Optimized Path Planning for Autonomous Robots in Complex Environments Using a Modified RRT Algorithm

Sampling-based motion planning algorithms such as Rapidly-Exploring Random Tree (RRT) and its variants are widely used in complex environments for autonomous robot navigation; however, they face fixed step-size limitation, inefficient exploration in narrow passages, and localisation clustering that leads to suboptimal paths and excessive iterations. This study proposes a modified RRT framework integrating (i) a dynamic step-size mechanism and (ii) a dynamically expanding circle-based sampling strategy to mitigate localization effects and improve global exploration. The proposed approach enhances both convergence efficiency and path reliability without increasing algorithmic complexity. The method is validated across seven benchmark and custom-designed maps of varying complexity and is compared against RRT, RRT*, Informed RRT*, and A*. Experimental results demonstrate success rate improvements up to 98%, consistent reduction in iteration counts compared to classical RRT variants, and competitive computational time while maintaining near-optimal path lengths. The combined dynamic step-size and dynamic circle RRT achieves superior robustness and convergence efficiency, making it suitable for autonomous robotic navigation in constrained and cluttered environments.