Identification and active exploration of deformable object boundary constraints through robotic manipulation

Boonvisut, Pasu; Çavuşoğlu, M. Cenk

doi:10.1177/0278364914536939

Cited by 11 publications

(9 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later, Khalil et al [ 15 ] used stereoscopic vision to build a 3D surface mesh from contours and colour in order to discover the deformation of non-rigid objects, and Leeper et al [ 16 ] used a low-cost stereo sensor mounted on the gripper to estimate grasp poses and to choose the best one according to a cost function based on points cloud features. More recently, Boonvisut et al [ 17 ] proposed an algorithm for the identification of the boundaries of deformable tissues, and to use it for both offline and online planning in robot-manipulation tasks. Moreover, Calli et al [ 18 ] presented a dataset to test method for manipulation tasks.…”

Section: Related Workmentioning

confidence: 99%

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

Mateo

Gil

Torres

2016

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Mateo

Gil

Torres

2016

Sensors

View full text Add to dashboard Cite

show abstract

“…In [13,14], they propose learning strategies based on demonstrations to get more adaptability in the system and improve the manipulation performance. In [15], boundary constraints of the deformable objects are also studied through identification and exploration of the objects. Sensory data can also be used to categorize and classify the objects depending on the values obtained while interacting with them [16].…”

Section: Introductionmentioning

confidence: 99%

Tactile control based on Gaussian images and its application in bi-manual manipulation of deformable objects

Delgado

Ramón

Mezouar

et al. 2017

Robotics and Autonomous Systems

View full text Add to dashboard Cite

show abstract

“…Limited solutions yet exist for the automated classification of deformable objects in accordance with different materials they can be made of, forming classes of elastic, plastic, elasto-plastic, or rigid objects, each category calling for different handling strategies. The characterization generally involves the approximate identification of elastic parameters of a formal model, often a mass-spring model [6,11], a finite-element (FEM) representation [6,12,13], or an elaborate surface mesh model [14,15]. Parameters are typically estimated by comparing the real and simulated object subject to interaction and aiming to minimize the differences between the two.…”

Section: Introductionmentioning

confidence: 99%

Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing

2017

View full text Add to dashboard Cite

Performing tasks with a robot hand often requires a complete knowledge of the manipulated object, including its properties (shape, rigidity, surface texture) and its location in the environment, in order to ensure safe and efficient manipulation. While well-established procedures exist for the manipulation of rigid objects, as well as several approaches for the manipulation of linear or planar deformable objects such as ropes or fabric, research addressing the characterization of deformable objects occupying a volume remains relatively limited. The paper proposes an approach for tracking the deformation of non-rigid objects under robot hand manipulation using RGB-D data. The purpose is to automatically classify deformable objects as rigid, elastic, plastic, or elasto-plastic, based on the material they are made of, and to support recognition of the category of such objects through a robotic probing process in order to enhance manipulation capabilities. The proposed approach combines advantageously classical color and depth image processing techniques and proposes a novel combination of the fast level set method with a log-polar mapping of the visual data to robustly detect and track the contour of a deformable object in a RGB-D data stream. Dynamic time warping is employed to characterize the object properties independently from the varying length of the tracked contour as the object deforms. The proposed solution achieves a classification rate over all categories of material of up to 98.3%. When integrated in the control loop of a robot hand, it can contribute to ensure stable grasp, and safe manipulation capability that will preserve the physical integrity of the object.

show abstract

Identification and active exploration of deformable object boundary constraints through robotic manipulation

Cited by 11 publications

References 52 publications

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

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

Tactile control based on Gaussian images and its application in bi-manual manipulation of deformable objects

Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing

Contact Info

Product

Resources

About