Path Planning of UAV in an Uncertain Static Environment Using Probability Fuzzy Logic

Abstract

The path planning and obstacle avoidance of Unmanned Arial Vehicle (UAV) in uncertain environment is a challenging task to perform. This is because of the greater number of control points involved in UAV navigation in a 3D environment. This article tackles a challenging issue in aerial navigation through the creation of a controller utilizing a probability fuzzy logic (PFL) methodology. The environment considered here is indoors which allows us to ignore the inertial effect of wind on UAV. The grid-based system is used (like mobile robot) for analyzing the path travelled by the UAV. The proposed controller takes the data from the sensor and processes it for decision making. The performance of the developed PFL approach has been evaluated simulation and experimental environment. The virtual environment for simulation is developed in MATLAB with some static obstacles. The results show that by using PFL controller, UAV can locate the safe and shortest path without collision in minimum time. The quality of PFL output is also compared with the other AI techniques like Ant Colony Optimization (ACO), Improved Ant Colony Optimization (IACO). The results show that PFL outperformed ACO and IACO algorithms in terms of short and smooth path finding.