Universal Robotic Gripper based on the Jamming of Granular Material

Gripping and holding of objects are key tasks for robotic manipulators. The development of universal grippers able to pick up unfamiliar objects of widely varying shape and surface properties remains, however, challenging. Most current designs are based on the multi-fingered hand, but this approach introduces hardware and software complexities. These include large numbers of controllable joints, the need for force sensing if objects are to be handled securely without crushing them, and the computational overhead to decide how much stress each finger should apply and where. Here we demonstrate a completely different approach to a universal gripper. Individual fingers are replaced by a single mass of granular material that, when pressed onto a target object, flows around it and conforms to its shape. Upon application of a vacuum the granular material contracts and hardens quickly to pinch and hold the object without requiring sensory feedback. We find that volume changes of less than 0.5% suffice to grip objects reliably and hold them with forces exceeding many times their weight. We show that the operating principle is the ability of granular materials to transition between an unjammed, deformable state and a jammed state with solid-like rigidity. We delineate three separate mechanisms, friction, suction and interlocking, that contribute to the gripping force. Using a simple model we relate each of them to the mechanical strength of the jammed state. This opens up new possibilities for the design of simple, yet highly adaptive systems that excel at fast gripping of complex objects.Comment: 10 pages, 7 figure

The implications of embodiment for behavior and cognition: animal and robotic case studies

In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. 'intelligence requires a body', the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of 'body schema' and 'forward models' as a natural extension of the embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5

A Lightweight Universal Gripper with Low Activation Force for Aerial Grasping

Soft robotic grippers have numerous advantages that address challenges in dynamic aerial grasping. Typical multi-fingered soft grippers recently showcased for aerial grasping are highly dependent on the direction of the target object for successful grasping. This study pushes the boundaries of dynamic aerial grasping by developing an omnidirectional system for autonomous aerial manipulation. In particular, the paper investigates the design, fabrication, and experimental verification of a novel, highly integrated, modular, sensor-rich, universal jamming gripper specifically designed for aerial applications. Leveraging recent developments in particle jamming and soft granular materials, the presented gripper produces a substantial holding force while being very lightweight, energy-efficient and only requiring a low activation force. We show that the holding force can be improved by up to 50% by adding an additive to the membrane's silicone mixture. The experiments show that our lightweight gripper can develop up to 15N of holding force with an activation force as low as 2.5N, even without geometric interlocking. Finally, a pick and release task is performed under real-world conditions by mounting the gripper onto a multi-copter. The developed aerial grasping system features many useful properties, such as resilience and robustness to collisions and the inherent passive compliance which decouples the UAV from the environment.Comment: 21 pages, 19 figures; corrected affiliation

Assessment of eggplant firmness with accelerometers on a pneumatic robot gripper

A pneumatic robot gripper capable of sorting eggplants according to their firmness has been developed and tested. The gripper has three fingers and one suction cup. Each finger has an inertial sensor attached to it. One of the fingers adapts to and copies the shapes of eggplants when the jamming of its internal granular material changes from soft to hard. The other fingers adapt to the shape of the eggplant with the use of extra degrees of freedom. Specific software acquires and processes the information obtained with the inertial sensors and generates 16 independent variables extracted from the signals. A total of 234 eggplants were selected and tested on the same day with the robot gripper, during the pick-and-place operation, and with a destructive firmness tester. The non-destructive parameters extracted from the gripper finger accelerometers were used to build and validate a partial least square model, with a calibration regression coefficient of r = 0.87 and a high prediction performance (r = 0.90). Furthermore, from the results of the paper, it has been seen that the procedure can be simplified by using only two non-destructive impacts and one uniaxial accelerometer to assess eggplant firmness. The non-destructive assessment of firmness while grasping agricultural products in pick-and-place operations could be implemented in many prehensile pneumatic robot grippers. This technique could mean an important advance in the hygienic postharvest handling of fruits and vegetables.This research is supported by MANI-DACSA project (Ref. RTA2012-00062-C04-02), partially funded by the Spanish Government (Ministerio de Economia y Competitividad).Blanes Campos, C.; Ortiz Sánchez, MC.; Mellado Arteche, M.; Beltrán Beltrán, P. (2015). Assessment of eggplant firmness with accelerometers on a pneumatic robot gripper. Computers and Electronics in Agriculture. 113:44-50. https://doi.org/10.1016/j.compag.2015.01.013S445011

