Bio-Inspired Hybrid Path Planning for Efficient and Smooth Robotic Navigation

Robotic navigation in complex, high-dimensional environments poses significant challenges, particularly in achieving efficient exploration, collision-free trajectory planning, and robust performance under dynamic conditions. Traditional approaches relying on a single optimization method, such as genetic algorithms or grey wolf optimizers, often suffer from premature convergence and limited exploration capabilities. To address these issues, this paper introduces a novel hybrid algorithm that synergistically integrates ant colony optimization (ACO), whale optimization algorithm (WOA), artificial potential fields (APF), and random jump mechanisms. Our framework not only enhances convergence reliability and maintains solution diversity but also significantly improves obstacle avoidance, as demonstrated through extensive simulation studies. Moreover, by reducing collision times across various initial settings and generating smooth trajectories, the proposed approach offers a safer and more practical solution for robotic navigation, particularly in applications involving human passengers. Experimental results validate the effectiveness of our method, highlighting its potential for real-world deployment in complex navigation tasks.