Çok Kameralı Görü Tabanlı Mobil Robot Kontrolü ve Yol Planlaması

Year 2021, Issue: 31, 890 - 905, 31.12.2021

Emrah Dönmez , Alper Özcan

https://doi.org/10.31590/ejosat.950375

Abstract

Görsel tabanlı kontrol, görüntüleme cihazlarından elde edilen görsel özellikleri kullanarak bir robotun nasıl kontrol edileceği ile ilgilidir. İki tür konfigürasyon vardır; dahili görüş yapılandırması – IVC (kamera cihazdadır) ve harici görüntü yapılandırması – EVC (kamera cihazın dışındadır). Bu çalışmada; EVC çoklu kamera konfigürasyonu altında Gauss tabanlı kontrolör ve uyarlanabilir yapay potansiyel alan (A-APF) yöntemleri ile bir mobil robot kontrolü kullanılmıştır. Belirli bir alanda mobil robotu kontrol etmek için aynı özelliklere sahip dört web kamerası aynı yükseklikten çalıştırılır. Dört kameradan alınan görüntüler ilk aşamada kesişim alanlarının ortak özelliklerine göre dikilir. Ardından, robot, hedef ve engellerle ilgili konumları ve diğer bilgileri tespit etmek için bu dikilmiş görüntü üzerinde renk tabanlı nesne tespiti gerçekleştirilir. Bir sonraki adımda robot ve hedef arasında uygun ve güvenli bir yol planı elde etmek için uyarlanabilir potansiyel alan algoritması yürütülür. Daha sonra Gauss tabanlı mobil robot denetleyicisi, robot hareketlerini yol planına göre modellemek için kullanılır. Her kontrol yinelemesinde, hareket eden robotun global konumundan elde edilen yerel konuma göre yalnızca bir kamera etkinleştirilir. Deneysel simülasyon ve gerçek dünya sonuçları, çoklu kamera konfigürasyonunun iyi performans ve verimlilik sağladığını göstermektedir.

Keywords

Görsel tabanlı kontrol, Gauss kontrolü, A-APF, Çoklu kamera, Görüntü birleştirme

Multi-Camera Vision-Based Mobile Robot Control and Path Planning

Abstract

Visual-based control concerns how to control a robot by using visual features acquired from imaging devices. There are two types of configurations; internal vision configuration – IVC (the camera is in the device) and external vision configuration – EVC (the camera is out of the device). In this study; a mobile robot control has been employed with Gaussian based controller and adaptive artificial potential field (A-APF) methods under the EVC multi-camera configuration. Four webcams with the same specifications are operated from the same height to control the mobile robot within a specific area. Images taken from the four cams are stitched according to common features of intersection areas in the first stage. Then, the color-based object detection is performed on this stitched image to detect positions and other information related to the robot, target, and obstacles. In the next step, to acquire a suitable and safe path plan between robot and target, an adaptive potential field algorithm is executed. Then, the Gaussian-based mobile robot controller is used to model the robot motions according to the path plan. In each control iteration, only one camera is activated according to the local position obtained from the global position of the moving robot. Experimental simulation and real-world results demonstrate that the multi-camera configuration provides good performance and efficiency.

