Application of a Genetic Algorithm in Planning the Optimal Route of Unmanned Aerial Vehicles Used for Large Area Monitoring

Military, construction, picture and video mapping, medical, search and rescue, parcel delivery, covert area survey, oil rig and power line monitoring are just a few of the applications for unmanned aerial vehicles (UAVs). UAVs are gaining popularity as a model for agricultural, wireless communications and aerial surveillance, as well as service and delivery. Unmanned aerial vehicle (UAV) research has attracted a lot of interest in recent decades due to the rapid growth of computer technology, autonomous control technology, and communication technology. In various industries, including aviation, unmanned systems are replacing manned systems. Due to their high success rates in military and civilian missions, unmanned aerial vehicles (UAVs), one of the most popular and successful unmanned systems, are gradually becoming an important part of the industry. air. The biggest problem with drones is finding the best trajectory in difficult conditions. It seeks to tour all control locations as efficiently as possible in order to receive information from sensors set in wide areas. In this paper, we employed a Genetic Algorithm (GA) to discover the best flight path for unmanned aerial vehicles (UAVs) in a three-dimensional environment, i.e. space. One of the important elements in the suggested strategy is solving the travelling salesman problem (TSP) to find the optimal path. In the future, we can enhance the complexity of our problem through the addition of new constraints imposed by the dynamic environment in which we operate. The experimental results suggest that GA can be used to optimize UAV path planning.