This paper presents an example of how a differential pressure sensor can be used to improve GRASPING of in-mold-labelling (IML) robots. In order to minimize mold-open time on injection molding machines, shorter operation time is desirable. To achieve this goal, an analysis of GRASPING labels using the in-mold-labelling robots was done with different approaches to improving the GRASPING process by measuring pressure in the chambers of the cylinder which carries the tool for GRASPING labels. The approach that uses the differential pressure sensor has proved best.

Using the soft fingers increases stability and dexterity in object GRASPING and manipulation. This is because of the enlarged contact interface between soft fingers and object. Although slippage phenomenon has crucial role in robust GRASPING and stable manipulation, in most of the previous researches in the field of finger manipulation, it is assumed that the slippage between finger and object does not occur. In this paper, slippage dynamic modeling in object GRASPING and manipulation using soft fingers is studied. Because of the enlarged contact interface between soft fingers and object, frictional moment along with tangential frictional force and normal force is applied on the contact interface. Therefore, novel method for dynamic modeling of planar slippage using the concept of Friction Limit Surface is presented. In this method, equality and inequality relations of different states of planar contact are rewritten in the form of single second-order differential equation with variable coefficients. These coefficients are determined based on the slippage conditions. This kind of dynamic modeling of contact forces can be used for designing the controllers to prevent the undesired slippage. The method is used in study of slippage analysis of three-link soft finger manipulating rigid object on horizontal surface. In order to increase the accuracy of dynamic modeling of soft finger, dynamics of soft tip is integrated with the dynamic of finger linkage. Dynamic behavior of this system is shown in the numerical simulations.

Safe GRASPING and handling delicate object with fingertips need a sophisticated control algorithm capable of position and force control in one direction. Most of position-force control algorithms proposed so far divide the work space of manipulators into two subspace and control position in one subspace and force in the other one. Meanwhile, for safe GRASPING purpose we should apply force at minimum necessary level which itself is not a known quantity during motion, so the desired value of normal force is not known forehand. This paper propose a new positionforce algorithm and employ that to accomplish pinching and moving delicate object with two multilink fingers. Performance issues and parameters robustness with respect to friction coefficient is also addressed. Several numerical examples are provided to show versatility of the proposed method.

The human hand is one of the most complex organs of the human body, capable of performing skilled tasks. Manipulation, especially GRASPING is a critical ability for robots. However, GRASPING objects by a robot hand is a challenging issue. Many researchers have used deep learning and computer vision methods to solve this problem. This paper presents a humanoid 5-degree-of-freedom robot hand for GRASPING objects. The robotic hand is made using a 3D printer and 5 servo motors are used to move the fingers. In order to simplify the robotic hand, a tendon-based transmission system was chosen that allows the robot's fingers to flexion and extension. The purpose of this article is to use deep learning algorithm to GRASPING different objects semi-automatically. In this regard, a convolutional neural network structure is trained with more than 600 images. These images were collected by a camera mounted on the robot's hand. Then, the performance of this algorithm is tested on different objects in similar conditions. Finally, the robot hand is able of successfully GRASPING with 85% accuracy.

This paper deals with the problem of safe GRASPING of an object. According to robot maneuvers during movement, the slipping or falling of the objects is possible. Here, an adaptive back stepping control method is used for controlling of slipping and tracking of desirable paths. First, the robot dynamics of GRASPING of an object including mechanical arm with three rotational joints, one prismatic joint, jaw gripper as well as dynamic of the electrical actuators is derived. Then, back stepping technique, which is a systematic approach based on Lyaponov theory, is applied for this nonlinear system. Because of existence of different uncertainties in this system such as mass and inertia of robot and object mass, it is required to design a controller to be able to cope with these uncertainties. Accordingly, a stable controller using adaptive back stepping control methodology is also designed to estimate of these parameters’ uncertainties. Stability analysis is provided based on Lyapunov theory. Simulations are carried out to evaluate the performance of the proposed controller. Results show the effectiveness of the proposed control method.

