Trajectory and Morphology Planning for Morphing Quadrotors in Ultra-Narrow Passages using Limited Perception
Trajectory and Morphology Planning for Morphing Quadrotors in Ultra-Narrow Passages using Limited Perception
This research presents an online trajectory and morphology planning framework enabling morphing quadrotors to traverse passages narrower than their unfolded width using only a 2D LiDAR. The method introduces a novel cost reduction factor computed from LiDAR data to cancel obstacle repulsive costs along the medial axis of narrow passages, allowing autonomous navigation through tight gaps where classical potential field methods fail. Validated through Gazebo/PX4 simulations and hardware experiments on the MorphoCopter, the system successfully demonstrates folding and unfolding maneuvers in curved, inclined, and cluttered environments while maintaining safety constraints.
The paper proposes an online planning framework for morphing quadrotors that uses a novel cost reduction factor derived from 2D LiDAR data to enable passage through ultra-narrow gaps, validated via Gazebo/PX4 simulations and hardware experiments on the MorphoCopter platform.
Source: arXiv Robotics — research abstracts · Read original article ↗
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ased nonlinear MPC, which jointly plans the trajectory, yaw, and fold angle of the platform. A user-specified minimum-width limit ensures that the cost reduction factor only opens the corridors that the platform can actually pass. Gazebo/PX4 simulations and hardware experiments on the MorphoCopter demonstrate autonomous folding and unfolding through curved, inclined, and cluttered passages narrower than the unfolded
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width, where the standard repulsive cost fails to pass safely in most of the trials. The implementation code is available at \href{https://github.com/harshjmodi1996/morphocopter_mpc}{\textcolor{blue}{Github}} and a short video about the paper is available at \href{https://zh.engr.tamu.edu/wp-content/uploads/sites/310/2026/10/Morphocopter_planning_final.mp4}{\textcolor{blue}{\textbf{video link}}}.
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Source：arXiv Robotics — research abstracts · arxiv.org