Intelligent hybrid path finding of mobile robots using modified pelican and quadratic parametric equations based on D* algorithm solution

A mobile robot's main objective is to reach its intended destination by traversing an optimal path determined by factors such as time, distance, and safety from potential impediments. Therefore, smooth pathfinding and obstacle avoidance are core functionalities of autonomous mobile robots (AMRs). Numerous academics have proposed a wide range of path planning and obstacle avoidance algorithms. However, these methods often face challenges in complex, dynamic environments requiring both local and global optimization. This work presents a novel hybrid path-finding method that integrates the strengths of the D* algorithm with a Modified Pelican Optimization Algorithm (MPOA) and quadratic parametric equations (QPE) for mobile robots. The proposed approach leverages the global optimization capabilities of MPOA and QPE to enhance path-finding efficiency, along with the adaptive and dynamic nature of the D* algorithm for real-time replanning. The comparison of simulation results to conventional approaches shows substantial progress in path-planning computation, especially adaptivity and functional scenarios. The validity and ability of the proposed method are also illustrated from the comparison of its performance with other path planning algorithms based on the optimization of the functions of cost and achieving the shortest smooth path length. the methods are tested in a static obstacles environment using the Python package.