Design and Grasping Force Modeling for a Soft Robotic Gripper with Multi-stem Twining

To improve the grasping power of soft robots,inspired by the scene of intertwined and interdependent vine branches safely clinging to habitats in a violent storm and the phenomenon of large grasping force after being entangled by aquatic plants,this paper proposes a soft robotic gripper with multi-stem twining.The proposed robotic gripper can realize a larger contact area of surrounding or containing object and more layers of a twining object than the current twining gripping methods.It not only retains the adaptive advantages of twining grasping but also improves the grasping force.First,based on the mechanical characteristics of the multi-stem twining of the gripper,the twining grasping model is developed.Then,the force on the fiber is deduced by using the twining theory,and the axial force of the gripper is analyzed based on the equivalent model of the rubber ring.Finally,the torsion experiments of fibers and the grasping experiments of the gripper are designed and conducted.The torsion experiment of fibers verifies the influence of a different number of fiber ropes and fiber torque on the grasping force,and the grasping experiment reflects the large load of the gripper and the high adaptability and practicability under different tasks.

Grasping Force Planning and Control for Tendon-driven Anthropomorphic Prosthetic Hands

A force planning and control method is proposed for a tendon-driven anthropomorphic prosthetic hand. It is necessary to consider grasping stability for the anthropomorphic prosthetic hand with multi degrees of freedom which aims to mimic human hands with dexterity and stability. The excellent grasping performance of the anthropomorphic prosthetic hand mainly depends on the accurate computation of the space position of finger tips and an appropriate grasping force planning strategy. After the dynamics model of the tendon-driven anthropomorphic prosthetic hand is built, the space positions of the finger tips are calculated in real time by solving the dynamic equations based on the Newton iteration algorithm with sufficient accuracy. Then, the balance of internal grasping force on the thumb is adopted instead of force closure of the grasped objects to plan the grasping forces of other fingers based on the method of the linear constraint gradient flow in real time. Finally, a fuzzy logic controller is used to control the grasping force of the prosthetic hand. The proposed force planning and control method is implemented on the tendon-driven anthropomorphic prosthetic hand and the experimental results dem- onstrate the feasibility and effectiveness of the proposed method.

FORCE OPTIMIZATION OF GRASPING BY ROBOTIC HANDS

It is important for robotic hands to obtain optimal grasping performance inthe meanwhile balancing external forces and maintaining grasp stability. The problem of forceoptimization of grasping is solved in the space of joint torques. A measure of grasping performanceis presented to protect joint actuators from working in heavy payloads. The joint torques arecalculated for the optimal performance under the frictional constraints and the physical limits ofmotor outputs. By formulating the grasping forces into the explicit function of joint torques, thefrictional constraints imposed on the grasping forces are transformed into the constraints on jointtorques. Without further simplification, the nonlinear frictional constraints can be simply handledin the process of optimization. Two numerical examples demonstrate the simplicity and effectivenessof the approach.

Grasping Force Optimization Algorithm of Soft Multi-fingered Hand Based on Safety Margin Detection

The classical gradient flow optimization algorithm requires a valid initial point before starting the recursive algorithm,and the existing methods can’t guarantee that the initial values fully satisfy the friction cone constraints of contact point in the optimization process of gradient flow algorithm.In order to improve safety margin and prevent the finger from slipping at contact point,we present an iterative method of safe initial values with safety margin detection and develop a gradient flow optimization algorithm based on the safe initial values.Firstly,the safety margin is defined which more intuitively reflects the margin of the grasping forces at contact point.The resulting safe initial values can be achieved by the detection of desired safety margin at each iteration.Secondly,the safe initial values are usually not optimal,even with the valid initial values,and it can’t always ensure that the finger contact force always satisfies the friction cone constraints during the optimization.It is an effective way to eliminate the unreliable initial values in the optimization and obtain a safer initial values by increasing the safety margin.By transforming the safe initial values into an initial point of the gradient flow algorithm,the final optimized values of grasping forces can be generated efficiently by gradient flow iteration.Grasp examples of the soft multi-fingered hand indicate the effectiveness of the general solution of the force optimization algorithm based on safety margin detection.The method eliminates the shortcomings of the gradient flow optimization process caused by the initial value problem and provides a more accurate and reliable force optimization result for multi-fingered dexterous manipulation.

METHOD OF CLASSIFYING GRASPS BY ROBOT HANDS

This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with respect to the connectivity of the object. The relationship reveals the differences between three types of grasps classified and indicates how the contact force can be decomposed corresponding to each type of grasp. The subspaces and the determination of their di- mensions are illlustrated by examples.