Constrained MPC-Based Motion Planning for Morphing Quadrotors in Ultra-Narrow Passages under Limited Perception

2026-05-15

AI Summary

This work addresses the challenge of motion planning in ultra-narrow passages, where conventional obstacle avoidance methods often erroneously block feasible paths due to excessively high repulsive costs. The authors propose a motion planning framework based on nonlinear model predictive control (MPC) that jointly optimizes both the configuration and trajectory of a morphing quadrotor. The approach directly incorporates 2D LiDAR measurements to formulate environmental constraints and introduces a novel smooth exponential obstacle cost function that avoids hard-threshold activation, thereby substantially reducing traversal cost. This cost function is generalizable and applicable to various mobile robotic platforms. Experimental results implemented with acados demonstrate that the proposed method enables safe and efficient navigation through extremely narrow corridors where traditional approaches fail, achieving a favorable balance between computational efficiency and practical applicability.

Abstract

This paper introduces a motion planning framework to plan morphology and trajectory for morphing quadrotors under extremely constrained environments. We develop a novel obstacle avoidance cost function for nonlinear model predictive control (MPC) that enables navigation through extremely narrow gaps under limited perception from a 2D LiDAR. Classical artificial potential field-based costs typically have a high cost in narrow passages, artificially blocking the navigable path. In contrast, we propose a smooth exponential obstacle cost that preserves low traversal cost within narrow gaps while maintaining strong collision avoidance behavior. The formulation avoids hard activation thresholds and introduces a cost reduction factor to reduce the cost within narrow passages. Direct use of 2D LiDAR measurements in MPC allows navigation around arbitrarily shaped obstacles. The method is embedded within an acados-based nonlinear MPC framework. Simulation and experimental results demonstrate successful traversal of narrow corridors where typical repulsive cost functions would fail. The approach provides a computationally efficient and practical solution for navigating through tight spaces while maintaining safety from the obstacles. While we are implementing the framework on the morphing quadrotors, the cost function formulation is general-purpose for any mobile robot application, and is not limited to the morphing quadrotors. The implementation code is available at \href{https://github.com/harshjmodi1996/morphocopter_mpc}{Github Repo} and a short video is available at \href{https://zh.engr.tamu.edu/wp-content/uploads/sites/310/2026/03/MPC_MorphoCopter_video.mp4}{Video Link}.

Problem

• Constrained Environments
• Morphing Quadrotors
• Motion Planning
• Limited Perception
• Narrow Passages

Innovation

• constrained MPC
• morphing quadrotor
• obstacle avoidance cost
• narrow passage navigation
• 2D LiDAR perception