Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called modified aging ant colony optimization (AACO). The AACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

中文翻译：

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在静态和动态环境中使用基于老化的蚁群优化算法的基于网格的移动机器人路径规划。

为移动机器人规划最佳路径是一个复杂的问题，因为它允许移动机器人通过遵循起点和目标之间最安全，最短的路径来自主导航。本工作涉及基于群体智能优化的静态和动态环境中移动机器人的智能路径规划算法的设计。将基于蚁龄的修改引入标准蚁群优化，称为修改后的老化蚁群优化（AACO）。AACO与静态和动态环境的基于网格的建模相关联，以解决路径规划问题。仿真在MATLAB环境中运行，以测试所提出算法的有效性。仿真结果表明，所提出的路径规划算法通过在各种静态和动态情况下找到最短和最自由的碰撞路径，可以提供卓越的性能。此外，通过与具有不同静态环境的其他传统路径规划算法进行比较，证明了所提出算法的优越性。