Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs
Martin Jacquet, Max Kivits, Hemjyoti Das, Antonio Franchi
Abstract
This letter introduces a cooperative control framework based on Nonlinear Model Predictive Control (NMPC) for solving an Active Information Acquisition problem (AIA) using a system of multiple multirotor UAVs equipped with onboard sensors. The observation task of the NMPC is a minimum-uncertainty pose estimation of a moving feature which is observed by the multi-UAV system, using a cooperative Kalman filter. The controller considers a full nonlinear model of the multirotors – including the motor-level actuation units and their real constraints in terms of maximum torque – and embeds the Kalman filter estimation uncertainty in its prediction. The framework allows and exploits heterogeneity in the actuation and sensing systems by considering a generic model of UAV – including both quadrotors and tilted-propeller multirotors – and a generic model of range-and-bearing sensor with arbitrary rate and field of view. The capability of the proposed framework to reduce the cooperative estimation uncertainty of a static or a moving feature, thus leading the system to optimal sensing configurations, is demonstrated through Gazebo simulations and real experiments. The software is provided open-source.
BibTeX
@inproceedings{ral2022_motorlevelnmpcfo,
title = {Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs},
author = {Martin Jacquet and Max Kivits and Hemjyoti Das and Antonio Franchi},
booktitle = {RA-L 2022},
year = {2022}
}