Navigation Control of Unmanned Aerial Vehicles in Dynamic Collaborative Indoor Environment Using Probability Fuzzy Logic Approach

Abstract

The developing use of drones in various applications makes it essential to address the critical issue of providing its collision free and optimal navigation in uncertain environments. The aim of the current research work is the development, simulation and experimentation of the Probability Fuzzy Logic (PFL) controller for route planning and obstacle avoidance for drones in uncertain static and dynamic environment. The methodology includes development and implementation of a PFL based controller to plan drone paths in static and dynamic environments. The fuzzy logic system takes in input about distance of objects from the drone's front, left, and right sides, as well as the probability of collision based on the drone's speed and how close it is to the obstacles. The set of thirty fuzzy rules based on the distance of obstacle from front left and right are defined to decide the output i.e. speed of drone and heading angle. The simulation environment is developed using MATLAB, with grid-based motion planning those accounts for both static and dynamic obstacles. The system’s performance is validated through both simulations and real-world experiments, comparing factors like path length and travel time. On comparing the simulation and experimental result, the proposed PFL based controller is proved to be an efficient, accurate and robust for both static and dynamics and from simple to complex environment. The drones can plan the shortest and collision free path across all the scenarios as depicted in the simulation and experimentation results. However, due to communication delay, inaccuracy of sensor response, environmental impact and motor delay there are slight deviation between the simulation and experimentation values. Upon performing the error analysis, it is found that the error between the simulation and experimental value is within range of 6.66% in all the studied scenarios.

Keywords: Probability, fuzzy logic, path planning, aerial navigation, drone