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Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

机译:使用蚁群算法的移动机器人路径规划和改进的电位现场方法

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For the problem of mobile robot’s path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop. Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone. The process of optimization of ACO is divided into two phases. In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner. In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path. Finally, some simulation experiments and mobile robot environment experiments are done. The experiment results verify that the method has stronger stability and environmental adaptability.
机译:对于在已知环境下的移动机器人路径规划的问题,提出了一种基于网格图的混合人工势场(APF)和蚁群优化(ACO)的路径规划方法。首先,基于网格模型,APF以三种方式改进:吸引场,由此产生的力的方向,跳出无限循环。然后,将开发出与本地更新的混合策略组合全局更新,以设计ACO信息素的更新方法。 ACO优化过程分为两个阶段。在预先,通过改进的APF获得的所得力的方向用作启发的因素,这引发了蚁群以方向的方式移动。在后期,激发因素被取消,蚁群过渡完全基于信息素更新,这可以克服蚁群的惯性,并迫使它们探索新的和更好的道路。最后,完成了一些仿真实验和移动机器人环境实验。实验结果验证该方法具有较强的稳定性和环境适应性。

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