A two‐step method for 4‐pin form‐closure gripper with grasping force optimization

A smart choice of contact forces between robotic grasping devices and objects is important for achieving a balanced grasp. Too little applied force may cause an object to slip or be dropped, and too much applied force may cause damage to delicate objects. Prior methods of grasping force optimization in literature have mostly assumed grasp only at the fingertips but have rarely considered how the whole hand grasps more common to anthropomorphic hands affect the optimization of grasping forces. Further, although numerical examples of grasping force optimization methods are routinely provided, it is often difficult to compare the performance of separate methods when they are evaluated using different parameters, such as the type of grasping device, the object grasped, and the contact model, among other factors. This paper presents three optimization approaches (linear, nonlinear, and nonlinear with linear matrix inequality (LMI) friction constraints) which are compared for an anthropomorphic hand. Numerical examples are provided for three types of grasp commonly performed by the human hand (cylindrical grasp, tip grasp, and tripod grasp) using both soft finger contact and point contact with friction models. Contact points between the hand and the object are predetermined. Results are compared based on their accuracy, computational efficiency, and other various benefits and drawbacks unique to each method. Future work will extend the problem of grasping force optimization to include consideration for variable forces and object manipulation.

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A grasping force optimization algorithm for dexterous robotic hands

2012 IEEE International Conference on Robotics and Automation ◽

10.1109/icra.2012.6224915 ◽

2012 ◽

Cited By ~ 7

Author(s):

Vincenzo Lippiello ◽

Bruno Siciliano ◽

Luigi Villani

Keyword(s):

Optimization Algorithm ◽

Robotic Hands ◽

Force Optimization ◽

Grasping Force

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Grasping Force Optimization Approaches for Anthropomorphic Hands

Journal of Mechanisms and Robotics ◽

10.1115/1.4038684 ◽

2017 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

Aimee Cloutier ◽

James Yang

Keyword(s):

Computational Efficiency ◽

Linear Method ◽

Contact Forces ◽

Applied Force ◽

Contact Friction ◽

Linear Matrix ◽

Human Hand ◽

Contact Points ◽

Force Optimization ◽

Grasping Force

An appropriate choice of contact forces for anthropomorphic robotic grasping devices is important for achieving a balanced grasp. Too little applied force may cause an object to slip or be dropped, and too much applied force may cause damage to delicate objects. Prior methods of grasping force optimization (GFO) in the literature can be difficult to compare due to variability in the parameters, such as the type of grasping device, the object grasped, and the contact model, among other factors. Additionally, methods are typically tested on a very small number of scenarios and may not be as robust in other settings. This paper presents a detailed analysis of three optimization approaches based on the literature, comparing them on the basis of accuracy and computational efficiency. Numerical examples are provided for three types of grasp commonly performed by the human hand (cylindrical grasp, tip grasp, and tripod grasp) using both soft finger (SF) contact and hard finger (HF) contact friction models. For each method and grasping example, an external force is applied to the object in eighteen different directions to provide a more complete picture of the methods' performance. Contact points between the hand and the object are predetermined (given). A comparison of the results showed that the nonlinear and linear matrix inequality (LMI) approaches perform best in terms of accuracy, while the computational efficiency of the linear method is stronger unless the number of contact points and segments becomes too large. In this case, the nonlinear method performs more quickly. Future work will extend the problem of GFO to real-time implementation, and a related work (briefly addressed here) examines the sensitivity of optimization methods to variability in the contact locations.

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