ES-HPC-MPC: Exponentially Stable Hybrid Perception Constrained MPC for Quadrotor with Suspended Payloads

2025-04-10

AI Summary

In quadrotor aerial transportation with suspended payloads, cable slack–taut transitions induce hybrid dynamical instability and loss of visual observability. To address this, we propose a model predictive control (MPC) framework integrating an exponentially stabilizing control Lyapunov function (ES-CLF) and a perception-constrained control barrier function (CBF). This is the first work to jointly embed ES-CLF and CBF into the MPC optimization, ensuring simultaneous exponential stability and persistent payload observability within the onboard camera’s field of view under dynamic mode switches. A hybrid-system model precisely captures cable-state transitions. Both simulation and real-world experiments demonstrate that the method robustly handles abrupt slack–taut transitions under external disturbances, achieves high-precision trajectory tracking, maintains strict visual perception constraints throughout operation, and enables stable execution of dynamically infeasible trajectories.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Control quadrotors with suspended payloads under hybrid dynamics
• Ensure stability during slack-to-taut cable transitions
• Maintain payload visibility within camera field of view

Innovation

• Exponentially Stable Hybrid Perception Constrained MPC
• Uses ES-CLFs for stability and CBFs for FoV
• Validated in simulations and real-world experiments