Enhanced Global Path Planning Efficiency by Bidirectional A*, Gradient Descent, and Orientation Interpolation Algorithms

Abstract

This paper presents an innovative approach to global path planning (GPP) that uniquely integrates an enhanced Bidirectional A* search algorithm, gradient descent smoothing, and orientation filter interpolation.These methods are applied within a quadratic approximation-based potential field and global cost map.The key idea is successfully integrating these powerful techniques, ensuring minimal impact on computational time while enhancing path size and smoothness.Rigorous testing in environments like the Amazon web services (AWS) RoboMaker Hospital Gazebo environment and the sections at the control and systems engineering building at Iraq's University of Technology (CSE-UOT) in Iraq shows significant improvement over existing planners like wavefront GPP and robot operating system (ROS) default GPP.The proposed algorithm reduced pathfinding time by approximately 60.3% and path size by 9.0%.These findings highlight the efficiency of the proposed global planner, suggesting it as a robust solution for autonomous robotic navigation in complex architectural scenarios.