A Moss Growth Optimization Approach to Robot Path Planning

Safe and efficient path planning remains a key challenge for mobile robots, especially in cluttered and complex environments. Many existing methods struggle to balance global exploration and local optimization, often leading to suboptimal paths or high computational costs. This paper presents a bio-inspired Moss Growth Optimization (MGO) algorithm integrated with a graph-based approach for robotic navigation. The environment is efficiently represented using MAKLINK Graph Theory (MGT), enabling rapid initial path generation. MGO enhances global exploration by thoroughly searching the solution space, while an implicit memory mechanism supports local refinement of promising paths. To further improve search efficiency, a Line-of-Sight (LoS) Reduction technique minimizes unnecessary exploration. Simulation results and comparative evaluations show that the proposed approach achieves faster convergence and shorter paths compared to state-of-the-art methods in high-dimensional and cluttered spaces. With its nature-inspired design and memory-based updates, the MGO framework offers a robust, efficient, and scalable solution for optimal robot path planning in real-world autonomous navigation scenarios.