Design of a partially-coupled self-adaptive robotic finger optimized for collaborative robots

This paper presents the design and optimization of a self-adaptive, a.k.a. underactuated, finger targeted to be used with collaborative robots. Typical robots, whether collaborative or not, mostly rely on standard translational grippers for pick-and-place operations. These grippers are constituted from an actuated motion platform on which a set of jaws is rigidly attached. These jaws are often designed to secure a precise and limited range of objects through the application of pinching forces. In this paper, the design of a self-adaptive robotic finger is presented which can be attached to these typical translational gripper to replace the common monolithic jaws and provide the gripper with shape-adaptation capabilities without any control or sensors. A new design is introduced here and specially optimized for collaborative robots. The kinetostatic analysis of this new design is first discussed and then followed by the optimization of relevant geometric parameters taking into account the specificities of collaborative robots. Finally, a practical prototype attached to a very common collaborative robot is demonstrated. While the resulting finger design could be attached to any translational gripper, specifically targeting collaborative robots as an application allows for more liberty in the choice of certain design parameters and more constraints for others

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Human-Robot Collaborations in Industrial Automation

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

An Ontology-Based Expert System for the Systematic Design of Humanoid Robots

Die Entwicklung humanoider Roboter ist eine zeitaufwendige, komplexe und herausfordernde Aufgabe. Daher stellt diese Thesis einen neuen, systematischen Ansatz vor, der es erlaubt, Expertenwissen zum Entwurf humanoider Roboter zu konservieren, um damit zukünftige Entwicklungen zu unterstützen. Der Ansatz kann in drei aufeinanderfolgende Schritte unterteilt werden. Im ersten Schritt wird Wissen zum Entwurf humanoider Roboter durch die Entwicklung von Roboterkomponenten und die Analyse verwandter Arbeiten gewonnen. Dieses Wissen wird im zweiten Schritt formalisiert und in Form einer ontologischen Wissensbasis gespeichert. Im letzten Schritt wird diese Wissensbasis von einem Expertensystem verwendet, um Lösungsvorschläge zum Entwurf von Roboterkomponenten auf Grundlage von Benutzeranforderungen zu generieren. Der Ansatz wird anhand von Fallstudien zu Komponenten des humanoiden Roboters ARMAR-6 evaluiert: Sensor-Aktor-Controller-Einheiten für Robotergelenke und Roboterhände