An Origami-Inspired Reconfigurable Suction Gripper for Picking Objects with Variable Shape and Size

Gripper adaptability to handle objects of different shape and size brings high flexibility to manipulation. Gripping flat, round, or narrow objects poses challenges to even the most sophisticated robotic grippers. Among various gripper technologies, the vacuum suction grippers provide design simplicity, yet versatility at low cost, however, their application is limited to their fixed shape and size. Here, we present an origami-inspired reconfigurable suction gripper to address adaptability with robotic suction grippers. Constructed from rigid and soft components and driven by compact shape memory alloy actuators, the gripper can effectively self-fold into three shape modes to pick large and small flat, narrow cylindrical, triangular and spherical objects. The 10-g few centimeters gripper, lifts loads up to 5 N, 50 times its weight. We also present an under-actuated prototype, demonstrating the versatility of our design and actuation methods

Innovative robot hand designs of reduced complexity for dexterous manipulation

This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects. Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension. Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty. In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness. Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm. In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces

A Thermoplastic Elastomer Belt Based Robotic Gripper

Novel robotic grippers have captured increasing interests recently because of their abilities to adapt to varieties of circumstances and their powerful functionalities. Differing from traditional gripper with mechanical components-made fingers, novel robotic grippers are typically made of novel structures and materials, using a novel manufacturing process. In this paper, a novel robotic gripper with external frame and internal thermoplastic elastomer belt-made net is proposed. The gripper grasps objects using the friction between the net and objects. It has the ability of adaptive gripping through flexible contact surface. Stress simulation has been used to explore the regularity between the normal stress on the net and the deformation of the net. Experiments are conducted on a variety of objects to measure the force needed to reliably grip and hold the object. Test results show that the gripper can successfully grip objects with varying shape, dimensions, and textures. It is promising that the gripper can be used for grasping fragile objects in the industry or out in the field, and also grasping the marine organisms without hurting them

Actuation Technologies for Soft Robot Grippers and Manipulators: A Review

Purpose of Review The new paradigm of soft robotics has been widely developed in the international robotics community. These robots being soft can be used in applications where delicate yet effective interaction is necessary. Soft grippers and manipulators are important, and their actuation is a fundamental area of study. The main purpose of this work is to provide readers with fast references to actuation technologies for soft robotic grippers in relation to their intended application. Recent Findings The authors have surveyed recent findings on actuation technologies for soft grippers. They presented six major kinds of technologies which are either used independently for actuation or in combination, e.g., pneumatic actuation combined with electro-adhesion, for certain applications. Summary A review on the latest actuation technologies for soft grippers and manipulators is presented. Readers will get a guide on the various methods of technology utilization based on the application

A 3D-Printed Omni-Purpose Soft Gripper

Numerous soft grippers have been developed based on smart materials, pneumatic soft actuators, and underactuated compliant structures. In this article, we present a three-dimensional (3-D) printed omni-purpose soft gripper (OPSOG) that can grasp a wide variety of objects with different weights, sizes, shapes, textures, and stiffnesses. The soft gripper has a unique design that incorporates soft fingers and a suction cup that operate either separately or simultaneously to grasp specific objects. A bundle of 3-D-printable linear soft vacuum actuators (LSOVA) that generate a linear stroke upon activation is employed to drive the tendon-driven soft fingers. The support, fingers, suction cup, and actuation unit of the gripper were printed using a low-cost and open-source fused deposition modeling 3-D printer. A single LSOVA has a blocked force of 30.35 N, a rise time of 94 ms, a bandwidth of 2.81 Hz, and a lifetime of 26 120 cycles. The blocked force and stroke of the actuators are accurately predicted using finite element and analytical models. The OPSOG can grasp at least 20 different objects. The gripper has a maximum payload-to-weight ratio of 7.06, a grip force of 31.31 N, and a tip blocked force of 3.72 N

Increasing the Energy-Efficiency in Vacuum-Based Package Handling Using Deep Q-Learning

Billions of packages are automatically handled in warehouses every year. The gripping systems are, however, most often oversized in order to cover a large range of different carton types, package masses, and robot motions. In addition, a targeted optimization of the process parameters with the aim of reducing the oversizing requires prior knowledge, personnel resources, and experience. This paper investigates whether the energy-efficiency in vacuum-based package handling can be increased without the need for prior knowledge of optimal process parameters. The core method comprises the variation of the input pressure for the vacuum ejector, compliant to the robot trajectory and the resulting inertial forces at the gripper-object-interface. The control mechanism is trained by applying reinforcement learning with a deep Q-agent. In the proposed use case, the energy-efficiency can be increased by up to 70% within a few hours of learning. It is also demonstrated that the generalization capability with regard to multiple different robot trajectories is achievable. In the future, the industrial applicability can be enhanced by deployment of the deep Q-agent in a decentral system, to collect data from different pick and place processes and enable a generalizable and scalable solution for energy-efficient vacuum-based handling in warehouse automation

