解决无人机吊挂物体时绳索松紧变化带来的控制与视觉难题
ES-HPC-MPC: Exponentially Stable Hybrid Perception Constrained MPC for Quadrotor with Suspended Payloads
- 用稳定函数和约束函数联合控制，确保飞行中绳索状态变化时仍稳定
- 实测可稳定跟踪原本无法完成的复杂轨迹，且吊挂物始终在摄像头视野内
- 适合需要高精度感知与鲁棒控制的救援、物流等实际场景
使用四轴无人机吊运物体在灾难响应、物流和基础设施维护中具有巨大潜力，但其混合且欠驱动的动力学特性带来了显著的控制与感知挑战。传统方法通常假设绳索始终拉紧，限制了在真实应用中的有效性，因为扰动会导致绳索从松弛到拉紧状态的转换。本文提出ES-HPC-MPC，一种模型预测控制框架，可在混合动力学下实现指数稳定并满足感知约束。该方法利用指数稳定控制李雅普诺夫函数（ES-CLFs）保证任务过程中的稳定性，同时使用控制屏障函数（CBFs）确保吊挂物始终在机载摄像头视场范围内。通过仿真和真实实验验证，该方法在追踪动态不可行轨迹及遭遇意外扰动引发的模式切换时，仍能保持系统稳定并满足感知约束。
原文摘要 · Abstract (English)
Aerial transportation using quadrotors with cable-suspended payloads holds great potential for applications in disaster response, logistics, and infrastructure maintenance. However, their hybrid and underactuated dynamics pose significant control and perception challenges. Traditional approaches often assume a taut cable condition, limiting their effectiveness in real-world applications where slack-to-taut transitions occur due to disturbances. We introduce ES-HPC-MPC, a model predictive control framework that enforces exponential stability and perception-constrained control under hybrid dynamics. Our method leverages Exponentially Stabilizing Control Lyapunov Functions (ES-CLFs) to enforce stability during the tasks and Control Barrier Functions (CBFs) to maintain the payload within the onboard camera's field of view (FoV). We validate our method through both simulation and real-world experiments, demonstrating stable trajectory tracking and reliable payload perception. We validate that our method maintains stability and satisfies perception constraints while tracking dynamically infeasible trajectories and when the system is subjected to hybrid mode transitions caused by unexpected disturbances.
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