Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

复杂环境下移动机器人的路径规划是最具挑战性的研究。本研究中提出了一种将增强型蚁群系统与基于路径几何特征的局部优化算法相结合的混合方法，称为 EACSPGO，用于移动机器人路径规划。首先，同时引入信息素扩散的简化模型、不平等分配的信息素初始化策略和自适应信息素更新机制，对经典蚁群算法进行了增强，从而显着提高了计算效率和解的质量. 设计了一种基于路径几何特征的局部优化方法，进一步优化初始路径，达到良好的收敛速度。最后，所提出方法的性能和优势已通过移动机器人路径规划中的一系列测试得到验证。仿真结果表明，与其他经过测试的优化算法相比，所提出的 EACSPGO 方法提供了更好的解决方案、适应性、稳定性和更快的收敛速度。