Robot Path Planning Based on the Tidal Optimization Algorithm: A New Swarm Intelligence Approach

With the rapid development of artificial intelligence and intelligent robotics, enhancing a robot's ability to operate in complex, dynamic environments has become a key research focus. To address the core challenge within this domain—specifically, the limitations of traditional robot path planning methods such as slow convergence and the tendency to fall into local optima—this study proposes a novel Tidal Optimization Algorithm (TOA) inspired by the periodic and dynamic characteristics of ocean tides. The proposed TOA is rigorously evaluated through comprehensive experiments. First, it is compared with six state-of-the-art algorithms on the CEC 2017 benchmark suite, followed by applications to gridbased robot path planning tasks against four competitive algorithms. The results demonstrate that TOA achieves superior or highly competitive performance on most benchmark functions, confirming its strong global search capability. In path planning, TOA generates shorter and safer trajectories, outperforming all competitors. From the perspective of intelligent robotics, this research not only contributes a powerful optimization approach for path planning but also provides potential algorithmic insights for future applications in human–robot interaction, adaptive motion control, and Human Action Recognition–driven navigation systems.