Unscented Kalman filter state estimation for manipulating unmanned aerial vehicles

Abstract Manipulating unmanned aerial vehicles (MUAVs) are aerial robots equipped with a mechanism to physically interact with the environment. State estimation of such robots is a challenging problem due to inherent couplings, nonlinearities and uncertainties of MUAV complex dynamics and, therefore, popular algorithms such as extended Kalman filter may not be applicable. With the above considerations, this paper formulates two variants of the unscented Kalman filter using (i) general and (ii) spherical unscented transform to address state estimation problem in MUAVs. In order to examine the effect of estimation quality on overall control performance, first the coupled dynamics of a quadcopter endowed with a robotic manipulator is presented. Next, a linear–quadratic–Gaussian (LQG) control is designed to achieve simultaneous control of the quadcopter and its manipulator. Then, the performance of each unscented Kalman filter algorithm is compared with that of extended Kalman filter in the context of estimation accuracy, overall control performance, and algorithm execution time. Additionally, sensitivity of the proposed approaches to increasing noise levels and total loss of sensory data are examined.

[1]  Chris Rizos,et al.  Sigma-Point Kalman Filtering for Tightly Coupled GPS/INS Integration , 2008 .

[2]  Robert Mahony,et al.  Modelling and control of a large quadrotor robot , 2010 .

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

[4]  G. Oriolo,et al.  Robotics: Modelling, Planning and Control , 2008 .

[5]  Miguel A. Olivares-Méndez,et al.  Adaptive control of Aerial Manipulation Vehicle , 2014, 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2014).

[6]  Farrokh Janabi-Sharifi,et al.  Aerial manipulation - A literature survey , 2018, Robotics Auton. Syst..

[7]  Jianwei Wan,et al.  Iterated Unscented Kalman Filter for Passive Target Tracking , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[8]  James F. Whidborne,et al.  A prototype of an autonomous controller for a quadrotor UAV , 2007, 2007 European Control Conference (ECC).

[9]  Eun-Hwan Shin,et al.  An unscented Kalman filter for in-motion alignment of low-cost IMUs , 2004, PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556).

[10]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[11]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[12]  Rogerio A. Enríquez-Caldera,et al.  LQR control for a quadrotor using unit quaternions: Modeling and simulation , 2013, CONIELECOMP 2013, 23rd International Conference on Electronics, Communications and Computing.

[13]  Felipe Espinosa,et al.  UAV Attitude Estimation Using Unscented Kalman Filter and TRIAD , 2012, IEEE Transactions on Industrial Electronics.

[14]  Liang Liu,et al.  The design and application of SmartCopter: An unmanned helicopter based robot for transmission line inspection , 2013, 2013 Chinese Automation Congress.

[15]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[16]  Simon J. Julier,et al.  The spherical simplex unscented transformation , 2003, Proceedings of the 2003 American Control Conference, 2003..

[17]  Costanzo Manes,et al.  Comparative Study of Unscented Kalman Filter and Extended Kalman Filter for Position/Attitude Estimation in Unmanned Aerial Vehicles , 2008 .

[18]  Vijay Kumar,et al.  Design, modeling, estimation and control for aerial grasping and manipulation , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[20]  Donald E. Kirk,et al.  Optimal control theory : an introduction , 1970 .

[21]  Alexandra Moutinho,et al.  Attitude Estimation in SO(3): A Comparative UAV Case Study , 2015, J. Intell. Robotic Syst..

[22]  Zheng You,et al.  Inertial/celestial-based fuzzy adaptive unscented Kalman filter with Covariance Intersection algorithm for satellite attitude determination , 2016 .

[23]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[24]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[25]  Aníbal Ollero,et al.  Control of an aerial robot with multi-link arm for assembly tasks , 2013, 2013 IEEE International Conference on Robotics and Automation.

[26]  Vaclav Smidl,et al.  Challenges and limits of extended Kalman Filter based sensorless control of permanent magnet synchronous machine drives , 2009, 2009 13th European Conference on Power Electronics and Applications.

