Improved MTAPF method for autonomous vehicles

Abstract

Path planning has evolved into one of the most crucial critical studies with the introduction of numerous autonomous vehicles. The artificial potential field approach is being used by many intelligent vehicles to plan their paths, and a variety of autonomous vehicles may avoid barriers depending on their own conditions and the movement of the obstacles. The design of dynamic real-time paths frequently makes use of artificial potential field (APF) techniques. The issue of local minima and unattainable targets in the APF approach is addressed in this study with a better solution. The global optimum path is first determined using the heuristic A-STAR path algorithm in this work, which is combined with the artificial potential field approach. The global optimal path is then divided into many sub-goals to form a sequence. The artificial potential field approach is then used to produce these targets' final routes sequentially, considerably lowering the likelihood that many unmanned vehicles may simultaneously enter a local minimum. The method could avoid the local minima problem and plan a viable path is supported by simulation results.