WIP Vehicle Control Method Based on Improved Artificial Potential Field Subject to Multi-obstacle Environment

This paper presents a control method for a WIP vehicle in multi-obstacle environment based on improved artificial potential field. Firstly, an improved artificial potential field (IAPF) is developed, where a safe distance is introduced to the existing repulsive potential field to solve the security issue, while the local minima can also be eliminated in the meantime. Next, an obstacle avoidance controller is designed based on the IAPF, where the nonholonomic constraint and underactuated characteristic of the WIP vehicle are fully considered, and the stability condition of the system is analyzed by means of the related control theory. Moreover, to further improve the control performance, a key parameter that play an important role in the controller is adjusted by taking advantage of fuzzy logic, and detailed analyses are given to demonstrate its necessity and effectiveness. Finally, considering a motion environment that contains dense obstacles, narrow corridor and an obstacle near the target, numerical simulations are conducted to validate the proposed method, whose results indicate that the method has a good performance to control the WIP vehicle in multi-obstacle environment.

[1]  Chun-Yi Su,et al.  Vision-Based Model Predictive Control for Steering of a Nonholonomic Mobile Robot , 2016, IEEE Transactions on Control Systems Technology.

[2]  Shuzhi Sam Ge,et al.  New potential functions for mobile robot path planning , 2000, IEEE Trans. Robotics Autom..

[3]  Jian Huang,et al.  Nonlinear Disturbance Observer-Based Dynamic Surface Control of Mobile Wheeled Inverted Pendulum , 2015, IEEE Transactions on Control Systems Technology.

[4]  Karl A. Stol,et al.  Review of modelling and control of two-wheeled robots , 2013, Annu. Rev. Control..

[5]  Ming Yue,et al.  RBFNN‐Based Identification and Compensation Mechanism for Disturbance‐Like Parametric Friction with Application to Tractor‐Trailer Vehicles , 2018, Asian Journal of Control.

[6]  Xiaofeng Liu,et al.  Control Design for Systems Operating in Complex Environments , 2019, Complex.

[7]  Xuebo Zhang,et al.  Multilevel Humanlike Motion Planning for Mobile Robots in Complex Indoor Environments , 2019, IEEE Transactions on Automation Science and Engineering.

[8]  Ravi N. Banavar,et al.  Symmetries in the wheeled inverted pendulum mechanism , 2016, ArXiv.

[9]  F. Khaber,et al.  Optimal fuzzy tracking control with obstacles avoidance for a mobile robot based on Takagi-Sugeno fuzzy model , 2018, Trans. Inst. Meas. Control.

[10]  Ning Sun,et al.  Trajectory planning-based control of underactuated wheeled inverted pendulum robots , 2018, Science China Information Sciences.

[11]  Kuo-Chu Chang,et al.  UAV Path Planning with Tangent-plus-Lyapunov Vector Field Guidance and Obstacle Avoidance , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Jian Huang,et al.  High-Order Disturbance-Observer-Based Sliding Mode Control for Mobile Wheeled Inverted Pendulum Systems , 2020, IEEE Transactions on Industrial Electronics.

[13]  Chenguang Yang,et al.  Neural-Adaptive Output Feedback Control of a Class of Transportation Vehicles Based on Wheeled Inverted Pendulum Models , 2012, IEEE Transactions on Control Systems Technology.

[14]  Sangtae Kim,et al.  Nonlinear Optimal Control Design for Underactuated Two-Wheeled Inverted Pendulum Mobile Platform , 2017, IEEE/ASME Transactions on Mechatronics.

[15]  Yan-Jun Liu,et al.  Adaptive Neural Network-Based Tracking Control for Full-State Constrained Wheeled Mobile Robotic System , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  Hüseyin Oktay Erkol Optimal PIλ Dμ Controller Design for Two Wheeled Inverted Pendulum , 2018, IEEE Access.

[17]  Songyang Lao,et al.  Collision Avoidance for Cooperative UAVs With Optimized Artificial Potential Field Algorithm , 2017, IEEE Access.

[18]  Tomislav Dragicevic,et al.  Fuzzy-Logic-Based Adaptive Proportional-Integral Sliding Mode Control for Active Suspension Vehicle Systems: Kalman Filtering Approach , 2019, Inf. Technol. Control..

[19]  Jian Huang,et al.  Modeling and Velocity Control for a Novel Narrow Vehicle Based on Mobile Wheeled Inverted Pendulum , 2013, IEEE Transactions on Control Systems Technology.

[20]  Xue Mei,et al.  Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment , 2019, Inf. Technol. Control..

[21]  Matin Macktoobian,et al.  Time-variant artificial potential field (TAPF): a breakthrough in power-optimized motion planning of autonomous space mobile robots , 2016, Robotica.

[22]  Lydia Tapia,et al.  Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field , 2017, IEEE Transactions on Robotics.

[23]  Martin Healey,et al.  Principles of automatic control , 1967 .

[24]  Masaki Takahashi,et al.  Dynamics-Based Nonlinear Acceleration Control With Energy Shaping for a Mobile Inverted Pendulum With a Slider Mechanism , 2016, IEEE Transactions on Control Systems Technology.

[25]  Rongxin Cui,et al.  Adaptive backstepping control of wheeled inverted pendulums models , 2015 .

[26]  Jinkun Liu,et al.  Boundary Control for a Flexible Inverted Pendulum System Based on a PDE Model with Input Saturation , 2018 .

[27]  Ming Yue,et al.  An Efficient Model Predictive Control for Trajectory Tracking of Wheeled Inverted Pendulum Vehicles with Various Physical Constraints , 2018 .

[28]  Hassan K. Khalil,et al.  Output feedback stabilization of inverted pendulum on a cart in the presence of uncertainties , 2015, Autom..

[29]  Ming Yue,et al.  Point Stabilization Control Method for WIP Vehicles Based on Motion Planning , 2019, IEEE Transactions on Industrial Informatics.

[30]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[31]  Jun Ye Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot , 2014, Intell. Serv. Robotics.

[32]  Stefan K. Gehrig,et al.  Collision Avoidance for Vehicle-Following Systems , 2007, IEEE Transactions on Intelligent Transportation Systems.

[33]  Seonghee Jeong,et al.  Wheeled inverted pendulum type assistant robot: design concept and mobile control , 2008, Intell. Serv. Robotics.

[34]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[35]  Jing Li,et al.  Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models , 2013, IEEE Transactions on Cybernetics.

[36]  Roberto Sepúlveda,et al.  Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles , 2015, Expert Syst. Appl..

[37]  Jinkun Liu,et al.  Boundary Control for A Flexible Inverted Pendulum System Based on A Pde Model , 2018 .

[38]  MH Mabrouk,et al.  Solving the potential field local minimum problem using internal agent states , 2008, Robotics Auton. Syst..

[39]  Ming Yue,et al.  A trajectory planning and tracking control approach for obstacle avoidance of wheeled inverted pendulum vehicles , 2018, Int. J. Control.

[40]  Petter Ögren,et al.  A convergent dynamic window approach to obstacle avoidance , 2005, IEEE Transactions on Robotics.