Way point based deliberative path planner for navigation

We present and implement a Genetic Algorithm (GA) based deliberative path planning algorithm using waypoints for navigation of Robots and AGV's. The Waypoint Navigation System is advantageous as it does not require a complete knowledge of the environment and can replace traditional robot navigation systems. The proposed GA for Robot navigation problem is NP-hard and Multi-objective. The performance of the implemented GA is compared with the results obtained from two popular algorithms namely the Dijikstra's algorithm and A* algorithms. We propose a new Representation that reduces the size of the chromosome array for large number of waypoints; the proposed GA has faster convergence and obtains near optimal solutions. The system has been developed for a near real-time experiment using a Matlab engine located on an online server. An integrated system complete with the required Hardware was constructed for the purpose of testing the performance of the various algorithms using a real world Geographical Information System (GIS) and actual node data (waypoints) of Kuala Lumpur City. The proposed new system has been tested and is found to be suitable for adaptation in robot navigation, path planning and intelligent traffic guidance systems.

[1]  F. Benjamin Zhan,et al.  Shortest Path Algorithms: An Evaluation Using Real Road Networks , 1998, Transp. Sci..

[2]  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'.

[3]  Yang Wang,et al.  Waypoint-based Mobile Robot Navigation , 2006, 2006 6th World Congress on Intelligent Control and Automation.

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

[5]  Christina Hallam,et al.  A Multiobjective Optimal Path Algorithm , 2001, Digit. Signal Process..

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

[7]  D. Mulvaney,et al.  Efflcient incremental decision tree generation for embedded applications , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[8]  Eric R. Ziegel,et al.  Genetic Algorithms and Engineering Optimization , 2002, Technometrics.

[9]  Bin Ran,et al.  MODELING DYNAMIC TRANSPORTATION NETWORKS , 1996 .

[10]  Chang Wook Ahn,et al.  A genetic algorithm for shortest path routing problem and the sizing of populations , 2002, IEEE Trans. Evol. Comput..

[11]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[12]  Yang Wang,et al.  Real-time machine learning in embedded software and hardware platforms , 2007, Int. J. Intell. Syst. Technol. Appl..