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 multimodal, 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 interfinger 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.
Adaptive robot hands are typically created by introducing structural compliance either in their joints (e.g., implementation of flexures joints) or in their finger-pads. In this paper, we present a series of alternative uses of structural compliance for the development of simple, adaptive, compliant and/or under-actuated robot grippers and hands that can efficiently and robustly execute a variety of grasping and dexterous, in-hand manipulation tasks. The proposed designs utilize only one actuator per finger to control multiple degrees of freedom and they retain the superior grasping capabilities of the adaptive grasping mechanisms even under significant object pose or other environmental uncertainties. More specifically, in this work, we introduce, discuss, and evaluate: (a) a design of pre-shaped, compliant robot fingers that adapts/conforms to the object geometry, (b) a hyper-adaptive finger-pad design that maximizes the area of the contact patches between the hand and the object, maximizing also grasp stability, and (c) a design that executes compliance adjustable manipulation tasks that can be predetermined by tuning the in-series compliance of the tendon routing system and by appropriately selecting the imposed tendon loads. The grippers are experimentally tested and their efficiency is validated using three different types of tests: (i) grasping tests that involve different everyday objects, (ii) grasp quality tests that estimate the contact area between the grippers and the objects grasped, and (iii) dexterous, in-hand manipulation experiments to evaluate the manipulation capabilities of the Compliance Adjustable Manipulation (CAM) hand. The devices employ mechanical adaptability to facilitate and simplify the efficient execution of robust grasping and dexterous, in-hand manipulation tasks.
Evaluating the dexterity of human and robotic hands through appropriate benchmarks, scores, and metrics is of paramount importance for determining how skillful humans are and for designing and developing new bioinspired or even biomimetic end-effectors (e.g., robotic grippers and hands). Dexterity tests have been used in industrial and medical settings to assess how dexterous the hands of workers and surgeons are as well as in robotic rehabilitation settings to determine the improvement or deterioration of the hand function after a stroke or a surgery. In robotics, having a comprehensive dexterity test can allow us to evaluate and compare grippers and hands irrespectively of their design characteristics. However, there is a lack of well defined metrics, benchmarks, and tests that quantify robot dexterity. Previous work has focused on a number of widely accepted functional tests that are used for the evaluation of manual dexterity and human hand function improvement post injury. Each of these tests focuses on a different set of specific tasks and objects. Deriving from these tests, this work proposes a new modular, affordable, accessible, open-source dexterity test for both humans and robots. This test evaluates the grasping and manipulation capabilities by combining the features and best practices of the aforementioned tests, as well as new task categories specifically designed to evaluate dexterous manipulation capabilities. The dexterity test and the accompanying benchmarks allow us to determine the overall hand function recovery and dexterity of robotic end-effectors with ease. More precisely, a dexterity score that ranges from 0 (simplistic, non-dexterous system) to 1 (human-like system) is calculated using the weighted sum of the accuracy and task execution speed subscores. It should also be noted that the dexterity of a robotic system can be evaluated assessing the efficiency of either the robotic hardware, or the robotic perception system, or both. The test and the benchmarks proposed in the study have been validated using extensive human and robot trials. The human trials have been used to determine the baseline scores for the evaluation system. The results show that the time required to complete the tasks reduces significantly with trials indicating a clear learning curve in mastering the dexterous manipulation capabilities associated with the imposed tasks. Finally, the time required to complete the tasks with restricted tactile feedback is significantly higher indicating its importance.
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