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

TL;DR

This work tackles the control and perception challenges of a quadrotor carrying a cable-suspended payload under hybrid slack-taut dynamics. It proposes ES-HPC-MPC, an on-board model predictive controller that enforces exponential stability via dynamically updated ES-CLFs and guarantees payload visibility with perception-based CBFs, across both taut and slack cable modes. The approach yields stable trajectory tracking and robust perception safety, validated through simulations and real-world experiments, including unexpected hybrid transitions and human-payload interactions. The key contributions are the hybrid-aware ES-CLF design for both the quadrotor and payload, the CBF-based perception constraint ensuring the payload remains within the onboard camera FoV, and comprehensive experimental validation demonstrating resilience to disturbances and mode transitions.

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.

Figures (6)

• Figure 1: Quadrotor with a cable-suspended load representation.
• Figure 2: The control block diagram of the proposed ES-HPC-MPC.
• Figure 3: Simulation results for a Lissajous trajectory with speeds up to $2.4\,\mathrm{m/s}$ under an external step force of $0.6\,\mathrm{N}$ applied for $0.5\,\mathrm{s}$. a: Top view of the simulation using ES-HPC-MPC (blue line) and baseline (orange line). b: ES-CLF values. c: Payload position errors. d: CBF values.
• Figure 4: Tracking results for a straight line trajectory with a maximum speed of up to $2.0\,\mathrm{m/s}$ under vertical external disturbances that generates slack-to-taut transitions. a: Snapshots showing the human-payload disturbances. b: ES-CLF values. c: Payload velocity and position errors. d: CBF values. e: Images of the payload observed by the onboard camera.
• Figure 5: Tracking results for dynamically infeasible trajectory a: Snapshots of the system movement. b: ES-CLF values. c: Payload position errors. d: Quadrotor angular velocity. e: CBF values. f: Images of the payload observed by the camera.