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Open AccessArticle

Vision for Robust Robot Manipulation

1
RoViT, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain
2
RobInLab, Jaume I University, 12071 Castello de la Plana, Spain
3
Interaction Science Dept., Sungkyunkwan University, Jongno-Gu, Seoul 110-745, Korea
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(7), 1648; https://doi.org/10.3390/s19071648
Received: 24 December 2018 / Revised: 1 April 2019 / Accepted: 3 April 2019 / Published: 6 April 2019
(This article belongs to the Special Issue Visual Sensors)
Advances in Robotics are leading to a new generation of assistant robots working in ordinary, domestic settings. This evolution raises new challenges in the tasks to be accomplished by the robots. This is the case for object manipulation where the detect-approach-grasp loop requires a robust recovery stage, especially when the held object slides. Several proprioceptive sensors have been developed in the last decades, such as tactile sensors or contact switches, that can be used for that purpose; nevertheless, their implementation may considerably restrict the gripper’s flexibility and functionality, increasing their cost and complexity. Alternatively, vision can be used since it is an undoubtedly rich source of information, and in particular, depth vision sensors. We present an approach based on depth cameras to robustly evaluate the manipulation success, continuously reporting about any object loss and, consequently, allowing it to robustly recover from this situation. For that, a Lab-colour segmentation allows the robot to identify potential robot manipulators in the image. Then, the depth information is used to detect any edge resulting from two-object contact. The combination of those techniques allows the robot to accurately detect the presence or absence of contact points between the robot manipulator and a held object. An experimental evaluation in realistic indoor environments supports our approach. View Full-Text
Keywords: robotics; robot manipulation; depth vision robotics; robot manipulation; depth vision
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Martinez-Martin, E.; del Pobil, A.P. Vision for Robust Robot Manipulation. Sensors 2019, 19, 1648.

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