Adaptive operator selection for path planning in static environments

Genetic algorithms have been fairly successful in solving difficult and ill-behaved optimization problems, such as path planning. In this paper we use the operator selection approach and introduce new one operator to solve the path planning problem. The Adaptive Operator Selection is utilized in the genetic algorithm to select the operators optimally in a given situation. In this paper, two different probability-based methods, namely the Probability Matching and the Adaptive Pursuit are used for this purpose. The methods are implemented and simulated in several static environments.

[1]  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).

[2]  Jianhua Zhang,et al.  Robot path planning in uncertain environment using multi-objective particle swarm optimization , 2013, Neurocomputing.

[3]  Dayal R. Parhi,et al.  Controlling the Motion of an Autonomous Mobile Robot Using Various Techniques: a Review , 2013 .

[4]  D. Rathbun,et al.  An evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments , 2002, Proceedings. The 21st Digital Avionics Systems Conference.

[5]  S. Areibi,et al.  Genetic algorithm for dynamic path planning , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[6]  David E. Goldberg,et al.  Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding , 1990, Machine Learning.

[7]  Michèle Sebag,et al.  Adaptive operator selection with dynamic multi-armed bandits , 2008, GECCO '08.

[8]  Ping Yan,et al.  Route Planning for Unmanned Air Vehicles with Multiple Missions Using an Evolutionary Algorithm , 2007, Third International Conference on Natural Computation (ICNC 2007).

[9]  Gianluca Antonelli,et al.  A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking , 2007, IEEE Transactions on Fuzzy Systems.

[10]  Xin Chen,et al.  Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN , 2005, ICNC.

[11]  Dirk Thierens,et al.  An Adaptive Pursuit Strategy for Allocating Operator Probabilities , 2005, BNAIC.

[12]  Zhang Yi,et al.  Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[13]  Hideki Hashimoto,et al.  Path generation for mobile robot navigation using genetic algorithm , 1995, Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics.

[14]  Wei Zhang,et al.  An Improved Genetic Algorithm of Optimum Path Planning for Mobile Robots , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[15]  Lawrence Davis,et al.  Adapting Operator Probabilities in Genetic Algorithms , 1989, ICGA.

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

[17]  Vincent Roberge,et al.  Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning , 2013, IEEE Transactions on Industrial Informatics.

[18]  John Yen,et al.  A fuzzy logic based extension to Payton and Rosenblatt's command fusion method for mobile robot navigation , 1995, IEEE Trans. Syst. Man Cybern..

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

[20]  Simon X. Yang,et al.  A knowledge based genetic algorithm for path planning of a mobile robot , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.