Keywords

Visual-based control, Gaussian control, A-APF, Multi-camera, Image stitching

References

• Siciliano B. and Khatib O., (2008). Handsbook of Robotics. 1st ed. Berlin. Springer-Verlag.
• Dönmez, E., Kocamaz, A.F. and Dirik, M. A (2018). Vision-Based Real-Time Mobile Robot Controller Design Based on Gaussian Function for Indoor Environment. A. J. Sci. Eng. 43, 7127–7142.
• Choset H. M., (2005). Principles of Robot Motion: Theory, Algorithms, and Implementation.
• Cowan N. J., Weingarten J. D., and Koditschek D. E., (2002). Visual servoing via navigation functions. IEEE Trans. Robot. Autom, 18(4), 521–533.
• Dönmez, E., Kocamaz, A.F. (2020). Design of Mobile Robot Control Infrastructure Based on Decision Trees and Adaptive Potential Area Methods. I.J. Sci. Tech. Trans. Electr. Eng., 44, 431–448.
• Dudek G. and Jenkin M., (2010). Computational Principles of Mobile Robotics. 2nd ed. New York: Cambridge University Press.
• Tsitsiklis J. N., (1995). Efficient algorithms for globally optimal trajectories. IEEE Trans. Automat. Contr., 40(9), 1528–1538.
• Cormen T. H., Rivest R. L., and Leiserson C. E., (2001). Introduction to algorithms. MIT Press.
• Hart P., Nilsson N., and Raphael B., (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Trans. Syst. Sci. Cybern., 4(2), 00–107.
• Stentz A., (1994). Optimal and efficient path planning for partially-known environments. in Proceedings of the 1994 IEEE International Conference on Robotics and Automation, 3310–3317.
• Lavalle S. M., (1998) Rapidly-Exploring Random Trees: A New Tool for Path Planning.
• LaValle S. M. and Kuffner J. J., (1999). Randomized kinodynamic planning. in Proceedings 1999 IEEE International Conference on Robotics and Automation, 1, 473–479.
• Rimon E. and Koditschek D. E., (1992). Exact robot navigation using artificial potential functions. IEEE Trans. Robot. Autom., 8(5), 501–518.
• Malis E., Chaumette F., and Boudet S., (2000). Multi-cameras visual servoing. in Proceedings ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings, 4, 3183–3188.
• Lippiello V., Siciliano B., and Villani L., (2005) Eye-in-Hand/Eye-to-Hand Multi-Camera Visual Servoing. in Proceedings of the 44th IEEE Conference on Decision and Control, 5354–5359.
• Qiu L., Song Q., Lei J., Yu Y., and Ge Y., (2006). Multi-Camera Based Robot Visual Servoing System. in 2006 International Conference on Mechatronics and Automation, 1509–1514.
• Yoshihata Y., Watanabe K., and Iwatani Y., (2007). Multi-camera visual servoing of a micro helicopter under occlusions. in 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2615–2620.
• Iwatani, K. Y., and Hashimoto K., (2008). Multi-camera visual servoing of multiple micro helicopters. in 2008 SICE Annual Conference, 2432–2435.
• Weber B. and Kuhnlenz K., (2010). Visual servoing using triangulation with an omnidirectional multi-camera system. in 2010 11th International Conference on Control Automation Robotics & Vision, 1440–1445.
• Kermorgant O. and Chaumette F., (2011). Multi-sensor data fusion in sensor-based control: Application to multi-camera visual servoing. in 2011 IEEE International Conference on Robotics and Automation, 4518–4523.
• Elsheikh E. A., El-Bardini M. A., and Fkirin M. A., (2016). Dynamic path planning and decentralized FLC path following implementation for WMR based on visual servoing. in 2016 3rd MEC International Conference on Big Data and Smart City, 1–7.
• Aliakbarpour H., Tahri O., and Araujo H., (2014). Visual servoing of mobile robots using non-central catadioptric cameras. Rob. Auton. Syst., 62(11), 1613–1622.
• Ahlin K., Joffe B., Hu A. P., McMurray G., and Sadegh N., (2016). Autonomous Leaf Picking Using Deep Learning and Visual-Servoing. IFAC-PapersOnLine, 49(16), 177–183.
• Alepuz J. P., Emami M. R., and Pomares J., (2016). Direct image-based visual servoing of free-floating space manipulators. Aerosp. Sci. Technol., 55, 1–9.
• Donmez E., Kocamaz A. F., and Dirik M., (2016). Robot control with graph based edge measure in real time image frames. in 24th Signal Processing and Communication Application Conference (SIU), 1789–1792.
• Dirik M., Kocamaz A. F., and Donmez E., (2016). Vision-based decision tree controller design method sensorless application by using angle knowledge. 24th Signal Processing and Communication Application Conference (SIU), 1849–1852.
• Dirik M., Kocamaz A. F., and Donmez E., (2020). Visual servoing based control methods for nonholonomic mobile robot. Journal of Engineering Research, 8(2), 95–113.
• Donmez E., Kocamaz A. F., and Dirik M., (2017). Visual based path planning with adaptive artificial potential field. 25th Signal Processing and Communications Applications Conference (SIU), 1–4.
• Dirik M., Kocamaz A. F., and Donmez E., (2017). Static path planning based on visual servoing via fuzzy logic. 25th Signal Processing and Communications Applications Conference (SIU), 1–4.
• Brown and Lowe, (2003). Recognising panoramas. in Proceedings Ninth IEEE International Conference on Computer Vision, 2, 1218–1225.
• Bay H., Ess A., Tuytelaars T., and Van Gool L., (2008). Speeded-Up Robust Features (SURF),” Comput. Vis. Image Underst., 110(3) 346–359.
• Lowe G., (2004). SIFT - The Scale Invariant Feature Transform,” Int. J., 2, 91–110.
• Gonzalez R. and Woods R., (2002). Digital image processing.
• Mota C., Gomes J., and Cavalcante M. I. A., (2001). Optimal image quantization, perception and the median cut algorithm,” An. Acad. Bras. Cienc., 73(3), 2001.