A Note on Redesign Material Substitution and Topology Optimization in a Lightweight Robotic Gripper

The gripper is required because it is the portion of the robot that makes direct contact with the object being grasped. It should weigh as little as possible without compromising functionality or its performance. This study aims to reconsider the construction of a lightweight robotic gripper by modifying the gripper's materials and topology. Using the finite element (FE) method, several types of gripper materials were evaluated for static stress. On the basis of the results of the FE analysis, the optimal material candidate was chosen using the weighted objective method. Using the Fusion 360 software, the topology of the selected material was then optimized in an effort to achieve the 40% weight reduction’s objective. In addition, the suggested optimized geometry is then fine-tuned so that it can be manufactured as efficiently as possible. The final step in the validation of the robotic gripper's design was stress static analysis. The revised gripper design has a mass of 0.08 kg, a reduction of 94% from the original mass, and a safety factor of 3.67%, which satisfies the desired level of performance for the robotic gripper. Utilizing different materials and optimizing the gripper's topology can significantly reduce the overall mass of a robotic gripper. &nbsp

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

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

Design and Development of Sensor Integrated Robotic Hand

Most of the automated systems using robots as agents do use few sensors according to the need. However, there are situations where the tasks carried out by the end-effector, or for that matter by the robot hand needs multiple sensors. The hand, to make the best use of these sensors, and behave autonomously, requires a set of appropriate types of sensors which could be integrated in proper manners. The present research work aims at developing a sensor integrated robot hand that can collect information related to the assigned tasks, assimilate there correctly and then do task action as appropriate. The process of development involves selection of sensors of right types and of right specification, locating then at proper places in the hand, checking their functionality individually and calibrating them for the envisaged process. Since the sensors need to be integrated so that they perform in the desired manner collectively, an integration platform is created using NI PXIe-1082. A set of algorithm is developed for achieving the integrated model. The entire process is first modelled and simulated off line for possible modification in order to ensure that all the sensors do contribute towards the autonomy of the hand for desired activity. This work also involves design of a two-fingered gripper. The design is made in such a way that it is capable of carrying out the desired tasks and can accommodate all the sensors within its fold. The developed sensor integrated hand has been put to work and its performance test has been carried out. This hand can be very useful for part assembly work in industries for any shape of part with a limit on the size of the part in mind. The broad aim is to design, model simulate and develop an advanced robotic hand. Sensors for pick up contacts pressure, force, torque, position, surface profile shape using suitable sensing elements in a robot hand are to be introduced. The hand is a complex structure with large number of degrees of freedom and has multiple sensing capabilities apart from the associated sensing assistance from other organs. The present work is envisaged to add multiple sensors to a two-fingered robotic hand having motion capabilities and constraints similar to the human hand. There has been a good amount of research and development in this field during the last two decades a lot remains to be explored and achieved. The objective of the proposed work is to design, simulate and develop a sensor integrated robotic hand. Its potential applications can be proposed for industrial environments and in healthcare field. The industrial applications include electronic assembly tasks, lighter inspection tasks, etc. Application in healthcare could be in the areas of rehabilitation and assistive techniques. The work also aims to establish the requirement of the robotic hand for the target application areas, to identify the suitable kinds and model of sensors that can be integrated on hand control system. Functioning of motors in the robotic hand and integration of appropriate sensors for the desired motion is explained for the control of the various elements of the hand. Additional sensors, capable of collecting external information and information about the object for manipulation is explored. Processes are designed using various software and hardware tools such as mathematical computation MATLAB, OpenCV library and LabVIEW 2013 DAQ system as applicable, validated theoretically and finally implemented to develop an intelligent robotic hand. The multiple smart sensors are installed on a standard six degree-of-freedom industrial robot KAWASAKI RS06L articulated manipulator, with the two-finger pneumatic SHUNK robotic hand or designed prototype and robot control programs are integrated in such a manner that allows easy application of grasping in an industrial pick-and-place operation where the characteristics of the object can vary or are unknown. The effectiveness of the actual recommended structure is usually proven simply by experiments using calibration involving sensors and manipulator. The dissertation concludes with a summary of the contribution and the scope of further work

Improved Deep Neural Networks for Generative Robotic Grasping

This thesis provides a thorough evaluation of current state-of-the-art robotic grasping methods and contributes to a subset of data-driven grasp estimation approaches, termed generative models. These models aim to directly generate grasp region proposals from a given image without the need for a separate analysis and ranking step, which can be computationally expensive. This approach allows for fully end-to-end training of a model and quick closed-loop operation of a robot arm. A number of limitations are identified within these generative models, which are identified and addressed. Contributions are proposed that directly target each stage of the training pipeline that help to form accurate grasp proposals and generalise better to unseen objects. Firstly, inspired by theories of object manipulation within the mammalian visual system, the use of multi-task learning in existing generative architectures is evaluated. This aims to improve the performance of grasping algorithms when presented with impoverished colour (RGB) data by training models to perform simultaneous tasks such as object categorisation, saliency detection, and depth reconstruction. Secondly, a novel loss function is introduced which improves overall performance by rewarding the network to focus only on learning grasps at suitable positions. This reduces overall training times and results in better performance on fewer training examples. The last contribution analyses the problems with the most common metric used for evaluating and comparing offline performance between different grasping models and algorithms. To this end, a Gaussian method of representing ground-truth labelled grasps is put forward, which optimal grasp locations tested in a simulated grasping environment. The combination of these novel additions to generative models results in improved grasp success, accuracy, and performance on common benchmark datasets compared to previous approaches. Furthermore, the efficacy of these contributions is also tested when transferred to a physical robotic arm, demonstrating the ability to effectively grasp previously unseen 3D printed objects of varying complexity and difficulty without the need for domain adaptation. Finally, the future directions are discussed for generative convolutional models within the overall field of robotic grasping

