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

TL;DR

This paper presents PCMPC, a real-time perception-constrained control and state estimation method for quadrotors with suspended loads using a single camera and IMU, ensuring payload visibility and accurate trajectory tracking.

Contribution

It introduces a novel control and estimation framework on the system manifold for quadrotors with suspended loads, integrating perception constraints directly into model predictive control.

Findings

Real-time operation at 500 Hz on limited hardware

Successful payload trajectory tracking at various speeds

Maintains payload visibility through camera FOV constraints

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.