Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm


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Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.



Advanced Materials Research (Volumes 328-330)

Edited by:

Liangchi Zhang, Chunliang Zhang and Zichen Chen




C. Zeng et al., "Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm", Advanced Materials Research, Vols. 328-330, pp. 1881-1886, 2011

Online since:

September 2011




[1] Farritor,S. and Dubowsky, S.: A Genetic Algorithm Based Navigation and Planning Methodology for Planetary Robot Exploration, Proceedings of the 7th American Nuclear Society Conference on Robotics and Remote Systms, Augusta, GA(1997).

[2] Sugihara, K. and Smith, J.: Genetic Algorithms for Adaptive Motion Planning of an autonomous Mobile Robot, Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, Monterey, CA, pp.138-146, (1997).

DOI: 10.1109/cira.1997.613850

[3] Vadakkepat, P. and Chen, T.K.: Evolutionary Artificial Potential Fields and Their Application in Real Time Robot Path Planning, Proceeding of the 2000 Congress on Evolutionary Computation, San Diego, CA, pp.256-264, (2000).

DOI: 10.1109/cec.2000.870304

[4] Geisler, T. and Manikas, T.: Autonomous Robot Navigation System Using a Novel Value Encoded Genetic Algorithm, Proceeding of IEEE Midwest Symposium on Circuits and Systems, Tulsa, OK, (2002).

DOI: 10.1109/mwscas.2002.1186966

[5] Geisler, T.: Autonomous Robot Navigation System Using A Genetic Algorithm with a Novel Value Encoding Technique, Master's Thesis, The University of Tulsa, OK, (2002).

[6] Hermanu, A.: Genetic Algorithm with Modified Novel Value Encoding Technique for Autonomous Robot Navigation, Master's Thesis, The University of Tulsa, OK, (2002).

[7] Lebedev D.: Neural network model for robot path planning in dynamically changing environment. volume 18 of Modeling and Analysis of Information Systems, (2001).

[8] Gallardo, D. and Colomina, O.: A Genetic Algorithm for Robust Motion Planing, Eleventh International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Castellon, Spain, June, pp.115-121(1998).

[9] Xiao, J. and Zhang, L.: Adaptive Evolutionary Planner/Navigator for Mobile Robots, volume 1 of IEEE Transactions on Evolutionary Computation, (1997).

DOI: 10.1109/4235.585889

[10] Hwang, Y.K., Ahuja, N.: Gross Motion Planning - A Survey, volume 24 of ACM Computing Surveys, issue 3, (1992).

[11] Trivedi,N., Lai,W. andZhang,Z.: Optimizing Windows Layout by Applying a Genetic Algorithm, Proceedings of the 2001Congress on Evolutionary Computation, Seoul, Korea, (2001).

DOI: 10.1109/cec.2001.934423

[12] Filho, J.L.R. and Treleaven P.C.: Genetic Algorithm Programming Environment, IEEE Computer, pp.28-43, (1994).

[13] Srinivas, M. and Patnaik, L.M.: Genetic Algorithms: A survey, IEEE computer, pp.17-26, (1994).

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