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基于改进蚁群算法的农用喷药机器人路径规划
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国家自然科学基金项目(51405246);江苏省产学研联合创新资金项目(BY201408107);南通市重点实验室项目计划(CP2014001);南通市应用基础研究-工业创新项目(GY12016006)


Path Planning of Agricultural Spraying Robot Based on Improved Ant Colony Algorithm
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    摘要:

    研究农用喷药机器人路径规划问题,提出一种基于改进蚁群算法的路径寻优方法。首先,获取实际工作环境信息,抽象化处理工作环境,采用栅格法建立喷药机器人工作环境模型;其次,为使算法搜索更具目的性,引入目标点诱导机制,设计新的距离启发函数,并在此基础上对状态转移概率进行改进;为避免算法搜索出现停滞和提高路径搜索效率,通过引入信息素阈值限定、信息素局部和全局更新相结合的策略对信息素更新方式进行优化;最后,通过仿真实验测试两种算法解决喷药机器人路径规划问题的实际效果。结果表明:两种算法均能有效解决喷药机器人路径规划问题,且相比传统蚁群算法,改进蚁群算法不仅可以有效改善自身收敛性能,而且可以增强自身全局寻优能力。

    Abstract:

    In order to solve the problem of agricultural spraying robot path planning, a pathplanning method based on improved ant colony algorithm was put forward. After acquiring actual working environment information and processing working environment with abstract method, grid method was adopted to establish the working environment model of spraying robot. To make the algorithm search more purposeful, heuristic function of ant colony algorithm was redesigned through introducing the target guiding mechanism. Then, state transition probability was ameliorated with new heuristic function. To avoid the algorithm search process stalled and enhance the efficiency of path searching, the pheromone update method was optimized and ameliorated with the strategy of restricting pheromone thresholds and the combination strategies of local updating and global updating of the pheromones. Finally, the simulation experiments were utilized to verify actual results of two algorithms solving the problems of spraying robot path planning. Experimental results show that, the problem of spraying robot path planning can be effectively solved by tradition ant colony algorithm (TACO) and improved ant colony algorithm (IACO). Besides, compared with TACO,IACO not only can effectively shorten program time, but also can improve its convergence performance and enhance its global optimization ability.

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庄丽阳,陈树林,朱龙彪,王辉.基于改进蚁群算法的农用喷药机器人路径规划[J].机床与液压,2018,46(21):15-19.
. Path Planning of Agricultural Spraying Robot Based on Improved Ant Colony Algorithm[J]. Machine Tool & Hydraulics,2018,46(21):15-19

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  • 在线发布日期: 2019-07-09
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