A Real-Time Obstacle Avoidance Method for Autonomous Vehicles Using an Obstacle-Dependent Gaussian Potential Field

A new obstacle avoidance method for autonomous vehicles called obstacle-dependent Gaussian potential field (ODG-PF) was designed and implemented. It detects obstacles and calculates the likelihood of collision with them. In this paper, we present a novel attractive field and repulsive field calculation method and direction decision approach. Simulations and the experiments were carried out and compared with other potential field-based obstacle avoidance methods. The results show that ODG-PF performed the best in most cases.

[1]  Vladimir J. Lumelsky,et al.  Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape , 1987, Algorithmica.

[2]  Luis Moreno,et al.  Path Planning for Mobile Robot Navigation using Voronoi Diagram and Fast Marching , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Shuzhi Sam Ge,et al.  Dynamic Motion Planning for Mobile Robots Using Potential Field Method , 2002, Auton. Robots.

[4]  Francesco Bullo,et al.  Smooth Nearness-Diagram Navigation , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  T. Arney An efficient solution to autonomous path planning by Approximate Cell Decomposition , 2007, 2007 Third International Conference on Information and Automation for Sustainability.

[6]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

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

[8]  Alain Oustaloup,et al.  Path planning by fractional differentiation , 2003, Robotica.

[9]  G. Edwar Jacinto,et al.  Path planning in static scenarios using image processing and cell decomposition , 2014, 2014 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC).

[10]  Yoram Koren,et al.  Potential field methods and their inherent limitations for mobile robot navigation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[11]  Muhannad Mujahed,et al.  Smooth and Safe Nearness-Diagram (SSND) Navigation for Autonomous Mobile Robots , 2011 .

[12]  Howie Choset,et al.  Sensor based motion planning: the hierarchical generalized Voronoi graph , 1996 .

[13]  Marina L. Gavrilova,et al.  Voronoi diagram in optimal path planning , 2007, 4th International Symposium on Voronoi Diagrams in Science and Engineering (ISVD 2007).

[14]  Olivier Lavialle,et al.  Consideration of obstacle danger level in path planning using A* and Fast-Marching optimisation: comparative study , 2003, Signal Process..

[15]  Dolores Blanco,et al.  Exploration of a cluttered environment using Voronoi Transform and Fast Marching , 2008, Robotics Auton. Syst..

[16]  Iwan Ulrich,et al.  VFH+: reliable obstacle avoidance for fast mobile robots , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[17]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[18]  Iwan Ulrich,et al.  VFH/sup */: local obstacle avoidance with look-ahead verification , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[19]  Sterling J. Anderson,et al.  An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios , 2010 .

[20]  Javier Minguez,et al.  Nearness diagram navigation (ND): a new real time collision avoidance approach , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[21]  Dong W. Kim,et al.  Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance , 2016, Comput. Intell. Neurosci..

[22]  Javier Minguez,et al.  A "divide and conquer" strategy based on situations to achieve reactive collision avoidance in troublesome scenarios , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[23]  Yoram Koren,et al.  The vector field histogram-fast obstacle avoidance for mobile robots , 1991, IEEE Trans. Robotics Autom..

[24]  Volkan Sezer,et al.  A novel obstacle avoidance algorithm: "Follow the Gap Method" , 2012, Robotics Auton. Syst..

[25]  Ellips Masehian,et al.  A voronoi diagram-visibility graph-potential field compound algorithm for robot path planning , 2004, J. Field Robotics.

[26]  Panagiotis Tsiotras,et al.  Beyond quadtrees: Cell decompositions for path planning using wavelet transforms , 2007, 2007 46th IEEE Conference on Decision and Control.