Experimental evaluation of the schunk 5-Finger gripping hand for grasping tasks

Ruehl, Steffen W.; Parlitz, Christoper; Heppner, G.; Hermann, A. M.; Roennau, Arne; Dillmann, Ruediger

doi:10.1109/robio.2014.7090710

Cited by 36 publications

(11 citation statements)

References 14 publications

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“…The robot hand has 9 DOFs driven by servo motors that rotate gradually with control system commands [52]. Jiang et al have realized the design and production of a robotic hand with a fiber optic sensor that can be manipulated in many ways.…”

Section: Discussionmentioning

confidence: 99%

Motion Control of the Robot Arm Manufactured with a Three-Dimensional Printer and Hardness Detection of Objects

Aksoy

Özsoy

YÜCEL>

et al. 2022

Bilişim Teknolojileri Dergisi

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Çalışmada 3D baskı teknolojilerinden Fused Deposition Modeling (FDM) yazıcı kullanılarak robotik kol üretilmiştir. Üretilen robot kolun görüntü işleme teknikleri ve makine öğrenme algoritmaları kullanarak dokunsal algılama ve hareket planlaması araştırılmıştır. Bu çalışmanın amacı, robotik kolun kontrolsüz kuvvet uygulamasını engellemek ve dokunsal kavrama sorunlarını çözmek için görüntü işleme teknikleri ve derin öğrenme algoritmaları kullanılarak yenilikçi yaklaşımların araştırılması ve uygulanmasıdır. Bu çalışmada, CAD programı ile tasarımı gerçekleştirilmiş parçaların FDM tipi üç boyutlu yazıcı kullanılarak katı modelleri alınmış ve montaj için uygun hale getirilmiştir. Montajı tamamlanan robotik elin kontrol sistemi ise temel olarak Raspberry Pi kontrol kartı, servo motorlar, basınç sesörleri ve kameradan oluşmaktadır. Robotik kola ait her parmak ucuna yerleştirilen basınç sensörleri ile ürünün sertliği ölçülerek dokunsal algılama işlemi gerçekleştirilmiştir. Raspberrry pi kontrol kartı kullanılarak sensörlerden alınan veriler işlenmekte ve servo motorlara uygun hareket ve kavrama basınç bilgisi gönderilmektedir. Kamera kullanılarak elde edilen insan elinin olası hareketleri ile robotik kol için referans bir veri seti hazırlanmıştır. Veri setine ait görüntüler üzerinde Gaussian filtreleme yöntemi kullanılarak görüntü işleme sağlanmıştır. Bununla birlikte veri seti üzerinde makine öğrenme algoritmaları kullanarak robotik kolun hareket açısal konumu optimize edilmiş ve HitNet, CNN, Kapsül Ağları ve Naive Bayes derin öğrenme modelleri kullanılarak robot kolun hareket planlanması %90 doğruluk oranı ile sınıflandırılmıştır. Performans değerlendirme kriterlerine göre başarıları kıyaslanan derin öğrenme modelleri arasında, robotik kolun hareket planlaması için; HitNET algoritması ile 97.23%, CNN ile 97.48%, Capsnet algoritması ile %98,58 ve Naive Bayes modeli ile %98.61 doğruluk oranı elde edilmiştir. Performans değerlendirme kriterleri sonucunda; Naive Bayes modelinin %98.61 doğruluk, %98.63 özgüllük, %98.65 duyarlılık, 1.39 hata oranı ve %68.64 F-ölçüsü değeri ile diğer modellere göre daha başarılı sonuç verdiği gözlemlenmiştir.

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Section: Discussionmentioning

confidence: 99%

Motion Control of the Robot Arm Manufactured with a Three-Dimensional Printer and Hardness Detection of Objects

Aksoy

Özsoy

YÜCEL>

et al. 2022

Bilişim Teknolojileri Dergisi

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“…Autonomous manipulation functionalities for arm movements and grasping further support the operator. To increase manipulation capabilities, we utilize an anthropomorphic Schunk hand (Ruehl et al, 2014) and a flexible SoftHand (Catalano et al, 2014) with complementary characteristics. Finally, a simulation-based visualization system provides an operator interface with high flexibility.…”

