Determining an appropriate grasp configuration for multi-finger grasping is difficult due to the complexity of robotic hands. The multi-finger grasp planning should consider not only geometry constraints of objects but also kinematics and dynamics of robotic hand. In this paper, a precision grasp-planning framework is presented for multi-finger hand to grasp unknown objects. First, the manipulation capabilities of the robotic hand are analyzed. The analysis results are further used as bases for the precision grasp planning. Second, the superquadric (SQ) fitting method is used for approximating unknown object models. Finally, a local–global optimization method is implemented to find appropriate grasp configurations for dexterous hand. The presented planning framework is validated in simulation experiments. Simulation results demonstrated that the presented grasp-planning framework enables the multi-finger hand to grasp unknown objects effectively.

As every real mechanical hand has a limited accuracy, the grasp planning process must be prepared to cope with unavoidable positioning errors. The concept of independent contact regions (ICRs) was proposed to deal with this issue by computing for each finger an ICR on the object's boundary such that each finger can be placed anywhere in its ICR to guarantee a force closure grasp. Existing methods for computing ICRs of a polygon requires that each ICR must lie on a single edge of the polygon. This constraint severely limits the size of computed ICRs, especially when the input polygon contains only small edges (e.g., when the polygon is used for representing a curve object). This paper proposes a method for computing the optimal ICRs for frictional two-fingered grasp of a polygon such that each ICR is allowed to extend across consecutive edges of the polygon. The time complexity of the method is O(n2log n), where n is the number of edges of the polygon. Implementation results using several test polygons are presented to exhibit effectiveness of the method.

In this paper, we propose an approach for computing suboptimal grasps of polyhedral objects. Assuming n hard-finger contact with Coulomb friction model and based on central axes of the grasp wrench, we develop a new necessary and sufficient condition for n-finger grasps to achieve force-closure property. Accordingly, we reformulate the proposed force-closure test as a new linear programming problem, which we solve using an interior point method. Furthermore, we present an approach for finding appropriate stable grasps for a robotic hand on arbitrary objects. We use the simulated annealing technique for synthesizing suboptimal grasps of 3D objects. Through numerical simulations on arbitrary shaped objects, we show that the proposed approach is able to compute good grasps for multifingered hands within a reasonable computational time.

Three-dimensional (3D) enveloping grasps for dexterous robotic hands possess several advantages over other types of grasps. This paper describes a new method for kinematic 3D enveloping grasp planning. A new idea for grading the 3D grasp search domain for a given object is proposed. The grading method analyzes the curvature pattern and effective diameter of the object, and grades object regions according to their suitability for grasping. A new approach is also proposed for modeling the fingers of the dexterous hand. The grasp planning method is demonstrated for a three-fingered, six degrees-of-freedom, dexterous hand and several 3D objects containing both convex and concave surface patches. Human-like high-quality grasps are generated in less than 20 s per object.

This paper proposes a grasp planning for a multifingered hand-arm robot in the presence of obstacles. When the hand grasps an object, neither the hand nor the arm should collide with obstacles. However, it is very difficult to generate a collision-free grasp configuration of the hand-arm robot due to its abundant degrees of freedom of motion. To overcome this difficulty, we first decompose the hand-arm robot into the multifingered hand part and the arm part. We develop two methods of the grasp planning. In one method, a collision-free configuration of the hand part is planned first. Then, the arm configuration is planned so as to enable the hand configuration. In the other method, the collision-free configuration of the arm part is planned first and the multi-fingered hand configuration second. The best method is selected according to the space structure of the environment. Various heuristics are developed to improve the efficiency of the planning and implementation of the system. Simulation results are presented.