Underactuated Robotic Gripper Research Articles

Frame-Based Slip Detection for an Underactuated Robotic Gripper for Assistance of Users with Disabilities

Stable grasping is essential for assistive robots aiding individuals with severe motor–sensory disabilities in their everyday lives. Slip detection can prevent unstably grasped objects from falling out of the gripper and causing accidents. Recent research on slip detection focuses on tactile sensing; however, not every robot arm can be equipped with such sensors. In this paper, we propose a slip detection method solely based on data collected by a RealSense D435 Red Green Blue-Depth (RGBd) camera. By utilizing Farneback optical flow (OF) to estimate the motion field of the grasped object relative to the gripper, while also removing potential background noise, the algorithm can perform in a multitude of environments. The algorithm was evaluated on a dataset of 28 daily objects that were lifted 30 times each, resulting in a total of 840 frame sequences. Our proposed slip detection method achieves an accuracy of up to 82.38% and a recall of up to 87.14%, which is comparable to state-of-the-art approaches when only using camera data. When excluding objects for which movements are challenging for vision-based methods to detect, such as untextured or transparent objects, the proposed method performs even better, with an accuracy of up to 87.19% and a recall of up to 95.09%.

Sensor-Less Grasping Force Control of a Pneumatic Underactuated Robotic Gripper

Abstract The primary motivation of this study is to develop a sensor-less, easily controlled, and passively adaptive robotic gripper. A back-drivable pneumatic underactuated robotic gripper (PURG), based on the pneumatic cylinder and underactuated finger mechanism, is presented to accomplish the above goals. A feedforward grasping force control method, based on the learned kinematics of the underactuated finger mechanism, is proposed to achieve sensor-less grasping force control. To enhance the grasping force control accuracy, a state-based actuating force modeling method is presented to compensate the hysteresis error which exists in the transmission mechanism. Actuating force control experiment is performed to validate the effectiveness of the state-based actuating pressure modeling method. Results reveal that compared with the non-state-based modeling method, the proposed state-based actuating force modeling method could reduce the modeling error and control error by about 37.0% and 77.2%, respectively. Results of grasping experiments further reveal that grasping force could be accurately controlled by the state-based feedforward control model in a sensor-less approach. Adaptive grasping experiments are performed to exhibit the effectiveness of the sensor-less grasping force control approach.

Design, Analysis, and Experiment of an Underactuated Robotic Gripper Actuated by Linear Series Elastic Actuator

Abstract The primary motivation of this study is to develop a cost-effective, safe, easily controlled, and passively adaptive robotic gripper. A novel linear series elastic actuated robotic gripper (LSEA-RG) is proposed to accomplish the aforementioned goals. The LSEA-RG has the ability to adaptively grasp objects with different shapes and sizes by grasping force control. First, the mechanical design, including the transmission scheme, the finger mechanism, and the actuating mechanism is presented. Subsequently, the kinematic analysis is performed to explore the transmission characteristic of the actuating displacement and force. Then a model-free control method based on the lookup functions of the kinematic transmission characteristics is presented to achieve the grasping range and force control. Finally, several grasping experiments are carried out based on the LSEA-RG prototype to evaluate the grasping performance. The results demonstrate that the presented grasping control method which based on the lookup functions is valid. The LSEA-RG could estimate the grasping force without the help of a fingertip force sensor and grasp objects with different shapes and hardness adaptively.

Modeling, Design, and Implementation of an Underactuated Gripper with Capability of Grasping Thin Objects

Underactuated robotic grippers have the advantage of lower cost, simpler control, and higher safety over the fully actuated grippers. In this study, an underactuated robotic finger is presented. The design issues that should be considered for stable grasping are discussed in detail. This robotic finger is applied to design a two-fingered underactuated gripper. Firstly, a new three-DOF linkage-driven robotic finger that combines a five-bar mechanism and a double parallelogram is presented. This special architecture allows us to put all of the required actuators into the palm. By adding a torsion spring and a mechanical stopper at a passive joint, this underactuated finger mechanism can be used to perform parallel grasping, shape-adaptive grasping, and environmental contact-based grasp. Secondly, the dynamic model of this robotic finger is developed to investigate how to select an appropriate torsion spring. The dynamic simulation is performed with a multi-body dynamic simulator to verify our proposed approach. Moreover, static grasp models of both two-point and three-point contact grasps are investigated. Finally, different types of grasping modes are verified experimentally with a two-fingered underactuated robotic gripper.

