Obstacle avoidance for autonomous Ground Vehicles based on moving horizon optimization

Obstacle avoidance is a critical problem to solve for Unmanned Ground Vehicles (UGVs). The schemes of obstacle avoidance for UGVs are first discussed according to the structured environment road. The braking algorithm is discussed using moving horizon optimization based on differential flatness, where 3 DOF vehicle model is used to follow the planning trajectory for the obstacle avoidance of UGVs. In addition, the cruise control and changing lane control algorithm are given using 2 DOF vehicle model. The optimal problem of avoiding obstacles is formulated in terms of minimizing the bias of the lateral displacement considering the lateral acceleration as constraints. In order to verify the effectiveness of the proposed method, simulation under multi-vehicle environment is carried out.

[1]  Steven Dubowsky,et al.  Hazard avoidance for high‐speed mobile robots in rough terrain , 2006, J. Field Robotics.

[2]  Renato Zaccaria,et al.  Planning and obstacle avoidance in mobile robotics , 2012, Robotics Auton. Syst..

[3]  Junmin Wang,et al.  Autonomous ground vehicle control system for high-speed and safe operation , 2008, 2008 American Control Conference.

[4]  Chen Hong,et al.  Flatness-based vehicle online path following with time-varying constraints of dynamics , 2011, 2011 Chinese Control and Decision Conference (CCDC).

[5]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[6]  Andrey V. Savkin,et al.  A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance , 2011, Autom..

[7]  Hong Chen,et al.  Stability control of vehicle with tire blowout using differential flatness based MPC method , 2012, Proceedings of the 10th World Congress on Intelligent Control and Automation.

[8]  Francesco Borrelli,et al.  Predictive Active Steering Control for Autonomous Vehicle Systems , 2007, IEEE Transactions on Control Systems Technology.

[9]  H. ChenT,et al.  A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability * , 1998 .

[10]  Fei Wang,et al.  Implementation of EKF for Vehicle Velocities Estimation on FPGA , 2013, IEEE Transactions on Industrial Electronics.

[11]  S. Shankar Sastry,et al.  Model-predictive active steering and obstacle avoidance for autonomous ground vehicles , 2009 .

[12]  George J. Pappas,et al.  Multi-UAV Cooperative Surveillance with Spatio-Temporal Specifications , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[13]  P. Falcone,et al.  A hierarchical Model Predictive Control framework for autonomous ground vehicles , 2008, 2008 American Control Conference.

[14]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[15]  Hong Chen,et al.  Field programmable gate array/system on a programmable chip-based implementation of model predictive controller , 2012 .

[16]  Roland Siegwart,et al.  Real-time obstacle avoidance for polygonal robots with a reduced dynamic window , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[17]  Frank Allgöwer,et al.  A computationally attractive nonlinear predictive control scheme with guaranteed stability for stable systems , 1998 .

[18]  Tor Arne Johansen,et al.  Vehicle velocity estimation using nonlinear observers , 2006, Autom..

[19]  Oliver Sawodny,et al.  Motion planning for an autonomous vehicle driving on motorways by using flatness properties , 2010, 2010 IEEE International Conference on Control Applications.

[20]  Francesco Borrelli,et al.  MPC-Based Approach to Active Steering for Autonomous Vehicle Systems , 2005 .