Purpose

This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments.

Design/methodology/approach

The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point.

Findings

Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these.

Originality/value

Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.

中文翻译：

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基于混合正弦-余弦算法和蚁群优化技术的移动机器人最优路径搜索与控制

目的

本文旨在将混合高级正弦余弦算法（ASCA）和高级蚁群优化（AACO）技术结合在一起，以在静态和动态未知环境中控制多个移动机器人的情况下实现最佳路径搜索。

设计/方法/方法

用于ASCA和AACO的控制器是通过MATLAB仿真以及各种环境中的实时实验来设计和实现的。每当传感器检测到障碍物时，都会使用ASCA在感应范围内找到它们的全局最佳位置，然后激活AACO以选择下一个观察点。这就是机器人如何移动到指定的目标点。

发现

通过使用单个和多个移动机器人实施此处开发的技术，可以进行导航分析。通过仿真与实验结果之间的比较来验证其效率。此外，发现与现有方法相比，所提出的技术更有效。在更好地控制这些路径的同时，实现了大约10.21％的路径长度显着改善。

创意/价值

提出的技术的系统介绍吸引了以AI技术为应用标准的研究人员广泛的读者群。