PCMPC: Perception-Constrained Model Predictive Control for Quadrotors with Suspended Loads using a Single Camera and IMU

TL;DR

This paper tackles perception-constrained control for a quadrotor carrying a cable-suspended payload using a single camera and an IMU. It formulates a nonlinear PCMPC on the configuration space $SE(3)\times S^2$, enforcing actuator limits and keeping the payload within the camera's Field Of View while respecting full system dynamics. State estimation combines on-board Visual Inertial Odometry with EKF-based cable-state estimation to recover the payload position, cable direction $\bm{\xi}$, and their derivatives in 3D, using only camera measurements, IMU data, and motor speeds. The approach runs on-board at $500$ Hz and is validated experimentally in an indoor setting with payload speeds up to about $4$ m/s, demonstrating robust trajectory tracking and accurate perception-aware control.

Abstract

In this paper, we address the Perception--Constrained Model Predictive Control (PCMPC) and state estimation problems for quadrotors with cable suspended payloads using a single camera and Inertial Measurement Unit (IMU). We design a receding--horizon control strategy for cable suspended payloads directly formulated on the system manifold configuration space SE(3)xS^2. The approach considers the system dynamics, actuator limits and the camera's Field Of View (FOV) constraint to guarantee the payload's visibility during motion. The monocular camera, IMU, and vehicle's motor speeds are combined to provide estimation of the vehicle's states in 3D space, the payload's states, the cable's direction and velocity. The proposed control and state estimation solution runs in real-time at 500 Hz on a small quadrotor equipped with a limited computational unit. The approach is validated through experimental results considering a cable suspended payload trajectory tracking problem at different speeds.

Figures (5)

• Figure 1: The quadrotor carrying a cable--suspended payload using PCMPC with monocular vision and IMU.
• Figure 2: System architecture.
• Figure 3: System convention with $\mathcal{I}$, $\mathcal{C}$, and $\mathcal{B}$ denoting inertial, camera, and body frames, respectively.
• Figure 4: Tracking results of the payload following a circle trajectory, (a) Cartesian position, (b) velocity, and (c) 3D path.
• Figure 5: Estimation results of the cable direction and velocity.