MPC controlled multirotor with suspended slung Load: System architecture and visual load detection

There is an increased interest in the use of Unmanned Aerial Vehicles for load transportation from environmental remote sensing to construction and parcel delivery. One of the main challenges is accurate control of the load position and trajectory. This paper presents an assessment of real flight trials for the control of an autonomous multi-rotor with a suspended slung load using only visual feedback to determine the load position. This method uses an onboard camera to take advantage of a common visual marker detection algorithm to robustly detect the load location. The load position is calculated using an onboard processor, and transmitted over a wireless network to a ground station integrating MATLAB/SIMULINK and Robotic Operating System (ROS) and a Model Predictive Controller (MPC) to control both the load and the UAV. To evaluate the system performance, the position of the load determined by the visual detection system in real flight is compared with data received by a motion tracking system. The multi-rotor position tracking performance is also analyzed by conducting flight trials using perfect load position data and data obtained only from the visual system. Results show very accurate estimation of the load position (~5% Offset) using only the visual system and demonstrate that the need for an external motion tracking system is not needed for this task.

[1]  S. Notter,et al.  Modelling, Simulation and Flight Test of a Model Predictive Controlled Multirotor with Heavy Slung Load , 2016 .

[2]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[3]  Sergei Lupashin,et al.  A platform for aerial robotics research and demonstration: The Flying Machine Arena , 2014 .

[4]  Luis F. Gonzalez,et al.  Evolutionary Optimisation Methods with Uncertainty for Modern Multidisciplinary Design in Aeronautical Engineering , 2009 .

[5]  Aaron McFadyen,et al.  Multi-rotor with suspended load: System Dynamics and Control Toolbox , 2015, 2015 IEEE Aerospace Conference.

[6]  Frank Allgöwer,et al.  An Introduction to Nonlinear Model Predictive Control , 2002 .

[7]  Luis Felipe Gonzalez Robust evolutionary methods for multi-objective and multdisciplinary design optimisation in aeronautics , 2005 .

[8]  Rodney A. Walker,et al.  Development of an autonomous unmanned aerial system to collect time‐stamped samples from the atmosphere and localize potential pathogen sources , 2011, J. Field Robotics.

[10]  Peter I. Corke,et al.  Aircraft collision avoidance using spherical visual predictive control and single point features , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Peter Corke,et al.  Towards the development of a gas sensor system for monitoring pollutant gases in the low troposphere using small unmanned aerial vehicles , 2012 .

[12]  B. Bethke,et al.  Real-time indoor autonomous vehicle test environment , 2008, IEEE Control Systems.

[13]  Francisco José Madrid-Cuevas,et al.  Automatic generation and detection of highly reliable fiducial markers under occlusion , 2014, Pattern Recognit..