Improved potential field method for unknown obstacle avoidance using UAV in indoor environment

Autonomous navigation; Collision-free path-planning; Indoor environment; Inertial sensing; PD controllers; Potential field methods; Quad rotors; Real time; Artificial Intelligence; Computer Science Applications; Information Systems

Abstract :

[en] This paper proposes a solution to real-time collision-free path planning for an AR. Drone 2.0 UAV using only on-board visual and inertial sensing. The proposed solution consists in a modified potential field method to overcome the non-reachable goal problem. The approach comprises three key components: pattern-based ground for localization, proposed potential field method for path planning and PD controllers for steering commands. By applying the proposed method, the quadrotor is successful in avoiding known/unknown obstacles and reaching the target in complex indoor environment. The results demonstrate the feasibility of the proposed strategy, which opens new possibilities for the agent to perform autonomous navigation.

Disciplines :

Aerospace & aeronautics engineering

Author, co-author :

Mac, Thi Thoa; Department of Electrical Energy, Systems and Automation (EeSA), Ghent University, Belgium ; School of Mechanical Engineering, Hanoi University of Science and Technology, Viet Nam

Copot, Cosmin; Department of Electrical Energy, Systems and Automation (EeSA), Ghent University, Belgium

Hernandez Naranjo, Jairo Andres ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Systèmes énergétiques

De Keyser, Robin; Department of Electrical Energy, Systems and Automation (EeSA), Ghent University, Belgium

Language :

English

Title :

Improved potential field method for unknown obstacle avoidance using UAV in indoor environment

Publication date :

21 January 2016

Event name :

14th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

Event organizer :

IEEE

Event place :

Herlany, Slovakia

Event date :

21-01-2016 => 23-01-2016

Audience :

International

Main work title :

SAMI 2016 - IEEE 14th International Symposium on Applied Machine Intelligence and Informatics - Proceedings

Publisher :

Institute of Electrical and Electronics Engineers Inc.

ISBN/EAN :

978-1-4673-8740-8

Peer review/Selection committee :

Peer reviewed

Additional URL :

http://xplorestaging.ieee.org/ielx7/7419133/7422973/7423032.pdf?arnumber=7423032

Available on ORBi :

since 19 January 2026

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