Section: Related Workmentioning

confidence: 99%

Remote mobile manipulation with the centauro robot: Full‐body telepresence and autonomous operator assistance

Klamt

Schwarz

Lenz

et al. 2019

Journal of Field Robotics

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Solving mobile manipulation tasks in inaccessible and dangerous environments is an important application of robots to support humans. Example domains are construction and maintenance of manned and unmanned stations on the moon and other planets. Suitable platforms require flexible and robust hardware, a locomotion approach that allows for navigating a wide variety of terrains, dexterous manipulation capabilities, and respective user interfaces. We present the CENTAURO system which has been designed for these requirements and consists of the Centauro robot and a set of advanced operator interfaces with complementary strength enabling the system to solve a wide range of realistic mobile manipulation tasks. The robot possesses a centaur-like body plan and is driven by torquecontrolled compliant actuators. Four articulated legs ending in steerable wheels allow for omnidirectional driving as well as for making steps. An anthropomorphic upper body with two arms ending in five-finger hands enables human-like manipulation. The robot perceives its environment through a suite of multimodal sensors. The resulting platform complexity goes beyond the complexity of most known systems which puts the focus on a suitable operator interface. An operator can control the robot through a telepresence suit, which allows for flexibly solving a large variety of mobile manipulation tasks. Locomotion and manipulation functionalities on different levels of autonomy support the operation. The proposed user interfaces enable solving a wide variety of tasks without previous task-specific training. The integrated system is evaluated in numerous teleoperated experiments that are described along with lessons learned. 3D laser scanner Cameras RGB-D sensor 7 DoF arm 9 DoF dexterous hand 1 DoF soft hand 5 DoF leg 360°steerable wheel Base with CPUs, router and battery Figure 1: The Centauro robot. IntroductionCapable mobile manipulation robots are desperately needed in environments which are inaccessible or dangerous for humans. Missions include construction and maintenance of manned and unmanned stations, as well as exploration of unknown environments on the moon and other planets. Furthermore, such systems can be employed in search and rescue missions on earth. It applies to all these missions that human deployment is impossible or dangerous, and depends on extensive logistical and financial effort.To address the wide range of possible tasks, a suitable platform needs to provide a wide range of capabilities. Regarding locomotion, exemplary tasks are to overcome a variety of obstacles which can occur on planetary surfaces and in man-made environments, e.g., in space stations. Regarding manipulation, tasks may be to use power tools, to physically connect and disconnect objects such as electrical plugs, or to scan surfaces, e.g., for radiation. Since maintenance is not possible during missions, a high hardware and software reliability is necessary. Furthermore, suitable operator interfaces are key to enable the control of a system that must solve suc...

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“…The anthropomorphic upper body consists of a torso yaw joint and two 7-DoF arms ending in end-effectors with different capabilities. The right end-effector is a 9-DoF anthropomorphic Schunk hand which allows for dexterous, human-like manipulation [16]. The left end-effector is a flexible 1-DoF SoftHand which can be used for robust manipulation [17].…”

Section: Hardwarementioning

confidence: 99%

Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-Like Robot

Klamt

Rodríguez

Schwarz

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities. Since the tasks are mostly unknown in advance, the robot has to adapt to a wide variety of terrains and workspaces during a mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and an anthropomorphic upper body to carry out complex tasks in environments too dangerous for humans. Due to its high number of degrees of freedom, controlling the robot with direct teleoperation approaches is challenging and exhausting. Supervised autonomy approaches are promising to increase quality and speed of control while keeping the flexibility to solve unknown tasks. We developed a set of operator assistance functionalities with different levels of autonomy to control the robot for challenging locomotion and manipulation tasks. The integrated system was evaluated in disaster response scenarios and showed promising performance. 2 https://newsroom.toyota.co.jp/en/download/ 20110424 arXiv:1809.06802v2 [cs.RO]

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Experimental evaluation of the schunk 5-Finger gripping hand for grasping tasks

Cited by 36 publications

References 14 publications

Motion Control of the Robot Arm Manufactured with a Three-Dimensional Printer and Hardness Detection of Objects

Motion Control of the Robot Arm Manufactured with a Three-Dimensional Printer and Hardness Detection of Objects

Remote mobile manipulation with the centauro robot: Full‐body telepresence and autonomous operator assistance

Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-Like Robot

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