A path planner based on multivariant optimization algorithm

被引：0
|
作者：
Li B.-L. [1 ,2 ]
Lü D.-J. [2 ]
Zhang Q.-H. [2 ]
Shi X.-L. [2 ]
Chen J.-H. [2 ]
Zhang Y.-F. [2 ]
机构：
[1] Physics and Electronic Engineering College, Nanyang Normal University, Nanyang, 473061, Henan
[2] School of Information Science and Engineering, Yunnan University, Kunming, 650091, Yunnan
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2016年 / 44卷 / 09期
关键词：
Bezier curve; Global atom; Local atom; Multivariant optimization algorithm; Path planning;
D O I：
10.3969/j.issn.0372-2112.2016.09.032
中图分类号：
学科分类号：
摘要：
A heuristic intelligent path planning method based on the multivariant optimization algorithm and the Bezier curve is presented. The path planning problem is transformed into an optimization problem through using the Bezier curve to represent a path in this method. Then, the multivariant optimization algorithm is applied to find the optimal control points of the best Bezier curve, aiming at finding the optimal path. The multivariant optimization algorithm searches the solution space through iterations of alternative global and local search. According to the different responsibilities, the search individuals (atoms) could be divided into two types: the global atoms and the local atoms. In each iteration, global atoms explore the whole solution space to local potential areas, and then, local atoms exploit each potential area. Obviously, atoms are characterized by multivariant responsibilities, hence the name of the multivariant optimization algorithm. The good performance of the multivariant optimization algorithm is ensured by the efficient communication and cooperation of multivariant atoms. To evaluate the performance of the multivariant optimization algorithm, comparative experiments against the other three classical heuristic path planning algorithms are carried out based on a standard testing map. The results show that our proposed method is superior to the other methods in optimality, stability and efficiency. © 2016, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码：2242 / 2247
页数：5
相关论文
共 19 条
  • [1] Canny J., Reif J., New lower bound techniques for robot motion planning problems, IEEE Symposium on the Foundations of Computer, pp. 49-60, (1987)
  • [2] Raja P., Pugazhenthi S., Optimal path planning of mobile robots: A review, International Journal of Physical Sciences, 7, 9, pp. 1314-1320, (2012)
  • [3] Hu Y., Yang S.X., A knowledge based genetic algorithm for path planning of a mobile robot, 2004 IEEE International Conference on Robotics and Automation, pp. 4350-4355, (2004)
  • [4] Liu C.-A., Yan X.-H., Liu C.-Y., Et al., Dynamic path planning for mobile robot based on improved ant colony optimization algorithm, Acta Electronic Sinica, 39, 5, pp. 1220-1224, (2011)
  • [5] Zhou L.-F., Hong B.-R., A knowledge based genetic algorithm for path planning of a mobile robot, Acta Electronic Sinica, 34, 5, pp. 911-914, (2006)
  • [6] Wu X.-X., Guo B.-L., Wang J., Mobile robot path planning algorithm based on particle swarm optimization of cubic splines, Robot, 31, 6, pp. 556-560, (2009)
  • [7] Xu X.-Q., Zhu Q.-B., Multi-artificial fish-swarm algorithm and a rule library based dynamic collision avoidance algorithm for robot path planning in a dynamic environment, Acta Electronic Sinica, 40, 8, pp. 1694-1700, (2012)
  • [8] Chen G., Shen L.-C., Genetic path planning algorithm for complex environment path planning, Robot, 23, 1, pp. 40-44, (2001)
  • [9] Fister I., Yang X.S., Fister D., Et al., Firefly Algorithm: A Brief Review of the Expanding Literature, pp. 347-360, (2014)
  • [10] Yang X.S., Firefly Algorithms for Multimodal Optimization, pp. 169-178, (2009)
12