Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms

A multi-objective vehicle path planning method has been proposed to optimize path length, path safety, and path smoothness using the elitist non-dominated sorting genetic algorithm—a well-known soft computing approach. Four different path representation schemes that begin their coding from the start point and move one grid at a time towards the destination point are proposed. Minimization of traveled distance and maximization of path safety are considered as objectives of this study while path smoothness is considered as a secondary objective. This study makes an extensive analysis of a number of issues related to the optimization of path planning task-handling of constraints associated with the problem, identifying an efficient path representation scheme, handling single versus multiple objectives, and evaluating the proposed algorithm on large-sized grids and having a dense set of obstacles. The study also compares the performance of the proposed algorithm with an existing GA-based approach. The evaluation of the proposed procedure against extreme conditions having a dense (as high as 91 %) placement of obstacles indicates its robustness and efficiency in solving complex path planning problems. The paper demonstrates the flexibility of evolutionary computing approaches in dealing with large-scale and multi-objective optimization problems.

[1]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[2]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .

[3]  O. Castilho,et al.  Multiple Objective Optimization Genetic Algorithms For Path Planning In Autonomous Mobile Robots , 2005, Int. J. Comput. Syst. Signals.

[4]  M. Gerke,et al.  Genetic path planning for mobile robots , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

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

[6]  Max Q.-H. Meng,et al.  Real-time Collision-free Path Planning of Robot Manipulators using Neural Network Approaches , 2000, Auton. Robots.

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Kalyanmoy Deb,et al.  A flexible optimization procedure for mechanical component design based on genetic adaptive search , 1998 .

[9]  Kalyanmoy Deb,et al.  Understanding Interactions among Genetic Algorithm Parameters , 1998, FOGA.

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

[11]  Yutaka Kanayama,et al.  Smooth local path planning for autonomous vehicles , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[12]  Ralf Salomon,et al.  Implementation of Path Planning using Genetic Algorithms on Mobile Robots , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[13]  Oscar Castillo,et al.  Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots , 2006, Soft Comput..

[14]  Marilena Vendittelli,et al.  Fuzzy maps: A new tool for mobile robot perception and planning , 1997, J. Field Robotics.

[15]  El Kebir Boukas,et al.  Optimal path generation for a simulated autonomous mobile robot , 1995, Auton. Robots.

[16]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[17]  D. K. Pratihar,et al.  FUZZY-GENETIC ALGORITHMS AND TIME-OPTIMAL OBSTACLE-FREE PATH GENERATION FOR MOBILE ROBOTS , 1999 .

[18]  K. S. Al-Sultan,et al.  A new potential field-based algorithm for path planning , 1996, J. Intell. Robotic Syst..

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

[20]  Anthony A. Maciejewski,et al.  Planning of collision-free paths for a reconfigurable dual manipulator equipped mobile robot , 1996, J. Intell. Robotic Syst..

[21]  Rafael Murrieta-Cid,et al.  A Sampling-Based Motion Planning Approach to Maintain Visibility of Unpredictable Targets , 2005, Auton. Robots.

[22]  Narendra Ahuja,et al.  Gross motion planning—a survey , 1992, CSUR.

[23]  J. Periaux,et al.  Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems , 2001 .

[24]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[25]  AhujaNarendra,et al.  Gross motion planninga survey , 1992 .

[26]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[27]  H. Van Dyke Parunak,et al.  Evolving adaptive pheromone path planning mechanisms , 2002, AAMAS '02.

[28]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[29]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[30]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[31]  K. Deb,et al.  Understanding knee points in bicriteria problems and their implications as preferred solution principles , 2011 .

[32]  Kalyanmoy Deb,et al.  Multi-objective path planning using spline representation , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

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

[34]  Marco Laumanns,et al.  Scalable test problems for evolutionary multi-objective optimization , 2001 .

[35]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[36]  J. Brian Burns,et al.  Path planning using Laplace's equation , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[37]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[38]  K. Deb,et al.  Optimal Scheduling of Casting Sequence Using Genetic Algorithms , 2003 .

[39]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[40]  Marco Laumanns,et al.  A Tutorial on Evolutionary Multiobjective Optimization , 2004, Metaheuristics for Multiobjective Optimisation.

[41]  Kokichi Sugihara,et al.  Genetic Algorithms for Adaptive Planning of Path and Trajectory of a Mobile Robot in 2D Terrains , 1999 .

[42]  Narendra Ahuja,et al.  Shape Representation Using a Generalized Potential Field Model , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  Stan C. A. M. Gielen,et al.  Neural Network Dynamics for Path Planning and Obstacle Avoidance , 1995, Neural Networks.

[44]  K. Sugihara Measures for Performance Evaluation of Genetic Algorithms , 1997 .

[45]  Robert J. Wood,et al.  Towards a 3g crawling robot through the integration of microrobot technologies , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..