Learning-based robotic manipulation for dynamic object handling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronic Engineering at the School of Food and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand

Figures are re-used in this thesis with permission of their respective publishers or under a Creative Commons licence.Recent trends have shown that the lifecycles and production volumes of modern products are shortening. Consequently, many manufacturers subject to frequent change prefer flexible and reconfigurable production systems. Such schemes are often achieved by means of manual assembly, as conventional automated systems are perceived as lacking flexibility. Production lines that incorporate human workers are particularly common within consumer electronics and small appliances. Artificial intelligence (AI) is a possible avenue to achieve smart robotic automation in this context. In this research it is argued that a robust, autonomous object handling process plays a crucial role in future manufacturing systems that incorporate robotics—key to further closing the gap between manual and fully automated production. Novel object grasping is a difficult task, confounded by many factors including object geometry, weight distribution, friction coefficients and deformation characteristics. Sensing and actuation accuracy can also significantly impact manipulation quality. Another challenge is understanding the relationship between these factors, a specific grasping strategy, the robotic arm and the employed end-effector. Manipulation has been a central research topic within robotics for many years. Some works focus on design, i.e. specifying a gripper-object interface such that the effects of imprecise gripper placement and other confounding control-related factors are mitigated. Many universal robotic gripper designs have been considered, including 3-fingered gripper designs, anthropomorphic grippers, granular jamming end-effectors and underactuated mechanisms. While such approaches have maintained some interest, contemporary works predominantly utilise machine learning in conjunction with imaging technologies and generic force-closure end-effectors. Neural networks that utilise supervised and unsupervised learning schemes with an RGB or RGB-D input make up the bulk of publications within this field. Though many solutions have been studied, automatically generating a robust grasp configuration for objects not known a priori, remains an open-ended problem. An element of this issue relates to a lack of objective performance metrics to quantify the effectiveness of a solution—which has traditionally driven the direction of community focus by highlighting gaps in the state-of-the-art. This research employs monocular vision and deep learning to generate—and select from—a set of hypothesis grasps. A significant portion of this research relates to the process by which a final grasp is selected. Grasp synthesis is achieved by sampling the workspace using convolutional neural networks trained to recognise prospective grasp areas. Each potential pose is evaluated by the proposed method in conjunction with other input modalities—such as load-cells and an alternate perspective. To overcome human bias and build upon traditional metrics, scores are established to objectively quantify the quality of an executed grasp trial. Learning frameworks that aim to maximise for these scores are employed in the selection process to improve performance. The proposed methodology and associated metrics are empirically evaluated. A physical prototype system was constructed, employing a Dobot Magician robotic manipulator, vision enclosure, imaging system, conveyor, sensing unit and control system. Over 4,000 trials were conducted utilising 100 objects. Experimentation showed that robotic manipulation quality could be improved by 10.3% when selecting to optimise for the proposed metrics—quantified by a metric related to translational error. Trials further demonstrated a grasp success rate of 99.3% for known objects and 98.9% for objects for which a priori information is unavailable. For unknown objects, this equated to an improvement of approximately 10% relative to other similar methodologies in literature. A 5.3% reduction in grasp rate was observed when removing the metrics as selection criteria for the prototype system. The system operated at approximately 1 Hz when contemporary hardware was employed. Experimentation demonstrated that selecting a grasp pose based on the proposed metrics improved grasp rates by up to 4.6% for known objects and 2.5% for unknown objects—compared to selecting for grasp rate alone. This project was sponsored by the Richard and Mary Earle Technology Trust, the Ken and Elizabeth Powell Bursary and the Massey University Foundation. Without the financial support provided by these entities, it would not have been possible to construct the physical robotic system used for testing and experimentation. This research adds to the field of robotic manipulation, contributing to topics on grasp-induced error analysis, post-grasp error minimisation, grasp synthesis framework design and general grasp synthesis. Three journal publications and one IEEE Xplore paper have been published as a result of this research

