scholarly journals Dexterous Manipulation of Unknown Objects Using Virtual Contact Points

Robotics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 86 ◽  
Author(s):  
Andrés Montaño ◽  
Raúl Suárez
Keyword(s):  
Physical Property ◽  
Robotic Hand ◽  
Tactile Sensors ◽  
Dexterous Manipulation ◽  
Everyday Objects ◽  
Simple Strategy ◽  
Contact Points ◽  
Different Shapes ◽  
Unknown Objects ◽  
Kinematic Information

The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.

Soft magnetic skin for super-resolution tactile sensing with force self-decoupling

2021 ◽  
Vol 6 (51) ◽  
pp. eabc8801
Author(s):  
Youcan Yan ◽  
Zhe Hu ◽  
Zhengbao Yang ◽  
Wenzhen Yuan ◽  
Chaoyang Song ◽  
...  
Keyword(s):  
External Disturbance ◽  
Super Resolution ◽  
Tactile Sensor ◽  
Human Robot Interaction ◽  
Tactile Sensors ◽  
Dexterous Manipulation ◽  
Robot Interaction ◽  
Flexible Film ◽  
Challenging Tasks ◽  
External Forces

Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction.

Visual Grasp Planning for Unknown Objects Using a Multifingered Robotic Hand

2013 ◽  
Vol 18 (3) ◽  
pp. 1050-1059 ◽  
Author(s):  
Vincenzo Lippiello ◽  
Fabio Ruggiero ◽  
Bruno Siciliano ◽  
Luigi Villani
Keyword(s):  
Robotic Hand ◽  
Grasp Planning ◽  
Unknown Objects

Calculation of Contact Forces of Large-Scale Heavy Forging Manipulator Grippers

2009 ◽  
Vol 419-420 ◽  
pp. 645-648 ◽  
Author(s):  
Qun Ming Li ◽  
Dan Gao ◽  
Hua Deng
Keyword(s):  
Large Scale ◽  
Optimization Method ◽  
Contact Forces ◽  
Robotic Hand ◽  
Closure Condition ◽  
Contact Points ◽  
Robot Hands ◽  
Calculation Results ◽  
Forging Manipulator ◽  
Gripping Force

Different from dexterous robotic hands, the gripper of heavy forging manipulator is an underconstrained mechanism whose tongs are free in a small wiggling range. However, for both a dexterous robotic hand and a heavy gripper, the force closure condition: the force and the torque equilibrium, must be satisfied without exception to maintain the grasping/gripping stability. This paper presents a gripping model for the heavy forging gripper with equivalent friction points, which is similar to a grasp model of multifingered robot hands including four contact points. A gripping force optimization method is proposed for the calculation of contact forces between gripper tongs and forged object. The comparison between the calculation results and the experimental results demonstrates the effectiveness of the proposed calculation method.

Automated Design of Robotic Hands for In-Hand Manipulation Tasks

2020 ◽  
Vol 17 (01) ◽  
pp. 1950029
Author(s):  
Christopher Hazard ◽  
Nancy Pollard ◽  
Stelian Coros
Keyword(s):  
Kinematic Model ◽  
Automated Design ◽  
Robotic Hand ◽  
Motion Synthesis ◽  
Robotic Hands ◽  
Dexterous Manipulation ◽  
Grasp Planning ◽  
User Input ◽  
High Level

Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline 32 to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term.

scholarly journals Vision for Robust Robot Manipulation

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1648 ◽  
Author(s):  
Ester Martinez-Martin ◽  
Angel del Pobil
Keyword(s):  
Robot Manipulator ◽  
Depth Information ◽  
Indoor Environments ◽  
Tactile Sensors ◽  
Recovery Stage ◽  
Depth Cameras ◽  
Contact Points ◽  
Vision Sensors ◽  
Proprioceptive Sensors ◽  
New Generation

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.

scholarly journals Fast geometry-based computation of grasping points on three-dimensional point clouds

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141983184 ◽  
Author(s):  
Brayan S Zapata-Impata ◽  
Pablo Gil ◽  
Jorge Pomares ◽  
Fernando Torres
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Service Robots ◽  
Robotic Hand ◽  
Complex Task ◽  
Changing Environments ◽  
Different Shapes ◽  
Points Of View ◽  
Partial View

Industrial and service robots deal with the complex task of grasping objects that have different shapes and which are seen from diverse points of view. In order to autonomously perform grasps, the robot must calculate where to place its robotic hand to ensure that the grasp is stable. We propose a method to find the best pair of grasping points given a three-dimensional point cloud with the partial view of an unknown object. We use a set of straightforward geometric rules to explore the cloud and propose grasping points on the surface of the object. We then adapt the pair of contacts to a multi-fingered hand used in experimentation. We prove that, after performing 500 grasps of different objects, our approach is fast, taking an average of 17.5 ms to propose contacts, while attaining a grasp success rate of 85.5%. Moreover, the method is sufficiently flexible and stable to work with objects in changing environments, such as those confronted by industrial or service robots.

scholarly journals Tactile Slippage Analysis in Optical Three-Axis Tactile Sensor for Robotic Hand

2013 ◽  
Vol 465-466 ◽  
pp. 1375-1379
Author(s):  
Hanafiah Yussof ◽  
Zahari Nur Ismarrubie ◽  
Ahmad Khushairy Makhtar ◽  
Masahiro Ohka ◽  
Siti Nora Basir
Keyword(s):  
Control Algorithm ◽  
Tactile Sensor ◽  
Robotic Hand ◽  
Robot Hand ◽  
Robot Arm ◽  
Tactile Sensors ◽  
Shear Forces ◽  
Improve Performance ◽  
Robot Arm Control ◽  
Two Phases

This paper presents experimental results of object handling motions to evaluate tactile slippage sensation in a multi fingered robot arm with optical three-axis tactile sensors installed on its two hands. The optical three-axis tactile sensor is a type of tactile sensor capable of defining normal and shear forces simultaneously. Shear force distribution is used to define slippage sensation in the robot hand system. Based on tactile slippage analysis, a new control algorithm was proposed. To improve performance during object handling motions, analysis of slippage direction is conducted. The control algorithm is classified into two phases: grasp-move-release and grasp-twist motions. Detailed explanations of the control algorithm based on the existing robot arm control system are presented. The experiment is conducted using a bottle cap, and the results reveal good performance of the proposed control algorithm to accomplish the proposed object handling motions.

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