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. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.

中文翻译：

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使用群体优化和传感器部署的全向移动机器人自主导航和避障

目前的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智能路径规划算法的设计。提出了两种修改来改进标准蝙蝠算法的搜索过程，并结合两种新算法的结果。第一种算法是Modified Frequency Bat算法，第二种是Particle Swarm Optimization与Modified Frequency Bat算法的混合，即Hybrid Particle Swarm Optimization-Modified Frequency Bat算法。Modified Frequency Bat 和Hybrid Particle Swarm Optimization-Modified Frequency Bat 算法都与提出的障碍检测和避障技术相结合，并使用自由空间建模应用于不同的静态和动态环境。而且，提出了一种新程序，将通过路径建议的基于群启发优化的路径规划算法提出的不可行解决方案转换为可行解决方案。仿真在 MATLAB 环境中运行以测试建议算法的有效性。他们已经表明，所提出的路径规划算法通过在各种静态和动态场景下找到最短和最平滑的无碰撞路径来获得卓越的性能。