Open AccessNavigation of Mini Unmanned Aerial Vehicle in Unknown Environment
Author(s) -
Baqir N. Abdul Samed,
Ammar A. Aldair
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/745/1/012014
Subject(s) - computer science , obstacle , trajectory , matlab , computer vision , path (computing) , motion planning , obstacle avoidance , controller (irrigation) , block (permutation group theory) , collision avoidance , global positioning system , algorithm , collision , real time computing , artificial intelligence , robot , mobile robot , mathematics , telecommunications , agronomy , physics , geometry , computer security , astronomy , political science , law , biology , programming language , operating system
In this paper, two algorithms are proposed to navigate the mini unmanned aerial vehicle (MUAV) in unknown environment. The first algorithm is path planning algorithm, it proposed to force the MUAV to track a specific trajectory. The second algorithm is obstacle avoidance algorithm, it proposed to avoid the collision between the MUAV and borrower when the MUAV is maneuvering in unknown environment. The pure pursuit algorithm (PPA) is used to force the MUAV to follow the desired trajectory depends on the given waypoints. While the vector field histogram algorithm (VFHA) is used to avoid collision of the MUAV with obstacles block in the desired path. To keep the flight of MUAV without collision, the 3D laser sensors are used to collect the information from the surrounding environment. The GPS sensor is used to keep the MUAV on the specific path. The collected information from the two sensors are analysed by the controller of the MUAV to improve its performance. The Matlab program is used to implement the proposed algorithms and plot the trajectories of MUAV in the unknown algorithm. The Matlab program is connected with the gazebo program via robotic operating system (ROS) to simulate the visual movement of the MUAV in any unknown environment. The obtained results show the effectiveness of the proposed algorithm to flight the MUAV in different environments.