TWISTER Hand: Underactuated Robotic Gripper Inspired by Origami Twisted Tower

This article presents a new cable-driven underactuated robotic gripper, called TWISTER Hand. It is designed for adaptable grasping of objects in different shapes, weights, sizes, and textures. Each finger of the gripper is made of a compliant and continuum mechanism inspired by an origami design. This design is converted into a computer-aided design (CAD) model and 3-D printed using flexible and rigid polymer composite materials. Two CAD modeling methods for this design are compared in terms of structural stiffness and durability in the printed outcomes. For each design, two soft materials are used for preliminary evaluation of the material effect in these properties. The best combination of the model and material is selected to fabricate the three fingers of the robotic gripper. Each finger has a single cable routed along the structure. All three cables are tied and actuated simultaneously using a single servo motor to generate closing and opening motions in the gripper. TWISTER Hand's adaptable grasping capability is tested using 36 different objects. The robot's grasping performance under object pose uncertainties is also experimentally tested and analyzed. This compact fully integrated gripper can be attached to a robotic arm for various manipulative tasks.

Geometric design optimization of an under-actuated tendon-driven robotic gripper

We build the mathematical model between the actuated force and the contact force for an under-actuated tendon-driven robotic gripper based on the geometric analysis.A mathematical model is constructed to obtain the transmission efficiency of the tension force when a tendon wraps a joint mandrel by the geometric relations.The geometric model of transmission characteristics determined by the tendon routes for reducing the resistance is generated.Genetic Algorithm is applied to optimizing the dimensions of the gripper and the tendon routes.The geometrically optimal approach provided by us has the characteristics of the versatility and can also be referred to optimizing most of the under-actuated robotic gripper with tendon-driven mechanisms. The design optimization of a robotic gripper is of utmost importance for achieving a stable grasp behaviour. This work focuses on analysing the optimal design of an under-actuated tendon-driven robotic gripper with two 3-phalange fingers and a geometric design optimization method is proposed to achieve a stable grasp performance. The problem has twenty-two design variables, including three phalange lengths, three phalange widths, three radii of joint mandrels, a palm width and twelve route variables for allocation of six pulleys. First, the mathematical model between the active and contact forces is expressed in relation to the geometric dimensions of the robotic gripper. Second, the geometric model of transmission characteristics determined by the tendon routes for reducing the resistance is generated. Next, three objective functions and multiple geometric constraints are derived and integrated into two fitness models. Finally, the genetic algorithm is applied to addressing the optimization problem. Practical experiments are performed as well to validate the proposed approach. The approach is universal for optimizing any conventional under-actuated tendon-driven gripper.

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult tо analyze with conventional analytical methods. Here, a novel design of an adaptive neuro fuzzy inference system (ANFIS) for estimation contact forces of a new adaptive gripper is presented. Since the conventional analytical methods is a very challenging task, fuzzy logic based systems are considered as potential candidates for such an application. The main points of this paper are in explanation of kinetostatic analyzing of the new gripper structure using rigid body model with added compliance in every single joint. The experimental results can be used as training data for ANFIS network for estimation of gripping forces. An adaptive neuro-fuzzy network is used to approximate correlation between contact point locations and contact forces magnitudes. The simulation results presented in this paper show the effectiveness of the developed method. This system is capable to find any change in ratio of positions of the gripper contacts and magnitudes of the contact forces and thus indicates state of both finger phalanges.