Çok Kameralı Görü Tabanlı Mobil Robot Kontrolü ve Yol Planlaması

Öz

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.

Anahtar Kelimeler

• 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

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