Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs

Martin Jacquet, Max Kivits, Hemjyoti Das, Antonio Franchi

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
134 Downloads (Pure)

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.
Original languageEnglish
Article number9682606
Pages (from-to)2063-2070
Number of pages8
JournalIEEE Robotics and automation letters
Volume7
Issue number2
DOIs
Publication statusPublished - 1 Apr 2022

Keywords

  • Sensors
  • Uncertainty
  • Robot sensing systems
  • Kalman filters
  • Estimation
  • Task analysis
  • Measurement uncertainty
  • 2023 OA procedure

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