An overview of pruning and harvesting manipulators

Purpose The motivation for robotics research in the agricultural field has sparked in consequence of the increasing world population and decreasing agricultural labor availability. This paper aims to analyze the state of the art of pruning and harvesting manipulators used in agriculture. Design/methodology/approach A research was performed on papers that corresponded to specific keywords. Ten papers were selected based on a set of attributes that made them adequate for review. Findings The pruning manipulators were used in two different scenarios: grapevines and apple trees. These manipulators showed that a light-controlled environment could reduce visual errors and that prismatic joints on the manipulator are advantageous to obtain a higher reach. The harvesting manipulators were used for three types of fruits: strawberries, tomatoes and apples. These manipulators revealed that different kinematic configurations are required for different kinds of end-effectors, as some of these tools only require movement in the horizontal axis and others are required to reach the target with a broad range of orientations. Originality/value This work serves to reduce the gap in the literature regarding agricultural manipulators and will support new developments of novel solutions related to agricultural robotic grasping and manipulation.

Interpreting and predicting tactile signals for the SynTouch BioTac

In the human hand, high-density contact information provided by afferent neurons is essential for many human grasping and manipulation capabilities. In contrast, robotic tactile sensors, including the state-of-the-art SynTouch BioTac, are typically used to provide low-density contact information, such as contact location, center of pressure, and net force. Although useful, these data do not convey or leverage the rich information content that some tactile sensors naturally measure. This research extends robotic tactile sensing beyond reduced-order models through (1) the automated creation of a precise experimental tactile dataset for the BioTac over a diverse range of physical interactions, (2) a 3D finite-element (FE) model of the BioTac, which complements the experimental dataset with high-density, distributed contact data, (3) neural-network-based mappings from raw BioTac signals to not only low-dimensional experimental data, but also high-density FE deformation fields, and (4) mappings from the FE deformation fields to the raw signals themselves. The high-density data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible. Datasets, CAD files for the experimental testbed, FE model files, and videos are available at https://sites.google.com/nvidia.com/tactiledata .

Systematic object-invariant in-hand manipulation via reconfigurable underactuation: Introducing the RUTH gripper

We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilizes a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers, jointly controlled by a single actuator, connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system in total, one for controlling the force exerted on objects by the fingers through an underactuated tendon system, and two for changing the configuration of the palm and, thus, the positioning of the fingers. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with a large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped. This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base positions change with palm reconfigurations. We analyze the theoretical grasping workspace and grasping and manipulation capability of the hand, present algorithms for computing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numerical and empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept.

Torsional Stiffness Improvement of a Soft Pneumatic Finger Using Embedded Skeleton

Silicone-based pneumatic actuators are among the most widely used soft actuators in adaptable fingers. However, due to the soft nature of silicone, the performance of these fingers is highly affected by the low torsional stiffness, which may cause failure in grasping and manipulation. To address this problem, a compact design is proposed by embedding a rigid skeleton into a soft pneumatic finger. A finite element approach with an analytical model is used to evaluate the performance of the fingers both with and without the skeleton. Then, a series of experiments is performed to study the bending motion and rigidity of the fingers. The results reveal that the skeleton increases the torsional stiffness of the finger up to 300%. Furthermore, the consistency with the experimental data indicates the good precision of the proposed modeling method. Finally, a two-finger hand is designed to evaluate the performance of the reinforced finger in reality. The grasp experiments illustrate that the hybrid finger with the skeleton is highly adaptable and can successfully grasp and manipulate heavy objects. Thus, a potential approach is proposed to improve the torsional stiffness of silicone-based pneumatic fingers while maintaining adaptability.

Design of a Wearable Haptic Device for Hand Palm Cutaneous Feedback

This study describes the main design and prototyping steps of a novel haptic device for cutaneous stimulus of a hand palm. This part of the hand is fundamental in several grasping and manipulation tasks, but is still less exploited in haptics applications than other parts of the hand, as for instance the fingertips. The proposed device has a parallel tendon-based mechanical structure and is actuated by three motors positioned on the hand’s back. The device is able to apply both normal and tangential forces and to render the contact with surfaces with different slopes. The end-effector can be easily changed to simulate the contact with different surface curvatures. The design is inspired by a smaller device previously developed for the fingertips; however, in the device presented in this study, there are significant differences due to the wider size, the different form-factor, and the structure of hand palm. The hand palm represents the support for the fingers and is connected to the arm through the wrist. The device has to be developed taking into account fingers’ and wrist’s motions, and this requirement constrains the number of actuators and the features of the transmission system. The larger size of the palm and the higher forces challenge the device from a structural point of view. Since tendons can apply only tensile forces, a spring-based support has been developed to keep the end-effector separated from the palm when the device is not actuated or when the force to be rendered is null. The study presents the main design guidelines and the main features of the proposed device. A prototype has been realized for the preliminary tests, and an application scenario with a VR environment is introduced.

