Towards cognitive grasping: modeling of unknown objects and its corresponding grasp types

Kim, Hyoungnyoun; Han, Inkyu; You, Bum-Jae; Park, Ji‐Hyung

doi:10.1007/s11370-011-0088-5

Cited by 1 publication

(1 citation statement)

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“…In the past, visual systems were used quite successfully for the manipulation of rigid objects, both for recognition [ 10 ] and for the location of an object [ 11 ]. This developments and applications of intelligent robot manipulation involve methods and approaches aimed at achieving an object classification and recognition goal.…”

Section: Related Workmentioning

confidence: 99%

3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

Mateo

Gil

Torres

2016

Sensors

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Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object’s surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand’s fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments.

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Section: Related Workmentioning

confidence: 99%