Abstract
This work proposes a framework that improves the dexterous manipulation capabilities of two fingered grippers by: i) optimizing the finger link dimensions and the interfinger distance for a given object and ii) analyzing the effect of finger symmetry and the distance between the finger base frames on their manipulation workspaces. The results of the workspace analysis motivate the development of a multi-modal, adaptive robotic gripper. In particular, the finger link lengths optimization problem is solved by a parallel multi-start search algorithm. The optimal link lengths are then used for the workspace analysis. The results of the analysis demonstrate that different inter-finger distances lead to completely different workspace shapes and that the ratio defined by the area of the optimized workspace (nominator) and the union of all workspaces (denominator), is always significantly less than 1. This means that the area of the union of all workspaces is always larger than the area of the “optimized” workspace. Based on these results the proposed robotic gripper is equipped with reconfigurable finger bases that vary the inter-finger distance as well as with selectively lockable robotic finger joints, offering an increased dexterous manipulation performance without sacrificing grasping efficiency. The device is considered multi-modal as it can be used both as a parallel jaw gripper and as an adaptive robotic gripper.
Highlights
Robotic hands and grippers are employed as end-effectors of robotic platforms to facilitate their interaction with the environments surrounding them
This paper proposed a framework for finding the optimal link dimensions and inter-finger distance for robotic grippers that are symmetric or asymmetric with two and three phalanges per finger
The optimization provided us with the optimal link ratios for all four gripper configurations examined
Summary
Robotic hands and grippers are employed as end-effectors of robotic platforms to facilitate their interaction with the environments surrounding them (e.g., grasp an object, push buttons, open a door). The versatility and ability of the grippers to manipulate a wide range of objects across many use cases and scenarios from service tasks [1] to industrial tasks [2], as well as their effectiveness in completing these tasks can be used as an indicator of their dexterity [3] Such complex tasks are executed by employing fully actuated, expensive, rigid robotic hands that require advanced sensing elements [4], [5]. The superior grasping performance of adaptive hands is typically attributed to the introduction of structural compliance combined with underactuation that make control simpler and more intuitive [9] These characteristics have led to a surge in the number of studies that focus on adaptive end-effectors. The structural compliance and underactuation compromise the pinch grasping capabilities of the gripper, introducing a post-contact parasitic reconfiguration of the gripper-object system that affects grasping stability [10]
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