Path Planning in Probabilistic Environment by Bacterial Memetic Algorithm

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. In case of probabilistic environment not only static obstacles obstruct the free passage of the robot, but there are appearances of obstacles with probability. The problem is approached by the bacterial memetic algorithm. The objective is to minimize the path length and the number of turns without colliding with an obstacle. Our method is able to generate a collision-free path in probabilistic environment. The proposed algorithm is tested by simulations.

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

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

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

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

[5]  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).

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

[7]  Naoyuki Kubota,et al.  Bacterial memetic algorithm for offline path planning of mobile robots , 2012, Memetic Comput..

[8]  Naoyuki Kubota,et al.  Topological environment reconstruction in informationally structured space for pocket robot partners , 2009, 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA).

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

[10]  Dharma P. Agrawal,et al.  Ad Hoc and Sensor Networks: Theory and Applications , 2006 .

[11]  Orest Iftime,et al.  Proceedings of the 16th IFAC World congress , 2006 .

[12]  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..

[13]  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).

[14]  Honghai Liu,et al.  Intelligent Robotics and Applications , 2014, Lecture Notes in Computer Science.

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

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

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

[18]  László T. Kóczy,et al.  Eugenic bacterial memetic algorithm for fuzzy road transport traveling salesman problem , 2011 .

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

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