Swarm intelligence is one of the most emerging methods used for autonomous mobile robot navigation. Researchers have developed many algorithms in mobile robot navigation by simulating the swarming behavior of various creatures like ants, firefly, honey bees, and cuckoo, and the findings are very motivating. The paper presents application and implementation of spider monkey optimization (SMO) along with Three Path method (TPM) for mobile robot navigation in cluttered environment. A collision-free path is selected by using TPM. When all the three paths are blocked by obstacles, SMO is used for obstacles avoidance. The finding of global and local leaders from the groups is the key concept of the proposed method. The proposed method efficiently improves the global search in less number of iterations, and hence, it can be easily implemented for real-time obstacle avoidance. The computational path and time are less as compared to other navigational methods. Some simulation results are presented at the end of the paper to show the effectiveness of the proposed navigational method.

中文翻译：

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使用蜘蛛猴优化算法在杂乱环境中的移动机器人导航

群智能是用于自主移动机器人导航的最新兴方法之一。研究人员通过模拟蚂蚁、萤火虫、蜜蜂和杜鹃等各种生物的蜂拥行为，开发了许多移动机器人导航算法，研究结果非常鼓舞人心。本文介绍了蜘蛛猴优化 (SMO) 以及三路径法 (TPM) 在杂乱环境中用于移动机器人导航的应用和实现。使用 TPM 选择无冲突路径。当三个路径都被障碍物挡住时，SMO 用于避障。从团体中寻找全球和地方领导者是所提出方法的关键概念。所提出的方法以较少的迭代次数有效地改进了全局搜索，因此，它可以轻松实现实时避障。与其他导航方法相比，计算路径和时间更少。论文最后给出了一些仿真结果，以证明所提出的导航方法的有效性。