Vision for Robust Robot Manipulation
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 recover...
| المؤلفون الرئيسيون: | , |
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| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
MDPI AG
2019-04-01
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| سلاسل: | Sensors |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www.mdpi.com/1424-8220/19/7/1648 |
| _version_ | 1828739689898573824 |
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| author | Ester Martinez-Martin Angel P. del Pobil |
| author_facet | Ester Martinez-Martin Angel P. del Pobil |
| author_sort | Ester Martinez-Martin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T00:25:50Z |
| format | Article |
| id | doaj.art-8f71a7570a2743d998e26913d901b5bc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:25:50Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8f71a7570a2743d998e26913d901b5bc2022-12-22T03:10:36ZengMDPI AGSensors1424-82202019-04-01197164810.3390/s19071648s19071648Vision for Robust Robot ManipulationEster Martinez-Martin0Angel P. del Pobil1RoViT, University of Alicante, 03690 San Vicente del Raspeig (Alicante), SpainRobInLab, Jaume I University, 12071 Castello de la Plana, SpainAdvances 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.https://www.mdpi.com/1424-8220/19/7/1648roboticsrobot manipulationdepth vision |
| spellingShingle | Ester Martinez-Martin Angel P. del Pobil Vision for Robust Robot Manipulation Sensors robotics robot manipulation depth vision |
| title | Vision for Robust Robot Manipulation |
| title_full | Vision for Robust Robot Manipulation |
| title_fullStr | Vision for Robust Robot Manipulation |
| title_full_unstemmed | Vision for Robust Robot Manipulation |
| title_short | Vision for Robust Robot Manipulation |
| title_sort | vision for robust robot manipulation |
| topic | robotics robot manipulation depth vision |
| url | https://www.mdpi.com/1424-8220/19/7/1648 |
| work_keys_str_mv | AT estermartinezmartin visionforrobustrobotmanipulation AT angelpdelpobil visionforrobustrobotmanipulation |