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在静态环境下,针对蚁群算法在进行多目标路径规划时存在搜索时间长以及容易陷入局部最优的特点,介绍一种可对捡球机器人进行多目标路径规划的改进蚁群算法.首先采用栅格法将捡球机器人的网球场环境划分建模;然后自适应调整算法中与信息素相关的系数以及种群规模,提高算法搜索速度;并引入交叉操作,改善算法停滞问题,增强算法的逃逸能力;最后实现改进蚁群算法在捡球机器人多目标路径规划中的应用.仿真实验结果证明,所提算法可以加快搜索速度,找到全局最优路径.
In static environment, the ant colony algorithm has the characteristics of long search time and easy to fall into the local optimum when it comes to multi-objective path planning. An improved ant colony algorithm for multi-objective path planning of ball picking robots is introduced. Firstly, The raster method is used to model the environment of the tennis court of picking robot. Then, the pheromone-related coefficients and population size are adaptively adjusted to improve the algorithm searching speed. The crossover operation is introduced to improve the algorithm stagnation problem and enhance the escape of the algorithm Finally, the application of improved ant colony algorithm in the multi-objective path planning of ball-catching robots is realized.The simulation results show that the proposed algorithm can speed up the search and find the optimal global path.