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 language | English |
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Article number | 9682606 |
Pages (from-to) | 2063-2070 |
Number of pages | 8 |
Journal | IEEE Robotics and automation letters |
Volume | 7 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Apr 2022 |
Keywords
- Sensors
- Uncertainty
- Robot sensing systems
- Kalman filters
- Estimation
- Task analysis
- Measurement uncertainty
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