Mobile robot path planning using artificial bee colony and evolutionary programming

Graphical abstractDisplay Omitted HighlightsWe solve the path planning problem using the combination of two evolutionary methods.First, an artificial bee colony (ABC) finds a feasible path in the free space.Second, evolutionary programming (EP) optimizes the path length and smoothness.The proposed approach was compared to a probabilistic roadmap (PRM) method.The ABC-EP approach outperforms the PRM approach on problems of varying complexity. In this paper, an evolutionary approach to solve the mobile robot path planning problem is proposed. The proposed approach combines the artificial bee colony algorithm as a local search procedure and the evolutionary programming algorithm to refine the feasible path found by a set of local procedures. The proposed method is compared to a classical probabilistic roadmap method (PRM) with respect to their planning performances on a set of benchmark problems and it exhibits a better performance. Criteria used to measure planning effectiveness include the path length, the smoothness of planned paths, the computation time and the success rate in planning. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed method are also shown.

[1]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[2]  Janusz Kacprzyk,et al.  A memetic algorithm based procedure for a global path planning of a movement constrained mobile robot , 2013, 2013 IEEE Congress on Evolutionary Computation.

[3]  Dunwei Gong,et al.  Robot path planning in an environment with many terrains based on interval multi-objective PSO , 2013, 2013 IEEE Congress on Evolutionary Computation.

[4]  Lydia E. Kavraki,et al.  Measure theoretic analysis of probabilistic path planning , 2004, IEEE Transactions on Robotics and Automation.

[5]  Wei Li,et al.  Application of improved PSO in mobile robotic path planning , 2010, 2010 International Conference on Intelligent Computing and Integrated Systems.

[6]  Simon Parsons,et al.  Principles of Robot Motion: Theory, Algorithms and Implementations by Howie Choset, Kevin M. Lynch, Seth Hutchinson, George Kantor, Wolfram Burgard, Lydia E. Kavraki and Sebastian Thrun, 603 pp., $60.00, ISBN 0-262-033275 , 2007, The Knowledge Engineering Review.

[7]  Ellips Masehian,et al.  Classic and Heuristic Approaches in Robot Motion Planning A Chronological Review , 2007 .

[8]  Nouara Achour,et al.  Mobile Robots Path Planning using Genetic Algorithms , 2011 .

[9]  Lydia E. Kavraki,et al.  Probabilistic roadmaps for path planning in high-dimensional configuration spaces , 1996, IEEE Trans. Robotics Autom..

[10]  Devendra Singh,et al.  An Improved ABC Algorithm for Optimal Path Planning , 2013 .

[11]  Howie Choset,et al.  Principles of Robot Motion: Theory, Algorithms, and Implementation ERRATA!!!! 1 , 2007 .

[12]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[13]  Ellips Masehian,et al.  A multi-objective PSO-based algorithm for robot path planning , 2010, 2010 IEEE International Conference on Industrial Technology.

[14]  Zbigniew Michalewicz,et al.  Adaptive evolutionary planner/navigator for mobile robots , 1997, IEEE Trans. Evol. Comput..

[15]  Ching-Chih Tsai,et al.  Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation , 2011, IEEE Transactions on Industrial Electronics.

[16]  Sachin Chitta,et al.  A generic infrastructure for benchmarking motion planners , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Hu Jun,et al.  Multi-objective Mobile Robot Path Planning Based on Improved Genetic Algorithm , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[18]  Kuo-Lan Su,et al.  Ant Colony System Based Mobile Robot Path Planning , 2010, 2010 Fourth International Conference on Genetic and Evolutionary Computing.

[19]  Chee Yap,et al.  Algorithmic motion planning , 1987 .

[20]  E. Mansury,et al.  Artificial Bee Colony optimization of ferguson splines for soccer robot path planning , 2013, 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM).

[21]  Lawrence J. Fogel,et al.  Intelligence Through Simulated Evolution: Forty Years of Evolutionary Programming , 1999 .