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

With the rapid development of artificial intelligence and robotics, enhancing the operational efficiency and autonomy of robots in complex, dynamic environments has become a key research focus. To address core challenges in this domain — particularly the limitations of traditional robot path planning methods, such as slow convergence and local optimum entrapment — this study proposes a novel Tidal Optimization Algorithm (TOA) inspired by the periodic dynamics of ocean tides. The performance of TOA is rigorously evaluated through comprehensive experiments. First, it is benchmarked against six state-of-the-art algorithms on the CEC 2017 test suite. Subsequently, it is applied to grid-based robot path planning tasks and compared with four competitive algorithms. Results demonstrate that TOA achieves superior or highly competitive performance on most benchmark functions, confirming its strong global search capability. In path planning applications, TOA generates shorter and safer trajectories than all competitors. From the perspective of intelligent robotics, this research not only provides an effective optimization tool for path planning but also offers potential algorithmic insights for future applications in areas such as human–robot interaction, adaptive motion control, and Human Action Recognition (HAR)-driven navigation systems.