Three-Dimensional Dynamic-Model-Aided Navigation of Multirotor Unmanned Aerial Vehicles

This paper presents a dynamic-model-aided navigation (DMAN) method for a small multirotor unmanned aerial vehicle. The method can be used for temporary navigation in cases where location and velocity measurements from external sources, e.g., global navigation satellite system, are missing or unreliable. The method combines proprioceptive measurements with a Kalman filter through a dynamic model to obtain the velocity and location of the vehicle. Acceleration and angular rate measurements from an inertial measurement unit, altitude measurements from a barometric altimeter, and proprioceptive measurements of the revolution speed of propellers are considered in the method. The dynamic model of the aerial vehicle relates the linear and angular velocities of the vehicle with the revolution speed of the propeller. The revolution speed is first converted into a thrust force and torque and then included in the model. The model avoids the singularity problem and describes processes and measurements in a three-dimensional space by representing attitude using quaternions instead of Euler angles. This study details two implementations of the DMAN method: extended Kalman filter (EKF) and unscented Kalman filter (UKF). The dynamic model is incorporated into the process model and measurement model of the implementations. A model that converts the revolution speed of propellers to thrust force and torque has been derived from unmanned aerial vehicle flight experiments. Experiments that implement the proposed method for quadrotor navigation verify the performance and state the limitations of the DMAN method. Compared with previous methods, the proposed method extends the application of DMAN to the three-dimensional space and obtains location and velocity measurements in a world coordinate system.

[1]  Tor Arne Johansen,et al.  A Nonlinear Model-Based Wind Velocity Observer for Unmanned Aerial Vehicles , 2016 .

[2]  Peter I. Corke,et al.  Multirotor Aerial Vehicles: Modeling, Estimation, and Control of Quadrotor , 2012, IEEE Robotics & Automation Magazine.

[3]  J. Skaloud,et al.  AUTONOMOUS NAVIGATION OF SMALL UAVS BASED ON VEHICLE DYNAMIC MODEL , 2016 .

[4]  K. Nonami,et al.  Integrated navigation of aerial robot for GPS and GPS-denied environment , 2016 .

[5]  Giancarlo Troni,et al.  Preliminary experimental evaluation of a Doppler-aided attitude estimator for improved Doppler navigation of underwater vehicles , 2013, 2013 IEEE International Conference on Robotics and Automation.

[6]  Philippe Martin,et al.  The role of propeller aerodynamics in the model of a quadrotor UAV , 2009, 2009 European Control Conference (ECC).

[7]  Joan Solà,et al.  Quaternion kinematics for the error-state Kalman filter , 2015, ArXiv.

[8]  Hadi Nobahari,et al.  Application of model aided inertial navigation for precise altimetry of Unmanned Aerial Vehicles in ground proximity , 2017 .

[9]  Alexandre Loeblen Heinen,et al.  Implementation of a fixed-wing UAV autopilot in Snapdragon Flight board , 2017 .

[10]  Jianye Liu,et al.  A novel integrated navigation system based on the quadrotor dynamic model , 2018, 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[11]  Gamini Dissanayake,et al.  Improved State Estimation in Quadrotor MAVs: A Novel Drift-Free Velocity Estimator , 2015, IEEE Robotics & Automation Magazine.

[12]  In Ho Choi,et al.  Features of Invariant Extended Kalman Filter Applied to Unmanned Aerial Vehicle Navigation , 2018, Sensors.

[13]  S. Kennedy,et al.  Performance of a deeply coupled commercial grade GPS/INS system from KVH and NovAtel Inc. , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[14]  Y. Oshman,et al.  Averaging Quaternions , 2007 .

[15]  Jan Skaloud,et al.  Assessment of VDM-based autonomous navigation of a UAV under operational conditions , 2018, Robotics Auton. Syst..

[16]  Robert E. Mahony,et al.  Nonlinear Complementary Filters on the Special Orthogonal Group , 2008, IEEE Transactions on Automatic Control.

[17]  Tae Suk Yoo,et al.  Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System , 2011, Sensors.

[18]  Philippe Martin,et al.  The true role of accelerometer feedback in quadrotor control , 2010, 2010 IEEE International Conference on Robotics and Automation.

[19]  Timothy W. McLain,et al.  Quadrotors and Accelerometers: State Estimation with an Improved Dynamic Model , 2014, IEEE Control Systems.

[20]  Marc Pollefeys,et al.  PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Danping Zou,et al.  An aerodynamic model-aided state estimator for multi-rotor UAVs , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[22]  Jan Skaloud,et al.  Autonomous Vehicle Dynamic Model-Based Navigation for Small UAVs , 2016 .

[23]  E. Kraft,et al.  A quaternion-based unscented Kalman filter for orientation tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[24]  Sven Behnke,et al.  Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments , 2016, J. Intell. Robotic Syst..

[25]  I-Ming Chen,et al.  Autonomous navigation of UAV by using real-time model-based reinforcement learning , 2016, 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV).

[26]  Jan Skaloud,et al.  VDM-based UAV Attitude Determination in Absence of IMU Data , 2018, 2018 European Navigation Conference (ENC).

[27]  Sandy Kennedy,et al.  GPS/INS Integration in Real-time and Post- processing with NovAtel's SPAN System , 2007 .

[28]  Michael Kaess,et al.  Long-range GPS-denied aerial inertial navigation with LIDAR localization , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[29]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[30]  Shaojie Shen,et al.  Model-Based Global Localization for Aerial Robots Using Edge Alignment , 2017, IEEE Robotics and Automation Letters.

[31]  Jianye Liu,et al.  A Thrust Model Aided Fault Diagnosis Method for the Altitude Estimation of a Quadrotor , 2018, IEEE Transactions on Aerospace and Electronic Systems.