| 基于凸策略优胜劣汰蚁群算法的机器人路径规划 |
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| 引用本文： | 鲍义东,夏栋梁,赵伟艇. 基于凸策略优胜劣汰蚁群算法的机器人路径规划[J]. 计算机系统应用, 2015, 24(8): 122-127 |
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| 作者姓名： | 鲍义东 夏栋梁 赵伟艇 |
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| 作者单位： | 平顶山学院 软件学院, 平顶山 467000;哈尔滨工业大学 机器人技术与系统国家重点实验室, 哈尔滨 150080;平顶山学院 软件学院, 平顶山 467000;平顶山学院 软件学院, 平顶山 467000 |
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| 基金项目： | 河南省教育厅科学技术研究重点项目(14B520039);校青年科研基金项目(PXY-QNJJ-2014004) |
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| 摘 要： | 针对机器人在已知静态工作环境中运动路径的快速选择和判优, 提出一种改进蚁群优化算法. 该算法首先对机器人的静态工作空间进行凸策略处理, 从环境上降低了搜索的盲目性和落入陷阱的可能性, 并在此基础上加入一种优胜劣汰策略, 进一步提高了算法的时间性能、最佳性能和鲁棒性. 实验结果表明改进的蚁群优化算法不仅克服了易于陷入局部最优解及运算量大的缺陷, 而且也大幅度提高了算法的运算效率.
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| 关 键 词： | 路径规划 蚁群算法 凸处理 优胜劣汰策略 |
| 收稿时间： | 2014-11-25 |
| 修稿时间： | 2015-01-12 |
Robot Path Planning Based on Ant Colony Optimization Convex Fittest Strategy |
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| BAO Yi-Dong,XIA Dong-Liang and ZHAO Wei-Ting. Robot Path Planning Based on Ant Colony Optimization Convex Fittest Strategy[J]. Computer Systems& Applications, 2015, 24(8): 122-127 |
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| Authors: | BAO Yi-Dong XIA Dong-Liang ZHAO Wei-Ting |
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| Affiliation: | School of Software, Pingdingshan University, Pingdingshan 467000, China;State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China;School of Software, Pingdingshan University, Pingdingshan 467000, China;School of Software, Pingdingshan University, Pingdingshan 467000, China |
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| Abstract: | An improved ant colony optimization based on convex policy and the survival of the fittest strategy for robot path planning is proposed in this paper. This algorithm first use convex policy to process the robot's static workspace, and it can reduce the blindness of searching and the possibility of falling into the trap in environment. In addition, a survival of the fittest strategy is added in the ant colony optimization to further improve the performance of the algorithm time, optimum performance and robustness. Experimental results show that the improved ant colony optimization not only can overcome the defect of easy to fall into local optimal solution, but also can improve the operation efficiency. |
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| Keywords: | path planning ant colony algorithm convex treatment survival of the fittest strategy |
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