Suat KARAKAYA, Gürkan KÜÇÜKYILDIZ, Hasan OCAK

POINT STABILIZATION TECHNIQUE USING MODEL PREDICTIVE CONTROL AND EXACT EUCLIDIAN DISTANCE TRANSFORM METHODS

This paper presents a point stabilization method which is a hybrid form of Exact Euclidian Distance Transform (EEDT) and Model Predictive Control (MPC) theory. An optimal control problem is evaluated using a wheeled mobile robot’s mathematical model. The model is represented in polar coordinates considering non-holonomic constraints. The optimal input parameters are obtained by solving a sequential quadratic cost function. The EEDT algorithm provides via-points starting from the initial position to the desired final coordinates. The line segments connected by these points form the shortest obstacle free static trajectory. Assuming that all of the occupied regions in the map are fully known, states of the mobile robot are constrained by avoiding the intersection of the obstacles and the planned path. Point stabilization phase is performed in a partial manner by applying MPC algorithm on consecutive via points. The overall stabilized trajectory between the start and goal is obtained by connecting the stabilized sub-trajectories. This approach provides a way to stabilize a trajectory without determining convex sub-regions on a non-convex map. The results on a simulated differentially driven mobile robot prove that the proposed algorithm gives satisfactory results for point stabilization problem in presence of static obstacles on a non-convex map.

Keywords:

Model predictive control point stabilization, static path planning, mobile robot,

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[1] Brockett R.W., (1982) New directions in applied mathematics, Springer-Verlag, New York.
[2] Lages W.F., Hemerly E.F. (1998) Smooth time-invariant control of wheeled mobile robots, Proc. Int. Congress on Systems Science, 1-8.
[3] Oya M., Yu C.H., Katoh R., (2003) Robust adaptive motion force tracking control of uncertain non-holonomic mechanical systems, IEEE Trans. Robotics and Automation, 19, 175-181.
[4] Dixon W.E., Queiroz M.S., Dawson D.M., Flynn T.J., (2004) Adaptive tracking and regulation of a wheeled mobile robot with controller update law modularity, IEEE Trans. Control Systems Technology, 12, 138-147.
[5] Kühne F., Lages W.F., Silva J.M.G., (2005) Point stabilization of mobile robots with nonlinear model predictive control, Proc. of the IEEE International Conference on Mechatronics and Automation, 1163-1168.
[6] Alves J.AV., Lages W.F., (2007) Real-time point stabilization of a mobile robot using model predictive control, Proc. IASTED International Conference of Robotics and Applications, 58-63.
[7] Hsieh C.H., Liu J.S., (2012) Nonlinear model predictive control for wheeled mobile robot in dynamic environment, Proc. International Conference on Advanced Intelligent Mechatronics, 363-368.
[8] Sun S., Cui P., (2004) Path tracking and a practical point stabilization of mobile robot, Journal of Robotics and Computer-Integrated Manufacturing, 20, 29-34.
[9] Shokraneh K.M., Masehian E., (2016) Planning robot navigation among movable obstacles (NAMO) through a recursive approach, Journal of Intelligent & Robotic Systems, doi: 10.1007/s10846-016-0344-1.
[10] Zhengcai C., Yingtao Z., Qidi W., (2011) Genetic fuzzy + PI path tracking control of a non-holonomic mobile robot, Chinese Journal of Electronics, 20, 31-34.
[11] Rongbing L., Jianye L., Zhang, L., Hang Y. (2014) LIDAR/MEMS IMU integrated navigation (SLAM) method for a small UAV in indoor environments, Proc. IEEE Inertial Sensors and Systems Symposium (ISS), 1-15.
[12] Wu Z., Feng L., (2012) Obstacle prediction–based dynamic path planning for a mobile robot, International Journal of Advancements in Computing Technology, 4(1), 118-124.
[13] Tzafestas S.G., Deliparaschos K.M., Moustris G.P. (2010) Fuzzy logic path tracking control for autonomous non-holonomic mobile robots: Design of system on a chip, Robotics and Autonomous Systems, 58, 1017-1027.
[14] Carlos E.J., Federico P.E., Gabriel R.J., (2013) The exact Euclidian distance transform: a new algorithm for universal path planning, International Journal of Advanced Robotic Systems, 10, 1-10.
[15] Jarvis R.A., (1985) Collision free trajectory planning using distance transform, Proc. National Conference and Exhibition on Robotics, 187-191.
[16] Essen H. V., Nijmeijer H., (2001) Nonlinear model predictive control of constrained mobile robots. Proc. European Control Conference, 1157–1162.
[17] Mayne D.Q., Rawlings J. B., Rao C.V., Scokaert P.O.M. (2000) Constrained model predictive control: stability and optimality, Automatica, 36(6), 789-814.
[18] Ollero A., Amidi O. (1991) Predictive path tracking of mobile robots: application to the CMU Navlab. Proc. IEEE International Conference on Advanced Robotics, 1081–1086.
[19] Normey-Rico J. E., Gomez-Ortega J., Camacho E. F. (1999) A smith-predictor-based generalised predictive controller for mobile robot path-tracking, Control Engineering Practice, 7(6), 729–740.
[20] Karakaya S., Ocak H., Kucukyildiz G. (2017) A New Mobile Robot Toolbox for Matlab, Journal of Intelligent & Robotic Systems, DOI: 10.1007/s10846-017-0480-2.

Sigma Mühendislik ve Fen Bilimleri Dergisi-Cover

ISSN: 1304-7191
Yayın Aralığı: Yılda 4 Sayı
Yayıncı: Yıldız Teknik Üniversitesi

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