Origami-inspired kinematic morphing surfaces

In the past decades, an emerging technology has tried to build robots from soft materials to mimic living organisms in nature. Despite the flexibility and adaptability offered by such robots, the soft materials introduce very high or even infinite degrees of freedom (DoFs). It is thus challenging to achieve controllable shape changes on soft materials, which are essential for robots to carry out their functions. Many material-based approaches have been attempted to constrain the excessive DoFs of soft materials, so that they can bend, stretch, or twist as desired. In most applications, considering that only limited mobility is required to perform certain tasks, it would also be feasible to employ mechanical coupling to remove unwanted motions. To achieve this, engineers resort to origami techniques to design predictable and controllable robotic structures. However, most origami-inspired robots are built from existing patterns, where the material thickness is always neglected. Using zero-thickness sheets restricts the modelling accuracy, fabrication flexibility, and motion possibility. A recent study reveals that considering material thickness can further reduce the overall DoFs of origami, since its mechanical model is often overconstrained and differs significantly from that of the zero-thickness one. The novel structures with thickness, known as thick-panel origami, were originally developed for space use and are not accessible to roboticists. Hence, a thorough investigation is needed to develop thick-panel origami targeting robotic applications. This thesis is thus centred on two aspects. The first is to systematically design thick-panel origami for shape-changing, namely morphing surfaces. The second part extends selected surfaces into the design of intelligent robots, with the aim of simplified design, actuation, and control. The main achievements of this research are as follows. Firstly, a systematic design methodology is proposed to map thick-panel origami with 6R spatial overconstrained linkages. A library of morphing units whose thicknesses are uniform and not negligible is thus uncovered. Morphing surfaces, which are the tessellations or assemblies of morphing units, are then demonstrated to achieve common soft material behaviours, including bending, expanding, and twisting. Complex motions such as wrapping and curling are also presented. The mobility of these surfaces is restricted to one, while bifurcations may exist for extra motion possibilities. Secondly, a robotic gripper is designed from the wrapping surface. By exploiting the bifurcation and compliance of the surface, the proposed gripper has achieved a balance between motion dexterity and control complexity, aiming to solve the control challenges of grasping and manipulation. More specifically, the gripper can grasp objects of various shapes with one motor and conduct manipulations with only two control inputs, as opposed to many current end effectors that can only grasp or need around 20 actuators for manipulation tasks. On top of this, the gripper can be 3D-printed with ease, largely streamlining the mechanical design and fabrication process. Lastly, a reconfigurable robot is demonstrated on the curling surface to mimic a millipede's morphology. The robot can not only morph into a coil but also reconfigure into wave-like and triangular shapes. The reconfigurability is achieved by utilising the kinematic bifurcations of the surface without increasing the system's overall DoF. The design is also free from module disconnection and reconnection for new configurations, making the system more robust. The proof-of-concept robotic study has showcased the potential of maintaining reconfigurability with a relatively straightforward control strategy

TacFR-Gripper: A Reconfigurable Fin Ray-Based Compliant Robotic Gripper with Tactile Skin for In-Hand Manipulation

This paper introduces the TacFR-Gripper, a reconfigurable Fin Ray-based soft and compliant robotic gripper equipped with tactile skin, which can be used for dexterous in-hand manipulation tasks. This gripper can adaptively grasp objects of diverse shapes and stiffness levels. An array of Force Sensitive Resistor (FSR) sensors is embedded within the robotic finger to serve as the tactile skin, enabling the robot to perceive contact information during manipulation. We provide theoretical analysis for gripper design, including kinematic analysis, workspace analysis, and finite element analysis to identify the relationship between the gripper's load and its deformation. Moreover, we implemented a Graph Neural Network (GNN)-based tactile perception approach to enable reliable grasping without accidental slip or excessive force. Three physical experiments were conducted to quantify the performance of the TacFR-Gripper. These experiments aimed to i) assess the grasp success rate across various everyday objects through different configurations, ii) verify the effectiveness of tactile skin with the GNN algorithm in grasping, iii) evaluate the gripper's in-hand manipulation capabilities for object pose control. The experimental results indicate that the TacFR-Gripper can grasp a wide range of complex-shaped objects with a high success rate and deliver dexterous in-hand manipulation. Additionally, the integration of tactile skin with the GNN algorithm enhances grasp stability by incorporating tactile feedback during manipulations. For more details of this project, please view our website: https://sites.google.com/view/tacfr-gripper/homepage

Controlling a Vacuum Suction Cup Cluster using Simulation-Trained Reinforcement Learning Agents

Using compressed air in industrial processes is often accompanied by a poor cost-benefit ratio and a negative impact on the environmental footprint due to usual distribution inefficiencies. Compressed air-based systems are expensive regarding installation and lead to high running costs due to pricey maintenance requirements and low energy efficiency due to leakage. However, compressed air-based systems are indispensable for various industrial processes, like handling parts with Class A surface requirements such as outer skin sheets in automobile production. Most of those outer skin parts are solely handled by vacuum-based grippers to minimize any visible effect on the finished car. Fulfilling customer expectations and simultaneously reducing the running costs of decisive systems requires finding innovative strategies focused on using the precious resource of compressed air as efficiently as possible. This work presents a sim2real reinforcement learning approach to efficiently hold a workpiece attached to a vacuum suction cup cluster. In addition to pure energy-saving, reinforcement learning enables those agents to be trained without collecting extensive data beforehand. Furthermore, the sim2real approach makes it easy and parallelizable to examine numerous agents by training them in a simulation of the testing rig rather than at the testing rig itself. The possibility to train various agents fast additionally facilitates focusing on the robustness and simplicity of the found agents instead of only searching for strategies that work, making training an intelligent system scalable and effective. The resulting agents reduce the amount of energy necessary to hold the workpiece attached by more than 15% compared to a reference strategy without machine learning and by more than 99% compared to a conventional strategy