Analysis and optimization of path finding algorithm for unmanned aerial vehicles

The rapid growth of network systems in the current scenario requires information efficacy and transmission speed, which is a very big challenge in Unmanned Aerial Vehicle (UAV) networks. The accuracy of data and the speed of transferring information should work hand in hand without any flaws. The participation of UAVs in the applications like surveillance, security, surveying, and emergency response in rescue operations has changed the dimension of expectations from users on the subject of their efficiency and speed. Recently the process of gathering data from such applications by using machines like drones and robots became quite common. A drone can be pilot driven or autonomous where in the latter case the complexity increases in designing the system. Another discrepancy in the usage of autonomous drones is the battery power’s durability. The solution to overcome the problem is to optimize the path which will enhance the longevity of the battery. In this paper, we propose a priority path planning algorithm to achieve path optimization faster. This is achieved by two sequences of operations. A grid map is generated with occupancy values in each cell which estimates the distance to be traveled by the drone based on the input map fed by the user for the known path. The second process is to trace the shortest path from all possible routes. Third is the security and smoothness of operation. The first two techniques can be combined and called path planning. The techniques inhibited in this paper are analyzed in such a way it is useful for surveillance and surveying where the path of the drone to be traveled is known and the area to be covered is also predetermined. The simulation results and comparison charts show that the proposed algorithm is better compared to existing techniques like fuzzy logic, ant colony optimization, and particle swarm optimization techniques.