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

Abstract

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.

A Manipulation Control Strategy for Granular Materials Based on a Gaussian Mixture Model

“…The most straightforward approach to move a robot is to design a velocity controller which relates the error function variations during timeė with the robot velocity V = [υ, ω], Chaummette and Hutchinson in [19]. Formally, this is written asė = ∇eV, where ∇e is the gradient of the error function e, υ ∈ R 3 is the linear velocity and ω ∈ R 3 the angular velocity, thus the velocity vector is in V ∈ R 6 .…”

“…Works [4,5] by Mateo et al present a method for understanding how the surface changes during manipulation task, implementing a Dijkstra-based method to model the deformation of the object. The authors also present a method in [6] to predict when the object surface changes drastically during manipulation tasks to achieve dexterous manipulation and prevent damages in objects. The previous cited works are focused mainly in the perception of the objects, but works more related to the control of robots for the manipulation of elastic objects are [7,8] by Navarro-Alarcon, in which an strategy to control the shape of objects with robots, using Fourier transform as features, is presented.…”

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human–Robot Interaction

“…The use of a tactile sensor inside the gripper provides useful information for gasping [ 23 , 24 ], object recognition [ 25 ], and manipulation [ 26 , 27 ] tasks. This problem has not yet been solved [ 28 ].…”

The Integration of the Image Sensor with a 3-DOF Pneumatic Parallel Manipulator

“…The system can determine the location of the end-effector in the three-dimensional Cartesian coordinate system. In 2016, the 3D visual data-driven spatiotemporal deformations for non-rigid object gasping using robot hands was introduced by Mateo et al [ 4 ]. The experiments show that the proposed method can grasp several objects in various configurations.…”