Improving Robotic Manipulation Without Sacrificing Grasping Efficiency: A Multi-Modal, Adaptive Gripper With Reconfigurable Finger Bases

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 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.

Current Designs of Robotic Arm Grippers: A Comprehensive Systematic Review

“…Moreover, the theory for the optimal design of the gripper is presented, including an objective function that maximizes the forces normal to the contact trajectory while avoiding loss of contact and ejection (Figure 25). Another optimization example is found in [15], which presents a multi-modal adaptive gripper with the optimal design of a re-configurable finger developed for improving robotic manipulation without sacrificing grasping efficiency (Figure 26). The optimization problem maximizes the workspace volume for a wide range of objects using a parallel multi-start search algorithm.…”

“…Grippers are classified depending on their design, how they are powered, and their application. For example, when considering industrial grippers, one of the simplest designs is the parallel motion two-jaw gripper, commonly used to lift objects [12][13][14][15]. Several other design types include the O-ring gripper [16], and the needle gripper [17].…”

Shape and Motion Optimization of Rigid Planar Effectors for Contact Trajectory Satisfaction

“…Several works search over small sets of geometric parameters, optimizing for desirable gripper behavior [10], [11], simple metrics for grasp stability [12]- [14], and force transmission [15]- [17]. Beyond these considerations, Elangovan et al [18] maximize manipulation workspace, and Yako et al [19] use a potential energy map to understand grasping behavior without simulation. These approaches show success in deciding parameters, but have limited expressivity compared to higher-dimensional design spaces like in our work.…”

Study of grasp-energy based optimal distribution of contact forces on a humanoid robotic hand during object grasp

“…Researchers from the University of Brazil developed an anthropometric robotic hand gripper, the UnB-Hand [27], which was designed with bio-inspired optimization algorithms, and was proven to be able to successfully perform grasps according to the Cutkosky grasping taxonomy, that is, power and precision grasps necessary for machining operations. Elangovan et al [28] proposed a novel adaptive gripper that can adjust link dimensions and finger base positions depending on the surface of the grasped object post contact, to give a more stable grasp.…”