Görsel Geri Beslemeli Otonom Mobil Kurtarma Robotunun Tasarım, Üretim ve Kontrolü

Year 2022, Issue: 37, 65 - 71, 15.07.2022

Ibrahim Hasan , Tolgay Kara

https://doi.org/10.31590/ejosat.1133067

Abstract

Bilinmeyen ortamlarda çalışabilen akıllı otonom mobil robot sistemi üretimi zorluklar içerebilir. Otonom mobil robot geliştirilmesi için çevreyi tanımlamak amacıyla algılayıcılar ve kameradan veri toplanması, mevcut köşe noktalarının bilinmesi ve robotun konumu ve yönünün anlaşılması gerekir. Son yıllarda mobil robotların otonom hareketi ve nesne belirleme hedefleri için yazılmış algoritmalar sunan çok sayıda araştırma yapılmıştır. Robot hareket tasarımı için en yaygın kullanılan platform ROS (Robot İşletim Sistemi) iken, nesne belirleme içinse YOLO (You Only Look Once) genel kabul görmektedir. Ek olarak, otonom sistemlerde lidar ve diğer algılama yöntemleriyle engeller içeren ortamlarda hareket kontrolü için gereken geri besleme işaretini üretmek üzere sistematik hareket planlamaya ihtiyaç duyulmaktadır. Mobil robotlar için en etkili ve çok yönlü geri beslemeli sistemlerden biri, yüksek çözünürlüklü kamerayla gerçek zamanlı görüntü işleme ve geri beslemeli kontrol karar işareti üretimine dayanan görsel geri besleme yoluyla imal edilebilir. Görsel geri besleme otonom mobil robotların algılayıcılar olmadan geri beslemeli kontrolü için kullanılabileceği gibi, hareket kontrolüyle birlikte yol planlama, engelden kaçınma, nesne belirleme, nesne tanıma gibi çoklu görevlerde de kullanılabilir. Bu çalışmada, akan görüntü verisi kullanılarak kurban / kazazedeleri bulmaya yarayan otonom mobil robot sisteminin tasarım ve üretimi ile ilgili sonuçlar sunulmaktadır. Burada kamera görüntüsünü hem veri toplama hem de otonom mobil robot sisteminde geri besleme işaretini üretme amacıyla kullanarak kurban / kazazedeleri bulmak amaçlanmıştır. Görsel geri besleme ve YOLO v3 sadece kurban / kazazedeleri bulmak için değil aynı zamanda hareket kontrolü için kullanılmaktadır. Ürettiğimiz otonom mobil robot, ROS küresel planlayıcı ile kurban / kazazede yönüne doğru yumuşak bir hareketle ilerlemek ve engellerden kaçınmak için tasarlanmıştır. Robot farklı engel ve kurban / kazazede sayları için değişken ortamlardaki senaryolar için test edilmiş ve yüksek başarımlı tatmin edici sonuçlar elde edilmiştir.

Keywords

Otonom mobil robot, ROS, YOLO, Hareket planlama, Engelden kaçınma, LIDAR, Görsel geri beslemeli sistem

Design, Construction and Control of an Autonomous Mobile Rescue Robot with Visual Feedback

Abstract

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.

Keywords

Autonomous mobile robot, ROS, YOLO, Motion planning, Obstacle avoidance, LIDAR, Visual feedback system