The simulation of automated leading edge assembly

Aircraft manufacturers are experiencing a fierce competition worldwide. Improving productivity, increasing throughput and reducing costs are influencing aircraft manufacturer’s future development. In order to improve competitiveness and provide sufficient and high quality products, it should reduce operations of aircraft assembly,majority of which are still in manual process, which limit production output. In contrast, these processes can be automated to replace manual operations. Much more attention should be placed on automated application. This project aims to propose a methodology to develop the automated assembly based on robotics and use this methodology to develop a new concept of Automated Leading Edge Assembly. The research selects an automated assembly process for further evaluation and brackets assembled on the front spar of Leading Edge are chosen to be automated assembly with robot assistant. The software DELMIA is used to develop and simulate the automated assembly process of brackets based on 3-D virtual aircraft Leading Edge models. The research development is mainly divided into three phases which are: (1) The state of art on Manual Leading Edge Assembly; (2) Automated Leading Edge Assembly framework development; (3) Automated Leading Edge Assembly framework evaluation including automated assembly process simulation based on DELMIA robotics workbench and automated assembly cost estimation. The research has proposed a methodology to develop the automated assembly based on robotics, proposed a new concept of Automated Leading Edge Assembly: using robots to replace workers to finish the assembly applications in the Leading Edge, and proposed a new automated bracket assembly process with laser ablation, adhesive bonding, drilling, riveting, and robot application. These applications can attract more and more engineers’ attention and provide preliminary knowledge for further study and detail research in the future

Pneumatic Hyperelastic Robotic End-Effector for Grasping Soft Curved Organic Objects

Pneumatically-driven soft robotic grippers can elastically deform to grasp delicate, curved organic objects with minimal surface damage. However, common actuators have complex geometries and are fabricated with ultra-soft hyperelastic elastomers not originally intended for scientific applications. The complexity of the actuator geometry and extreme nonlinearity of their material’s stress-strain behaviour make it difficult to predict the actuator’s deformation prior to experimentation. In this work, a compact soft pneumatic gripper made with polydimethylsiloxane (PDMS) is developed for grasping delicate organic objects, analyzed through computational modelling and experimentally validated. COMSOL Multiphysics is used to simulate the impact of geometrical parameters on the actuator’s behaviour, allowing for the refinement of the proposed geometry prior to fabrication. Optimal parameters are selected for fabrication, with experimental tests matching simulations within ± 1 mm. Gripper performance is evaluated for three actuator wall thicknesses in terms of contact area with target, contact force, and maximum payload before slippage. The comparative assessment between simulations and experiments demonstrate that the proposed soft actuators can be used in robotic grippers tailored for grasping delicate objects without damaging their surface. Furthermore, analysis of the actuators provides additional insight on how to design simple but effective soft systems

Tactile Based Active Perception of Structural Members in Truss Structures

University of Technology Sydney. Faculty of Engineering and Information Technology.Complex Three-Dimensional (3D) truss structures such as power transmission towers require regular inspection and maintenance during their service life. Developing a robot to climb and explore such complex structures is challenging. Changing lighting conditions can render vision sensors unreliable; therefore, the robot should be endowed with a complementary sensory modality such as touch for accurate perception of the environment, including recognising a structural beam member and its properties of cross-sectional shape, size and the grasping Angle-of-Approach (AoA). The research presented in this thesis addresses three questions related to grasping and touch based perception of beam members in truss structures. (1) Methods for designing adaptive grippers for grasping a wide variety of structural beam member cross-sectional shapes and sizes; (2) Sensing for data collection and methods for classifying beam member properties; and (3) Efficient methods for selecting the next best grasping action to confidently recognise a beam member. A stiffness constrained topology optimisation design method is developed and applied in designing a soft gripper for grasping a variety of cross-sectional shapes of beam members. The gripper design is verified through both simulation and experiments. It is found that the gripper is proficient in grasping different shapes and sizes of beam members, with adequate contact points. A comparative study of commonly used machine learning classifiers is conducted to analyse the effectiveness of recognising a structural beam member and its properties. Using data collected during grasping with a soft gripper, the cross-sectional shape, size and grasping AoA of a beam member are classified. Evaluation of the various classifiers revealed that a Random Forest (RF) classifier with 100 trees achieved high classification accuracies, with short training and classification times. An information-based method for selecting the next best grasping AoA to confidently recognise a beam member is developed. This method is verified through simulation using grasping data collected with a soft gripper. The results show that this method can correctly recognise a structural beam member and its properties, typically with fewer than four grasping actions. This method can be generally used with many different gripper designs and sensor arrangements