← 研踪 PaperTrailarXiv:2611.11830 · v1
ROS2MR: Connecting ROS 2 Motion Planning to Mixed-Reality Trajectory Visualization
首次提交 2026-11-09 11:48
版本更新 2026-11-09 11:48
雷达观测 2026-11-10 13:18
摘要
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.
作者备注：Pre-print submitted in Elsevier
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v1 · 2026-11-09 11:48 · 最新观测版本
ROS2MR: Connecting ROS 2 Motion Planning to Mixed-Reality Trajectory Visualization
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.
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