Hyperstatic grasping optimization with finger deformability and sliding constraints

Optimization of grasping forces with finger deformability and joint parameter variation constraints

This paper presents a tele-control system constructed from a multi-fingered robot hand and operator. The angle of the robot hand is controlled by the angle of the operator’s finger, and the operator feels the environmental force, as detected by the robot hand, constituting so-called bilateral master/slave control.

To achieve a successful grasp, gripper attributes such as its geometry and kinematics play a role as important as the object geometry. The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand. We propose UniGrasp, an efficient data-driven grasp synthesis method that considers both the object geometry and gripper attributes as inputs. UniGrasp is based on a novel deep neural network architecture that selects sets of contact points from the input point cloud of the object. The proposed model is trained on a large dataset to produce contact points that are in force closure and reachable by the robot hand. By using contact points as output, we can transfer between a diverse set of multifingered robotic hands. Our model produces over 90% valid contact points in Top10 predictions in simulation and more than 90% successful grasps in real world experiments for various known two-fingered and three-fingered grippers. Our model also achieves 93%, 83% and 90% successful grasps in real world experiments for an unseen two-fingered gripper and two unseen multi-fingered anthropomorphic robotic hands.

Optimization of grasping forces in handling of brittle objects

This paper presents a tele-control system constructed from a multi-fingered robot hand and operator. The angle of the robot hand is controlled by the angle of the operator's finger, and the operator feels the environmental force, as detected by the robot hand, constituting so-called bilateral master/slave control. In the experiments, the operator grasped the object in spite of Round Trip Time (RTT) as Osec, 0.56sec, using a multi-fingered humanoid robot hand by master/slave control feeling fingertip force. However, with increases in the RTT, the operation became more difficult. We also analyzed the stability of the master site and the slave site by frequency characteristics. The results showed that this system was unstable. However, grasping by tele-control with a communication delay was demonstrated.

A multi-finger robotic hand with an iris mechanism that we previously developed was driven by a single actuator and could grasp an object by wrapping fingers completely around its circumference at multiple points. However, the blades used to grasp objects were within the robotic hand mechanism, so it could only grasp objects small enough to fit within the hollow disk comprising the outer surface of the device body. Furthermore, the hand could not grasp objects smaller than the thickness of the hollow disk. The multi-fingered robotic hand proposed in this study has a new mechanism in which the blades of the iris are placed outside of the hand mechanism, and fingers shaped as equilateral triangular prisms extend perpendicular to the disk of the robotic hand body and are attached to the blade tip. Placing the blade outside the mechanism and adjusting the gear ratios within allows adjustments to the gripping torque and speed. The vertically extended fingers can thus grasp small objects and objects longer than the blade diameter. In this study, we performed geometric and theoretical analyses of the proposed multi-fingered robotic hand. We then fabricated an actual robotic hand, verified the validity of the analyses.

Based on inspiration of human grasping activities, a new idea is developed in this paper that grasping forces in a multifingered robotic hand can be regulated and controlled through its compliance by actively coordinating small joint motions in its fingers. According to this idea, a grasping force control model is formulated by means of a compliance model developed by the authors before, and a novel theory is then developed for grasping force control in a multifingered robot hand. The developed theory is expected to lead to a new force control method which could serve as a promising alternative for the active stiffness method. As an application of the developed theory, a two-fingered planar robotic hand is also analyzed, and the simulation results verify the developed theory.

Multi-finger Robotic hands (MFRH) are desired similar to human hands in order to perform stable grasping and fine manipulation of different objects. Their industrial applications including material handling fulfills the requirement of unique end-effector tool empowering specific reach, payloads, and flexibility. The design and control of dexterous and prosthetic robotic hands is of important concern these days. The performance of these hands depends on their mechanical design, prosthetics etc. The mechanical range of movement must be properly controlled and monitored to get the best performance of the robotic hand. In order to obtain the desired outcome from these robotic hands, various design parameters are discussed. The control issues of the multi-finger hand-arm system in order to interact with the human environment are also discussed. The objective of this paper is to evaluate multi-finger robotic hands capable of grasping a large variety of products. An overview of the relations between the designing features for the robotic hand, its anthropomorphism and dexterity is reported. Also, the best known robotic hands developed so far are reviewed emphasizing on their ergonomics and mechanical features. Based on these parameters, a newly designed four fingered tendon actuated robotic hand is discussed along with its mechanical structure.

This paper proposes a grasping stiffness control method using a multi-fingered robot hand. This method does not need any information of a grasped object, such as geometry and attitude. The grasping stiffness is derived from geometrical constraints including rolling contact between a multi-fingered robot hand and a grasped object. To estimate the grasping stiffness without the use of external sensors, a contact point position of each fingertip is estimated by a relative relationship between the initial contact position and a virtual object frame. The proposed method is designed so that the desired grasping stiffness optimally transforms into a joint stiffness, and the desired joint stiffness is realized through each joint angle control. Finally, the usefulness of the proposed method is demonstrated through several numerical simulation results.

This paper presents a massage motion control system comprised of position control and force control in a multi-fingered robot hand. By making use of an algorithm which converted the desired fingertip trajectory into the desired angle of links in each finger by means of inverse kinematics, the finger position control from the initial position of the multi-fingered robot hand to a target position of the objects for massage was achieved. Its controller was used until the robot hand contacted the objects for massage. After contact was made, the fingertip position control was switched to a force control position needed to apply pressure for the massage. The fingertip forces exerted by an expert human therapist was measured using sheet distribution pressure sensors, and the data obtained was recorded in a computer. After the measurements were taken, the human expert's fingertip force was reproduced by the robot hand. The fingertip force of the robot hand was controlled using feedback obtained with a 6-axis force sensor. To make the force of each fingertip of the four-fingered robot hand track to the fingertip force exerted by the expert human massage therapist, PI servo compensation and a Jacobian matrix were really applied for the human's shoulder. Through simulation and experiments, the usefulness of the proposed control systems was demonstrated.

Hybrid Impedance Control of Human Skin Muscle by Multi-fingered Robot Hand

The purpose of this paper is to present the expert massage robot using a multi-fingered robot hand. First, the fingertip forces applied by an expert human therapist was measured using sheet distribution pressure sensors, and the obtained data was rec

Synthesis of dextrous manipulation by multifingered robotic hands by Guanfeng Liu thesis 2003 xxii, 143, [48] leaves : ill. ; 30 cm Three most important problems in dextrous manipulation by multifingered robotic hands are the optimal grasp synthesis problem, real-time…Read more ›

Dynamic Modeling and Control of a Multi-Fingered Robot Hand for Grasping Task

In the construction of robot applications, controller is very important in producing good performance the robot system. This paper presents the modeling and simulation of multi fingered robot hand (MFRH) with the conventional and modern controllers where the output responses have been analyzed. A swarm algorithm known as Particle Swarm Optimization (PSO) has been used to find optimum solutions in a large search space. Simulation of modern controller (PSO-PID) has produced better results than the conventional controller in terms of system parameters such as rise time, settling time steady state error and maximum overshoot in the DC Servomotor speed control MFRH.