References

• Alatise, M. B., & Hancke, G. P. (2020). A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access, 8, 39830-39846.
• Azeta, J. B. (2019). An android based mobile robot for monitoring and surveillance. Procedia Manufacturing, p. 6. Azeta, J., Bolu, C. A., Hinvi, D., Abioye, A. A., Boyo, H., Anakhu, P., & Onwordi, P. (2019). An android based mobile robot for monitoring and surveillance. Procedia Manufacturing, 35, 1129-1134.
• BALDEMİR, Y., İYİGÜN, S., MUSAYEV, O., & Cenk, U. L. U. (2020). Design and Development of a Mobile Robot for Search and Rescue Operations in Debris. International Journal of Applied Mathematics Electronics and Computers, 8(4), 133-137.
• Bhondve, T. B., Satyanarayan, R., & Mukhedkar, M. (2014). Mobile rescue robot for human body detection in rescue operation of disaster. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(6), 9876-9882.
• Bishop, B. E., Crabbe, F. L., & Hudock, B. M. (2005). Design of a low-cost, highly mobile urban search and rescue robot. Advanced Robotics, 19(8), 879-899.
• Dissanayake, G., Paxman, J., Miro, J. V., Thane, O., & Thi, H. T. (2006, August). Robotics for urban search and rescue. In First international conference on industrial and information systems (pp. 294-298). IEEE.
• Gelan, A. A. (2019). AUTONOMOUS SEARCH AND RESCUE ROBOT USING ROS PLATFORM. NEAR EAST UNIVERSITY. Habibian, S., Dadvar, M., Peykari, B., Hosseini, A., Salehzadeh, M. H., Hosseini, A. H., & Najafi, F. (2021). Design and implementation of a maxi-sized mobile robot (Karo) for rescue missions. Robomech Journal, 8(1), 1-33.
• Huang, A. S., Bachrach, A., Henry, P., Krainin, M., Maturana, D., Fox, D., & Roy, N. (2017). Visual odometry and mapping for autonomous flight using an RGB-D camera. In Robotics Research (pp. 235-252). Springer, Cham.
• Iuga, C., Drăgan, P., & Bușoniu, L. (2018). Fall monitoring and detection for at-risk persons using a UAV. IFAC-PapersOnLine, 51(10), 199-204.
• Kayisli, K., Tezel, C., Gunay, O., & Demir, E. (2017). Design and Implementation of a Tank Rescue Robot Controlled with Android Software. International Journal of Engineering Science and Application, 1(1), 18-23.
• Kececi, E. F. (2009). Design and prototype of mobile robots for rescue operations. Robotica, 27(5), 729-737. Koubâa, A. (Ed.). (2017). Robot Operating System (ROS) (Vol. 1, pp. 112-156). Cham: Springer.
• Lang, H., Khan, M. T., Tan, K. K., & de Silva, C. W. (2016). Application of visual servo control in autonomous mobile rescue robots. International Journal of Computers Communications & Control, 11(5), 685-696.
• Liu, Y., Zhong, Y., Chen, X., Wang, P., Lu, H., Xiao, J., & Zhang, H. (2016, August). The design of a fully autonomous robot system for urban search and rescue. In 2016 IEEE International Conference on Information and Automation (ICIA) (pp. 1206-1211). IEEE.
• Moosavian, S. A. A., Semsarilar, H., & Kalantari, A. (2006, October). Design and manufacturing of a mobile rescue robot. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3982-3987). IEEE.
• O'Kane, J. M. (2014). A gentle introduction to ROS.
• Quigley, M., Gerkey, B., & Smart, W. D. (2015). Programming Robots with ROS: a practical introduction to the Robot Operating System. " O'Reilly Media, Inc.".
• Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
• Rubio, F., Valero, F., & Llopis-Albert, C. (2019). A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems, 16(2), 1729881419839596.
• Saputra, R. P., & Kormushev, P. (2018, July). Resqbot: A mobile rescue robot with immersive teleperception for casualty extraction. In Annual Conference Towards Autonomous Robotic Systems (pp. 209-220). Springer, Cham.
• Saputra, R. P., & Kormushev, P. (2018, July). Resqbot: A mobile rescue robot with immersive teleperception for casualty extraction. In Annual Conference Towards Autonomous Robotic Systems (pp. 209-220). Springer, Cham.
• Sucuoglu, H. S., Bogrekci, I., & Demircioglu, P. (2018). Development of mobile robot with sensor fusion fire detection unit. IFAC-PapersOnLine, 51(30), 430-435.
• Tyugin, D. Y., Belyakov, V. V., Kurkin, A. A., Zeziulin, D. V., & Filatov, V. I. (2019). Development of the ground mobile robot with adaptive agility systems. Procedia Computer Science, 150, 287-293.
• Zhang, X., Lai, J., Xu, D., Li, H., & Fu, M. (2020). 2D LiDAR-based SLAM and path planning for indoor rescue using mobile robots. Journal of Advanced Transportation, 2020.