GRASPING in unstructured environments is one of the most challenging issues currently facing robotics. The inherent uncertainty about the properties of the target object and its surroundings compels the use of robot hands, which typically involve complex hands, require elaborate sensor suites, and are difficult to control. For this purpose, in this paper combining the kinematic structure of a three and two links finger for design and fabrication of robotic gripper will be evaluated. First, the challenges associated with GRASPING by careful mechanical design of gripper are analyzed. Then, the design and fabrication of a sample gripper by combining a three-links finger similar to the human index finger and a two-links finger similar to the thumb are described. In the following, the performance of this hand for GRASPING various objects will be examined. The results show that with two fingers and simple design, without the need for the complex control, c various objects can be grasped successfully. Also, the results demonstrate that compared with the previous researches and by proximity to the kinematic structure of the human hand fingers, by combining two with three link fingers this gripper will have a better performance than the previous symmetric gripper for successfully GRASPING large objects.

In this paper, an eye-in-hand stereo image-based visual serving controller for industrial 6 degrees of freedom manipulator robots is presented. The visual control algorithms mostly use the relationship between camera speed and changes in image features, to determine the end-effector movement path. One of the main problems of the classical IBVS method is the inability to estimate the distance of the object related to the camera, which requires peripheral equipment such as a laser rangefinder to estimate the depth. In this study, two cameras were mounted on the end-effector of a 6 DOF manipulator robot. The distance of the object to the camera is estimated by the equations associated with the epipolar plane, and the interaction matrix is updated at any time. For increasing response speed, the image interaction matrix was divided into two separate parts related to translational and rotational motion, and it was found that only the translational motion part is affected by distance. The control method separates the camera motion into three-stage based on pure rotation, pure translation, and hybrid motion, which has a better time response compared to the classical IBVS control methods. Additionally, a method for position prediction and trajectory estimation of the moving target in order to use in a real-time GRASPING task is proposed and developed using Recursive Least Square as the trajectory estimators in the image plane. The simulation results show that the proposed method increases the system response speed and improves the tracking performance.

Soft- tips in human fingers play a great role in object GRASPING; especially when talking about isotropic and elastic matters. In other words, stability of a typical grasp is exceedingly dependent on contact conditions. Pure rolling (without slippage) is a kind of contact constraint which is greatly tied with this feature; we have focused on modelling this type of contact in this paper where the problem of GRASPING an elastic sphere-formed ball by a pair of soft-tip parallel fingers is presented. First, by modeling objects as a mass-spring system, dynamical and kinematical equations of motion are derived. Then, a position based impedance control is developed. In order to implement and validate the suggested controller, a pair of soft-tip fingers, holding an elastic ball, is simulated in MSC ADAMS. This software begets a control block in the Simulink software as a plant of the control system. Thus, the process of GRASPING under the effect of controller can be simulated in a real time manner. The result of the simulation shows the ability of controller in appropriate tracking fingers until contact with ball is made. Moreover, the simulation results show the stability of the GRASPING process.

Object GRASPING by robot fingers with purely rolling constraints is one of the most interesting issues under consideration by many researchers. In earlier studies, the main goal was the manipulation of object under purely rolling constraints to reach the final stable configuration. In this paper, in addition to deriving kinematic and dynamic equations of the system dual fingers robot and GRASPING semicircular object on the horizontal plane with rigid hemi spherical fingertips under pure rolling constrained, we investigate object manipulation on desired path maintaining dynamics stability. Modified multiple impedance control is used for object manipulation and robot fingers by considering the required reforms in this control law. In this method multiple impedance control is performed by applying the desired behavior of the entire system, including fingers and object, and dynamics stability condition is satisfied.In this way power adjustment and that these forces arrive in the right place largely effective in minimizing slip is the fingers on the surface object. The results of simulations show the eligible object manipulation and dynamics stability by robot fingers under pure rolling grasp.