Synergizing Graph-Based Methods with Biologically Inspired Algorithms for Enhanced Robot Path Planning Efficiency

Robotic path planning and navigation in intricate environments pose significant challenges in various domains, including search and rescue, agriculture, and various defense applications. There have been various methods proposed to solve these problems, such as graph-based methodologies. Although the majority of cell decomposition methods lack the capability to develop a near optimal path, we propose a middle point cell decomposition in combination with a biologically inspired optimization algorithm for robot path planning and mapping. The proposed model leverages vertical cell decomposition in combination with an enhanced biologically inspired particle swarm optimization algorithm (ePSO). Vertical cell decomposition is employed as a spatial partitioning technique, segmenting complex environments into vertical cells, each characterized by a simplified geometric representation. To improve the path finding process, we introduce middle points within these cells. In this research, midpoints in the graph are regulated and slid by the developed biologically inspired optimization approach to generate optimal robot trajectories. This method enables the algorithm to approximate complex geometry more accurately and efficiently, facilitating smoother navigation for robotic systems. The primary objective of this study is to develop a comprehensive model for robotic path planning and navigation in complex environments, with a particular focus on enhancing efficiency, adaptability, and robustness. The proposed model is validated through extensive simulations in diverse complex environments. Comparative studies are performed against existing path planning algorithms, demonstrating the effectiveness of our approach in terms of path quality, computational efficiency, and adaptability to changing conditions