Bacterial memetic algorithm for offline path planning of mobile robots

The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. This problem belongs to the group of combinatorial optimization problems which are approached by modern optimization techniques such as evolutionary algorithms. In this paper the bacterial memetic algorithm is proposed for path planning of a mobile robot. The objective is to minimize the path length and the number of turns without colliding with an obstacle. The representation used in the paper fits well to the algorithm. Memetic algorithms combine evolutionary algorithms with local search heuristics in order to speed up the evolutionary process. The bacterial memetic algorithm applies the bacterial operators instead of the genetic algorithm’s crossover and mutation operator. One advantage of these operators is that they easily can handle individuals with different length. The method is able to generate a collision-free path for the robot even in complicated search spaces. The proposed algorithm is tested in real environment.

[1]  C. Cabrita,et al.  Bacterial Memetic Algorithm for Fuzzy Rule Base Optimization , 2006, 2006 World Automation Congress.

[2]  Peter Merz,et al.  Solving the routing and wavelength assignment problem with a multilevel distributed memetic algorithm , 2009, Memetic Comput..

[3]  Ferrante Neri,et al.  Memetic Compact Differential Evolution for Cartesian Robot Control , 2010, IEEE Computational Intelligence Magazine.

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  Takeshi Furuhashi,et al.  Fuzzy system parameters discovery by bacterial evolutionary algorithm , 1999, IEEE Trans. Fuzzy Syst..

[6]  Janusz Kacprzyk,et al.  Computational Intelligence in Engineering , 2010 .

[7]  Heng-Ming Tai,et al.  Autonomous local path planning for a mobile robot using a genetic algorithm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  Naoyuki Kubota,et al.  Evolutionary Computation for Simultaneous Localization and Mapping Based on Topological Map of a Mobile Robot , 2008, ICIRA.

[9]  I. Ashiru,et al.  Optimal motion planning for mobile robots using genetic algorithms , 1995, Proceedings of IEEE/IAS International Conference on Industrial Automation and Control.

[10]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[11]  Ruhul A. Sarker,et al.  Memetic algorithms for solving job-shop scheduling problems , 2009, Memetic Comput..

[12]  Colin R. Reeves,et al.  Hybrid genetic algorithms applied to beam orientation in radiotherapy , 1996 .

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

[14]  Theodore W. Manikas,et al.  Autonomous robot navigation system using a novel value encoded genetic algorithm , 2002, The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002..

[15]  Ajith Abraham,et al.  A Bacterial Evolutionary Algorithm for automatic data clustering , 2009, 2009 IEEE Congress on Evolutionary Computation.

[16]  Guan-Chun Luh,et al.  ABACTERIAL EVOLUTIONARY ALGORITHM FOR THE JOB SHOP SCHEDULING PROBLEM , 2006 .

[17]  José Aguilar,et al.  Resolution of pattern recognition problems using a hybrid Genetic/Random Neural Network learning algorithm , 2005, Pattern Analysis and Applications.

[18]  László T. Kóczy,et al.  Comparative Investigation of Various Evolutionary and Memetic Algorithms , 2010 .

[19]  Ralf Östermark,et al.  Solving Irregular Econometric and Mathematical Optimization Problems with a Genetic Hybrid Algorithm , 1999 .

[20]  János Botzheim,et al.  Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm , 2010, Memetic Comput..

[21]  Alexander P. Topchy,et al.  FAST LEARNING IN MULTILAYERED NEURAL NETWORKS BY MEANS OF HYBRID EVOLUTIONARY AND GRADIENT ALGORITHMS , 2007 .

[22]  Toshio Fukuda,et al.  The role of virus infection in virus-evolutionary genetic algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[23]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[24]  Mark Sumner,et al.  A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[25]  Jing Tang,et al.  Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems , 2006, Soft Comput..

[26]  Donald C. Wunsch,et al.  Memetic Mission Management [Application Notes] , 2010, IEEE Computational Intelligence Magazine.

[27]  Paolo Gaiardelli,et al.  Hybrid genetic algorithmsfor a multiple-objective scheduling problem , 1998, J. Intell. Manuf..

[28]  László T. Kóczy,et al.  Fuzzy rule extraction by bacterial memetic algorithms , 2009 .

[29]  Caro Lucas,et al.  Memetic Algorithm Based Path Planning for a Mobile Robot , 2007, International Conference on Computational Intelligence.

[30]  Emmanuel C. Ifeachor,et al.  Automatic design of frequency sampling filters by hybrid genetic algorithm techniques , 1998, IEEE Trans. Signal Process..

[31]  Maoguo Gong,et al.  Natural and Remote Sensing Image Segmentation Using Memetic Computing , 2010, IEEE Computational Intelligence Magazine.

[32]  B. Freisleben,et al.  A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[33]  Yew-Soon Ong,et al.  Memetic Computation—Past, Present & Future [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[34]  Zhen Ji,et al.  Towards a Memetic Feature Selection Paradigm [Application Notes] , 2010, IEEE Computational Intelligence Magazine.

[35]  John Smith,et al.  Genetic algorithms for adaptive motion planning of an autonomous mobile robot , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[36]  Carlos Cotta,et al.  A Hybrid Genetic Algorithm for the 0-1 Multiple Knapsack Problem , 1997, ICANNGA.

[37]  Colin Reeves,et al.  Hybrid genetic algorithms for bin-packing and related problems , 1996, Ann. Oper. Res..

[38]  Habib Izadkhah,et al.  Evolutionary Approach for Mobile Robot Path Planning in Complex environment , 2010 .

[39]  Toshio Fukuda,et al.  Computational intelligence for robotic systems , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[40]  Simon X. Yang,et al.  Genetic algorithm based path planning for a mobile robot , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[41]  Mario Drobics,et al.  Optimization of fuzzy rule sets using a bacterial evolutionary algorithm , 2008, SOCO 2008.