Path planning of Mobile Robot in Dynamic Environment by Evolutionary Algorithms

Abstract

Mobile robot route planning is the process of identifying the ideal path for a robot to take in order to accomplish a particular objective. It is a crucial consideration of mobile robotics since it helps robots to navigate their environment and execute tasks in a safe and efficient manner. Path planning for mobile robots presents numerous difficulties, including the need to take into account the robot's physical capabilities and constraints, the presence of static obstacles and other dynamic elements of the environment, and the need to optimize for factors such as energy efficiency and time. Path planning is crucial for the successful deployment of mobile robots in a range of applications, including search and rescue, transportation, inspection, and production. It can also play a crucial role in enabling robots to function in collaborative contexts alongside people. Inspired by natural evolution and genetics, evolutionary algorithms are a kind of optimization technique. They are widely utilized in numerous fields, including mobile robot path planning. Evolutionary algorithms have the potential to find solutions to difficult, nonlinear problems, which is a major advantage for mobile robot path planning. Evolutionary algorithms are able to avoid local minima and produce globally optimal solutions, whereas conventional optimization approaches frequently become trapped in unsatisfactory solutions. This is especially crucial in real-world contexts where the robot may encounter unforeseen impediments or environmental changes. Using the ant colony optimization algorithm as an efficient evolutionary approach, this work seeks to determine the path of a mobile robot in an environment containing a number of static and dynamic obstacles. The presented model demonstrated that ACO is an effective real-time calculating solution for this type of problem.