Exploiting Robot Hand Compliance and Environmental Constraints for Edge Grasps

Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design

Robotic hands have long strived to reach the performance of human hands. The physical complexity and extraordinary capabilities of the human hand, in terms of sensing, actuation, and cognitive abilities, make achieving this goal challenging. At the heart of the physical structure of the hand is its' passive behaviors. Seen most clearly in soft robotic hands, these behaviors influence and affect the mechanical, sensing, and control functionalities. With this perspective, we present a framework through which passivity in robot hands can be understood, by concretely identifying the role of passivity in the design, fabrication, and control of soft hands. In this framework we focus on the interactions between the physical hand and the: environment, internal actuation, sensor morphology, and wrist control. Taking these surrounding systems away, we are left with a passive soft hand whose behaviors emerge from external interactions. Inspired by the human hand, we define the role of these four key interacting pillars and review how state-of-the art robot hands utilize these four elements to aid functionality. We show how these pillars promote hybrid soft-rigid hands with rich behaviors, providing benefits in terms of the increased adaptability to uncertain environments, improved scalability and reduction in the cost of actuation, sensing, and control. This review provides a conceptual framework for approaching hand design and analysis through consideration of the passive behaviors. This highlights not only the advances that can be made by approaching the problem in this way but also the outstanding challenges that stem from this outlook.

SurgGrip: a compliant 3D printed gripper for vision-based grasping of surgical thin instruments

This paper presents a conceptual design and implementation of a soft, compliant 3D printed gripper (SurgGrip), conceived for automated grasping of various surgery-based thin-flat instruments. The proposed solution includes (1) a gripper with a resilient mechanism to increase safety and better adaptation to the unstructured environment; (2) flat fingertips with mortise and tenon joint to facilitate pinching and enveloping based grasping of thin and random shape tools; (3) a soft pad on the fingertips to enable the high surface area to maintain stable grasping of the surgical instruments; (4) a four-bar linkage with a leadscrew mechanism to provide a precise finger movement; (5) enable automated manipulation of surgical tools using computer vision. Our gripper model is designed and fabricated by integrating soft and rigid components through a hybrid approach. The SurgGrip shows passive adaptation through inherent compliance of linear and torsional spring. The four-bar linkage mechanism controlled by a motor-leadscrew-nut drive provides precise gripper opening and closing movements. The experimental results show that the SurgGrip can detect, segment through a camera, and grasp surgical instruments (maximum 606.73 gs), with a 67% success rate (grasped 10 out of 12 selected tools) at 3.21 mm/s grasping speed and 15.81 s object grasping time autonomously. Besides, we demonstrated the pick and place abilities of SurgGrip on flat and nonflat surfaces in real-time.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11012-022-01594-6.

Grasp compliance achieved with a planar hand composed of multiple 3-joint fingers

In this paper, the realization of any specified planar Cartesian compliance for an object grasped by a compliant hand is addressed. The hands considered have 2 or more fingers for which each has 3 modulated elastic joints and predetermined link lengths. Geometric construction-based compliance synthesis procedures are developed. Using these procedures, a large set of compliant behaviors can be realized by a single hand simply by adjusting the configuration of each finger and by adjusting the joint stiffness (using variable stiffness actuation) of each finger joint.

Modeling and Simulation of Robotic Grasping in Simulink Through Simscape Multibody

Grasping and dexterous manipulation remain fundamental challenges in robotics, above all when performed with multifingered robotic hands. Having simulation tools to design and test grasp and manipulation control strategies is paramount to get functional robotic manipulation systems. In this paper, we present a framework for modeling and simulating grasps in the Simulink environment, by connecting SynGrasp, a well established MATLAB toolbox for grasp simulation and analysis, and Simscape Multibody, a Simulink Library allowing the simulation of physical systems. The proposed approach can be used to simulate the grasp dynamics in Simscape, and then analyse the obtained grasps in SynGrasp. The devised functions and blocks can be easily customized to simulate different hands and objects.

Hand closure model for planning top grasps with soft robotic hands

Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.