Routing of vehicles by intelligent algorithms in the matter of transporting goods
Hadi Raeesi, Amirhossein Hosseinpour · 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) · 2022
The task of route planning for a mobile robot or vehicle has attracted the attention of many research papers. In these writings, it is mostly assumed that the robot has a complete and accurate model of its surroundings before moving. Less attention has been paid to the issue of unknown environments. This is the case for a crawler or robot that has to move towards a target without a map of the area, such as vehicle route planning. Traditional intelligent Hard-NP optimization algorithms occur because this has been proven to be of the type they have not had much success in it. As a result, various innovative methods have been used to solve these problems. In the meantime, evolutionary methods are used as a population-based method with an increasing trend. On the other hand, for routing mobile robots, when the working space of the robot is clear, routing for the mobile robot is done in a global mode and offline. If the specifications of the environment are not known, routing is done locally and on the line by taking information from the sensors. In this research, the problem of routing a mobile robot in an unknown environment with two-dimensional dynamic barriers is addressed. Used off-line. Comparison and evaluation of the simulation results of the proposed evolutionary algorithms indicate that the robot guidance from the obtained path has the optimal criteria, in other words, the selected path length has the lowest cost and is much closer to the shortest possible path length.