Implementation and Comparison of Path Planning Algorithms for Autonomous Navigation

In autonomous navigation, efficient path planning is critical for ensuring robust and reliable performance in dynamic environments. This paper presents a comparative study of two widely-used path planning algorithms: Dynamic Window Approach (DWA) and $A^{*}$ algorithm, among other leading algorithms, implemented using Robot Operating System (ROS) on the TurtleBot3 platform. The DWA algorithm focuses on real-time obstacle avoidance and local path optimization, while $A^{*}$ provides a global path solution based on heuristic search. Through a series of experiments in simulated and real-world scenarios, the effectiveness of the algorithms in terms of computational efficiency, path optimality, and adaptability to dynamic obstacles has been evaluated. The findings highlight the strengths and limitations of each approach, providing valuable insights for the development of autonomous systems in various applications. This study aims to contribute to the ongoing efforts in enhancing the autonomy and reliability of mobile robots. Notable enhancements in terms of travel distance, time and overall path have been recorded, and the combined approach shows significant improvement in Path Distance (approximately 3.53%), Travel Time (approximately $\mathbf{6. 6 2 \%}$), and Travel Distance (approximately $\mathbf{3 0. 1 7 \%}$).