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

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.

This paper has been published at the 2021 IEEE International Conference on Robotics and Automation. Please cite this paper with the standard IEEE Conference format. We will provide the detail of the DOI information later once the Proceedings has come out

Cited by in corpus (9)

• Autonomous Aerial Delivery Vehicles, a Survey of Techniques on how Aerial Package Delivery is Achieved
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking
• PANTHER: Perception-Aware Trajectory Planner in Dynamic Environments
• AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation
• Reconfigurable Drone System for Transportation of Parcels With Variable Mass and Size
• Robust Active Visual Perching with Quadrotors on Inclined Surfaces
• RotorTM: A Flexible Simulator for Aerial Transportation and Manipulation
• Learning Model Predictive Control for Quadrotors
• One-shot Learning for Autonomous Aerial Manipulation