Comparative Study of Modified Dynamic A-Star Programming and A-Star for Mobile Robot Path Planning

Navigating autonomously through complex environments is a critical capability for mobile robots, enabling them to perform various tasks in a wide range of applications. However, navigating effectively in the presence of obstacles presents a significant challenge for these intelligent systems. This paper delves into the intricacies of mobile robot navigation. At the heart of mobile robot navigation lies the ability to construct traversable paths between designated start and goal points. This intricate process involves evaluating various alternative routes and selecting the one that minimizes the overall cost, typically measured in terms of distance or time. Two prominent path planning algorithms, namely A* and modified dynamic A*, are thoroughly investigated within this paper. A* stands out as a widely used and well-established algorithm that employs a heuristic approach to efficiently find optimal paths. However, in scenarios with dynamic environments, where obstacles may appear or disappear, A* can struggle to adapt effectively. To address this limitation, a modified dynamic A* algorithm is proposed, incorporating dynamic programming techniques that enable it to adapt to changes in the environment without compromising path efficiency. To evaluate the performance of these two algorithms, a simulation environment is employed, replicating a scenario with obstacles. The results demonstrate that the modified dynamic A* algorithm consistently outperforms A* in terms of path planning efficiency.