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Alex Tunchez

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Learning Model Predictive Control for Quadrotors

Feb 15, 2022
Guanrui Li, Alex Tunchez, Giuseppe Loianno

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Aerial robots can enhance their safe and agile navigation in complex and cluttered environments by efficiently exploiting the information collected during a given task. In this paper, we address the learning model predictive control problem for quadrotors. We design a learning receding--horizon nonlinear control strategy directly formulated on the system nonlinear manifold configuration space SO(3)xR^3. The proposed approach exploits past successful task iterations to improve the system performance over time while respecting system dynamics and actuator constraints. We further relax its computational complexity making it compatible with real-time quadrotor control requirements. We show the effectiveness of the proposed approach in learning a minimum time control task, respecting dynamics, actuators, and environment constraints. Several experiments in simulation and real-world set-up validate the proposed approach.

* This paper has been accepted to the 2022 IEEE International Conference on Robotics and Automation. Please cite this paper with the standard IEEE Conference format. Link to the Video: https://youtu.be/-5cIsIM5G7M 
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PCMPC: Perception-Constrained Model Predictive Control for Quadrotors with Suspended Loads using a Single Camera and IMU

Jul 22, 2021
Guanrui Li, Alex Tunchez, Giuseppe Loianno

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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 
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