Navigating urban skies: obstacle avoidance strategies for quadrotor MAVs

• Main Authors: Pebrianti, Dwi, Wong, Jack Kee, Bayuaji, Luhur, Solichin, Achmad
• Format: Article
• Language: English
• Published: IOP Publishing Ltd. 2024
• Subjects: T Technology (General); T175 Industrial research. Research and development; TJ Mechanical engineering and machinery; TJ181 Mechanical movements; TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General); TJ212 Control engineering
• Online Access: http://irep.iium.edu.my/116513/1/2024%20urban%20sky%20Pebrianti_2024_J._Phys.__Conf._Ser._2866_012039.pdf; http://irep.iium.edu.my/116513/; https://iopscience.iop.org/journal/1742-6596

Micro Aerial Vehicles (MAVs) has gained attentions since more than two decades ago starting from the applications in air combat up to civil applications such as packet deliveries, environmental monitoring, and surveillance. In an environment such as cities that grows denser, navigation and control for drones becomes challenging to ensure safe navigation around buildings and other obstacles. This study proposes an approach for obstacle avoidance for MAVs by using ultrasonic sensors. Four sensors are strategically positioned to cover the front, right, back, and left directions. Additionally, a downward-facing sensor measures the quadrotor’s height above ground. Our goal is to develop autonomous MAV that can avoid obstacles, ensuring safe flight even in complex urban landscapes. The scenario implemented in the study is by introducing obstacle in any directions. When an obstacle is detected by the ultrasonic sensor, the signal will be sent to microcontroller and the attitude of the MAVs, roll or pitch will be adjusted to avoid the obstacle by moving to the counter direction of the obstacle. We conducted 20 trials of experiments by varying the gain values of Proportional Integral Derivative (PID) values, we fine-tune our obstacle avoidance algorithm. Modifications include optimizing roll and pitch adjustments, refining detection height thresholds, and implementing countermeasures after obstacle clearance. The results show that our proposed method has 10% overshoot when detecting any obstacles in different directions to avoid the obstacles. Our findings contribute to the advancement of safe and efficient urban drone operations, bridging the gap between technology and real-world challenges.