotics LAB platform that converts YAML-serialized ROS 2 joint trajectories, including MoveIt motion plans, into the platform's existing JSON playback format. The converter validates the input, maps arm joints by name, and resamples the trajectory at a nominal rate of 30 Hz, using cubic Hermite interpolation when joint velocities are supplied and linear interpolation otherwise, while binary gripper commands are sampled separately using a zero-order hold. A Python export helper combines sequences of motion plans and gripper states into a single trajectory and checks positional agreement at segment boundaries. The MR application was also adapted for Apple Vision Pro in addition to Meta Quest 3. A technical evaluation showed that the converter preserved the normalized duration and endpoint configurations for dense, sparse, and irregularly timed inputs. With supplied velocities, the position RMSE was approximately 2.91x10^-5 degrees for all sampling patterns; without them, sparse input increased it to 0.85 degrees, and close positional agreement did not guarantee accurate velocity profiles. A pick-and-place sequence retained its open--closed--open gripper order, although off-grid transitions were shifted to the output sampling grid. The resulting file-based pathway enables inspection of ROS-generated trajectories in MR without a live ROS connection.