Effect of fruit and hand characteristics on thumb–index finger power-grasp stability during manual fruit sorting

A new non-geometric transmission parameter optimization design method for HMCVT based on improved GA and maximum transmission efficiency

The robotic grasping task persists as a modern industry problem that seeks autonomous, fast implementation, and efficient techniques. Domestic robots are also a reality demanding a delicate and accurate human–machine interaction, with precise robotic grasping and handling. From decades ago, with analytical heuristics, to recent days, with the new deep learning policies, grasping in complex scenarios is still the aim of several works’ that propose distinctive approaches. In this context, this paper aims to cover recent methodologies’ development and discuss them, showing state-of-the-art challenges and the gap to industrial applications deployment. Given the complexity of the related issue associated with the elaborated proposed methods, this paper formulates some fair and transparent definitions for results’ assessment to provide researchers with a clear and standardised idea of the comparison between the new proposals.

Robotic grasping: from wrench space heuristics to deep learning policies

In the grasping and manipulation of 3D deformable objects by robotic hands, the physical contact constraints between the fingers and the object have to be considered in order to validate the robustness of the task. Nevertheless, previous works rarely establish contact interaction models based on these constraints that enable the precise control of forces and deformations during the grasping process. This paper considers all steps of the grasping process of deformable objects in order to implement a complete grasp planning pipeline by computing the initial contact points (pregrasp strategy), and later, the contact forces and local deformations of the contact regions while the fingers close over the grasped object (grasp strategy). The deformable object behavior is modeled using a nonlinear isotropic mass-spring system, which is able to produce potential deformation. By combining both models (the contact interaction and the object deformation) in a simulation process, a new grasp planning method is proposed in order to guarantee the stability of the 3D grasped deformable object. Experimental grasping experiments of several 3D deformable objects with a Barrett hand (3-fingered) and a 6-DOF industrial robotic arm are executed. Not only will the final stable grasp configuration of the hand + object system be obtained, but an arm + hand approaching strategy (pregrasp) will also be computed.

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Grasp Planning Pipeline for Robust Manipulation of 3D Deformable Objects with Industrial Robotic Hand + Arm Systems

Abstract To generate suitable grasping positions between tessellated handling objects and specific planar grippers, we propose a 2D analytical approach which uses a polygon clipping algorithm to generate detailed information about the intersection between both objects. With the generated knowledge about the intersection we check whether its shape fits to the set criteria of the operator and represents a valid grasping position. Before the polygon clipping algorithm is applied, a preprocessing step is performed, where appropriate surfaces from the handling object and the gripper are extracted. After rotating all surfaces into a common plane, potential clipping positions are detected and the clipping is performed to get an accurate intersection detection. The validation shows comparable running times to a OBBTree algorithm (0.1 ms per grasping position) while increasing the stability of the results from 30 to 100% for the evaluated test objects. 

/pdf/computing-gripping-points-in-2d-parallel-surfaces-via-25vtygjx.pdf

Computing Gripping Points in 2D Parallel Surfaces Via Polygon Clipping

The problem of automated grasp generation is exacerbated by the infinite types of objects to be handled by robots. In this work, the issue is cast as an optimization problem and a modified genetic algorithm-based approach has been formulated for the synthesis of high-quality grasps. The convex hull of the grasp contact wrenches is built, and the largest ball is inscribed within it. The radius of this resulting ball, centered at the origin, is used to represent the grasp quality. An initial feasible grasp is increased in quality by generating wrench population considering the complete body for an exhaustive search. Tessellated objects are utilized for the planner to ensure the applicability of the approach on complex shapes. The performance efficacy of the proposed method is numerically showcased through various frictional and non-frictional prehensile contact examples and is featured along with the results of an existing heuristic method on similar models with moderate and dense tessellation.

Optimizing force closure grasps on 3D objects using a modified genetic algorithm

Automated grasp planning for robotic hands is a complex problem when compared with the ease with which human hands grasp objects. Research in robotic grasp synthesis attempts to find novel ways in which a stable grasp can be achieved reliably. In this work, we present a grasping methodology that achieves optimized force closure grasps on 3D irregular objects. 3D objects in the form of polygonal meshes are parameterized to 2D shapes in order to reduce the search space by constraining robotic hands finger tips to be in contact with the objects surface. We use a Particle Swarm Optimization (PSO) based framework to optimize an initial grasp. The scheme has been validated on test-case 3D objects represented with surface tessellation for a 5-fingered DLR robotic hand.

S. Murugan

Papers

Optimizing force closure grasps on 3D objects using a modified genetic algorithm

Grasp Quality Improvement with Particle Swarm Optimization (PSO) for a Robotic Hand Holding 3D Objects