Neural-network-based docking of autonomous vehicles

In this paper, a neural-network-based guidance methodology is proposed for the docking of autonomous vehicles. The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation). In such instances, a guidance technique that utilizes line-of-sight based task-space sensory feedback is needed to minimize the impact of accumulated systematic motion errors. Herein, the proposed neural-network (NN) based guidance methodology is implemented during the final stage of the vehicle's motion (i.e., docking). The systematic motion errors of the vehicle are reduced iteratively by executing the corrective motion commands, generated by the NN, until the vehicle achieves its desired pose within random noise limits. The guidance methodology developed was successfully tested via simulations and experiments for a 3-dof high-precision planar platform

[1]  Hideki Hashimoto,et al.  Nonlinear filter road vehicle model development , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[2]  Huei-Yung Lin,et al.  A Visual Positioning System for Vehicle or Mobile Robot Navigation , 2006, IEICE Trans. Inf. Syst..

[3]  L. Wang,et al.  Automatic guidance of a vehicle based on DGPS and a 3D map , 2000, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493).

[4]  A. Kampfer,et al.  Principles of systems that enable autonomous driving of vehicles , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[5]  Toshiaki Kakinami,et al.  Evaluation of a Vision-Based Parking Assistance System , 2005 .

[6]  Ming-Liang Wang,et al.  A Visual Positioning System for Vehicle Navigation , 2005 .

[7]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[8]  A. B. Rad,et al.  Truck backing up neural network controller optimized by genetic algorithms , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[9]  Beno Benhabib,et al.  A guidance-based motion-planning methodology for the docking of autonomous vehicles , 2005, J. Field Robotics.

[10]  S.E. Shladover,et al.  Evaluation of lane-assist systems for urban transit operations , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[11]  L. Acar,et al.  Neural network based control for a backward maneuvering trailer truck , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[12]  Hideki Hashimoto,et al.  Development of advanced parking assistance system , 2003, IEEE Trans. Ind. Electron..

[13]  Howie Choset,et al.  Accurate relative localization using odometry , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[14]  M. Gini,et al.  Visual servoing of a miniature robot toward a marked target , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[15]  Pi-Ming Cheng,et al.  DGPS-based lane assist system for transit buses , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).