Contents I. Introduction
Today, robots are used extensively in various fields. In mining, rescue, they are used in dangerous work for humans. Safe path planning is an important function of robot navigation. In recent years, path planning techniques have been widely studied. In addition, path planning is a hot topic in robotics. Robot navigation can be divided into 2 types: global navigation and local navigation. Global path planning is used when robots know about the moving environment. Including obstacles and The destination. On the other hand, the local path planning that the robot does not have information about the environment before. In addition, the environment can change at any time. There are many global path planning algorithms. They are divided into 2 approaches. Firstly, it is called the classical approach, which works effectively in a stable environment. Cell Decomposition (CD), Roadmap Approach (RA) and Artificial Potential Field (APF) are examples of algorithms in this approach. Lastly, the reactive approach that uses algorithms like metaheuristic in processing to create paths for robots in an ever-changing environment. From the literature, found that There have been many studies on path planning [1]–[23]. In most cases, swarm intelligence is used to solve the problems. As mentioned above, path planning is an important component of robot navigation. In robotics, path planning may be defined as a process to create walking paths from the beginning to their destination without colliding with any obstacles. The obtained paths have to satisfy the specified criteria, such as the shortest distance, the shortest processing time, the least fuel consumption, etc.