Application of soft computing methods in robotic grasping: A state-of-the-art survey

Robotic grasping has become one of the most important domains of robotics research over the past few decades due to its wide range of applications in industrial automation. The model of grasping objects by robot hand depends on a good number of factors, such as type and size of the object, the morphology of object, type of hand, degree of freedom, etc. Thus, the model sometimes becomes mathematically intractable. With the progress in computational capability, soft computing methods have found a way to address the challenges faced by traditional methods while dealing with the robotic grasping problem. This article aims to summarize the outcome of a systematic study in the field of application of soft computing methods in robotic grasping and manipulation. The key processes of robotic grasping where soft computing methods are applied are identified, and research contributions of all processes are analyzed. This review presents a state-of-the-art survey and attempts to find the research gaps in the area of soft computing applications to address the robotic grasping problem.

Proximity Sensor for Thin Wire Recognition and Manipulation

In robotic grasping and manipulation, the knowledge of a precise object pose represents a key issue. The point acquires even more importance when the objects and, then, the grasping areas become smaller. This is the case of Deformable Linear Object manipulation application where the robot shall autonomously work with thin wires which pose and shape estimation could become difficult given the limited object size and possible occlusion conditions. In such applications, a vision-based system could not be enough to obtain accurate pose and shape estimation. In this work the authors propose a Time-of-Flight pre-touch sensor, integrated with a previously designed tactile sensor, for an accurate estimation of thin wire pose and shape. The paper presents the design and the characterization of the proposed sensor. Moreover, a specific object scanning and shape detection algorithm is presented. Experimental results support the proposed methodology, showing good performance. Hardware design and software applications are freely accessible to the reader.

Preliminary Study on Multibody Modeling and Simulation of an Underactuated Gripper With Differential Transmission

Robotic grippers have represented a challenge for designers and engineers since at least three decades, due to the complexity of grasping and manipulation tasks. Underactuated and soft robotic grippers are a technology that allows good dexterity and manipulating capabilities, by reducing the number of actuators. However, this type of device requires the use of complex mechanical systems to compensate the underactuated implementation limits, such as differential mechanisms. The differential mechanism is necessary to decouple finger closures and distribute forces. The multibody simulation allows to evaluate the main parameters of the elements to understand how the differential system can work. The development and design of complex mechanical systems is simplified by this technique. In particular, this paper presents a multibody simulation analysis which recreates an elementary model of a gripper with two links and a single actuator; the developed model reproduces the grasping of an object using a mechanical differential pulley system, placed beneath the fingers. Some results are presented to study the role of the differential when the fingers grasp an object with different configurations. The aim of this work is to show how an accurate and still manageable multibody model integrated in Matlab environment is able to extend the classical grasp metrics to a more general dynamic setup.

Robust scalable reversible strong adhesion by gecko-inspired composite design

Bio-inspired reversible adhesion has significant potential in many fields requiring flexible grasping and manipulation, such as precision manufacturing, flexible electronics, and intelligent robotics. Despite extensive efforts for adhesive synthesis with a high adhesion strength at the interface, an effective strategy to actively tune the adhesion capacity between a strong attachment and an easy detachment spanning a wide range of scales has been lagged. Herein, we report a novel soft-hard-soft sandwiched composite design to achieve a stable, repeatable, and reversible strong adhesion with an easily scalable performance for a large area ranging from ∼1.5 to 150 cm2 and a high load ranging from ∼20 to 700 N. Theoretical studies indicate that this design can enhance the uniform loading for attachment by restraining the lateral shrinkage in the natural state, while facilitate a flexible peeling for detachment by causing stress concentration in the bending state, yielding an adhesion switching ratio of ∼54 and a switching time of less than ∼0.2 s. This design is further integrated into versatile grippers, climbing robots, and human climbing grippers, demonstrating its robust scalability for a reversible strong adhesion. This biomimetic design bridges microscopic interfacial interactions with macroscopic controllable applications, providing a universal and feasible paradigm for adhesion design and control.