Tactile Sensing with Accelerometers in Prehensile Grippers for Robots

This is the author’s version of a work that was accepted for publication in Mechatronics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mechatronics, Vol. 33, (2016)] DOI 10.1016/j.mechatronics.2015.11.007.Several pneumatic grippers with accelerometers attached to their fingers have been developed and tested. The first gripper is able to classify the hardness of different cylinders, estimate the pneumatic pressure, monitor the position and speed of the gripper fingers, and study the phases of the action of grasping and the influence of the relative position between the gripper and the cylinders. The other grippers manipulate and assess the firmness of eggplants and mangoes. To achieve a gentle manipulation, the grippers employ fingers with several degrees of freedom in different configurations and have a membrane filled with a fluid that allows their hardness to be controlled by means of the jamming transition of the granular fluid inside it. To assess the firmness of eggplants and mangoes and avoid the influence of the relative position between product and gripper, the firmness is estimated while the products are being held by the fingers. Better performance of the accelerometers is achieved when the finger employs the granular fluid. The article presents methods for designing grippers capable of assessing the firmness of irregular products with accelerometers. At the same time, it also studies the possibilities that accelerometers, attached to different pneumatic robot gripper fingers, offer as tactile sensors. (C) 2015 Elsevier Ltd. All rights reserved.This research is supported by the MANI-DACSA project (Grant number RTA2012-00062-C04-02), which is partially funded by the Spanish Government (Ministerio de Economia y Competitividad.).Blanes Campos, C.; Mellado Arteche, M.; Beltrán Beltrán, P. (2016). Tactile Sensing with Accelerometers in Prehensile Grippers for Robots. Mechatronics. 33:1-12. https://doi.org/10.1016/j.mechatronics.2015.11.007S1123

Advances in soft grasping in agriculture

Agricultural robotics and automation are facing some challenges rooted in the high variability 9 of products, task complexity, crop quality requirement, and dense vegetation. Such a set of 10 challenges demands a more versatile and safe robotic system. Soft robotics is a young yet 11 promising field of research aimed to enhance these aspects of current rigid robots which 12 makes it a good candidate solution for that challenge. In general, it aimed to provide robots 13 and machines with adaptive locomotion (Ansari et al., 2015), safe and adaptive manipulation 14 (Arleo et al., 2020) and versatile grasping (Langowski et al., 2020). But in agriculture, soft 15 robots have been mainly used in harvesting tasks and more specifically in grasping. In this 16 chapter, we review a candidate group of soft grippers that were used for handling and 17 harvesting crops regarding agricultural challenges i.e. safety in handling and adaptability to 18 the high variation of crops. The review is aimed to show why and to what extent soft grippers 19 have been successful in handling agricultural tasks. The analysis carried out on the results 20 provides future directions for the systematic design of soft robots in agricultural tasks.Comment: Chapter 12 of the book entitled "Advances in agri-food robotics

Directly 3D Printed, Pneumatically Actuated Multi-Material Robotic Hand

Soft robotic manipulators with many degrees of freedom can carry out complex tasks safely around humans. However, manufacturing of soft robotic hands with several degrees of freedom requires a complex multi-step manual process, which significantly increases their cost. We present a design of a multi-material 15 DoF robotic hand with five fingers including an opposable thumb. Our design has 15 pneumatic actuators based on a series of hollow chambers that are driven by an external pressure system. The thumb utilizes rigid joints and the palm features internal rigid structure and soft skin. The design can be directly 3D printed using a multi-material additive manufacturing process without any assembly process and therefore our hand can be manufactured for less than 300 dollars. We test the hand in conjunction with a low-cost vision-based teleoperation system on different tasks.Comment: 7 pages, 16 figure