[27]  R. Capua,et al.  Implementation of the Unscented Kalman Filter and a Simple Augmentation System for GNSS SDR Receivers , 2012 .

[28]  Haider A. F. Almurib,et al.  Control and path planning of quadrotor aerial vehicles for search and rescue , 2011, SICE Annual Conference 2011.

[29]  Vijay Kumar,et al.  Cooperative manipulation and transportation with aerial robots , 2009, Auton. Robots.

[30]  Timothy W. McLain,et al.  Cooperative forest fire surveillance using a team of small unmanned air vehicles , 2006, Int. J. Syst. Sci..

[31]  Jinquan Huang,et al.  An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring , 2016 .

[32]  Shesheng Gao,et al.  Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter , 2018 .

[33]  Vijay Kumar,et al.  Toward image based visual servoing for aerial grasping and perching , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[34]  Halil Ersin Soken,et al.  UKF-Based Reconfigurable Attitude Parameters Estimation and Magnetometer Calibration , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[35]  Z. Peroutka.  Design considerations for sensorless control of PMSM drive based on extended Kalman filter , 2005, 2005 European Conference on Power Electronics and Applications.

[36]  R. V. Jategaonkar,et al.  Aerodynamic Parameter Estimation from Flight Data Applying Extended and Unscented Kalman Filter , 2010 .

[37]  Yuqing He,et al.  Rotor-flying manipulator: Modeling, analysis, and control , 2014 .

[38]  Vincenzo Lippiello,et al.  Hybrid Visual Servoing With Hierarchical Task Composition for Aerial Manipulation , 2016, IEEE Robotics and Automation Letters.

[39]  Jeffrey K. Uhlmann,et al.  Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[40]  Timothy W. McLain,et al.  Decentralized Cooperative Aerial Surveillance Using Fixed-Wing Miniature UAVs , 2006, Proceedings of the IEEE.

[41]  Jie Wu,et al.  In-flight initial alignment for small UAV MEMS-based navigation via adaptive unscented Kalman filtering approach , 2017 .

[42]  Weiliang Xu,et al.  Initial flight experiments of a canopy sampling aerial manipulator , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[43]  Aníbal Ollero,et al.  Control of a multirotor outdoor aerial manipulator , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[44]  Farrokh Janabi-Sharifi,et al.  UKF-Based LQR Control of a Manipulating Unmanned Aerial Vehicle , 2017, Unmanned Syst..

[45]  Yihua Liao,et al.  Attitude measure system based on extended Kalman filter for multi-rotors , 2017, Comput. Electron. Agric..

[46]  Bjarne A. Foss,et al.  Constrained nonlinear state estimation based on the UKF approach , 2009, Comput. Chem. Eng..

[47]  Jan Wendel,et al.  A Performance Comparison of Tightly Coupled GPS/INS Navigation Systems based on Extended and Sigma Point Kalman Filters , 2005 .

[48]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[49]  Cristian Lascu,et al.  State Estimation of Induction Motor Drives Using the Unscented Kalman Filter , 2012, IEEE Transactions on Industrial Electronics.

[50]  Matko Orsag,et al.  Stability control in aerial manipulation , 2013, 2013 American Control Conference.

[51]  Dongjun Lee,et al.  Dynamics and control of quadrotor with robotic manipulator , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[52]  Stjepan Bogdan,et al.  Hybrid Adaptive Control for Aerial Manipulation , 2013, Journal of Intelligent & Robotic Systems.

[53]  Vijay Kumar,et al.  Construction of Cubic Structures with Quadrotor Teams , 2011, Robotics: Science and Systems.

[54]  Vincenzo Lippiello,et al.  Cartesian Impedance Control of a UAV with a Robotic Arm , 2012, SyRoCo.

[55]  Marin Kobilarov,et al.  Towards model-predictive control for aerial pick-and-place , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).