Open Access

ARTICLE

Steering Behavior-based Multiple RUAV Obstacle Avoidance Control

Vishnu Kumar Kaliappan¹, Tuan Anh Nguyen¹, Dugki Min^2,*, Jae-Woo Lee¹, U. Sakthi³

1 Konkuk Aerospace Design-Airworthiness Research Institute (KADA), Konkuk University, Seoul, 05029, Korea
2 Department of Computer Science and Engineering, Konkuk University, Seoul, 05029, Korea
3 Department of Computer Science and Engineering, Saveetha School of Engineering, Chennai, 602105, India

* Corresponding Author: Dugki Min. Email:

Intelligent Automation & Soft Computing 2022, 34(1), 575-591. https://doi.org/10.32604/iasc.2022.024577

Received 22 October 2021; Accepted 15 December 2021; Issue published 15 April 2022

Abstract

In recent years, the applications of rotorcraft-based unmanned aerial vehicles (RUAV) have increased rapidly. In particular, the integration of bio-inspired techniques to enhance intelligence in coordinating multiple Rotorcraft-based Unmanned Aerial Vehicles (RUAVs) has been a focus of recent research and development. Due to the limitation in intelligence, these RUAVs are restricted in flying low altitude with high maneuverability. To make it possible, the RUAVs must have the ability to avoid both static and dynamic obstacles while operating at low altitudes. Therefore, developing a state-of-the-art intelligent control algorithm is necessary to avoid low altitude obstacles and coordinate without collision while executing the desired task. This paper proposes an Artificial Intelligence (AI) based steering behavior algorithm that generates a smooth trajectory by avoiding static and dynamic obstacles. The proposed method uses an obstacle detection algorithm (ODA) and obstacle avoidance algorithm (OAA). An obstacle detection algorithm is used to detect potential threats that fall in the course of flight while reaching the desired target, and obstacle avoidance algorithms use the steering behavior method to generate avoidance trajectory. A PC104 embedded board-based HIL (Hardware in the Loop) simulation environment is developed to validate the proposed algorithm. The proposed algorithm is evaluated by experimenting with a variety of nontrivial scenarios involving static and dynamic obstacles.

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Keywords

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