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The purpose of this paper is to present work which is a part of the Comprehensive Automation for Specialty Crops project (CASC). Desired trajectory tracking objective has been previously performed by using a non‐model based approach in this project. Long distance autonomous drive has been achieved; however the results haven't met the expectations of the project requirements. In order to provide these requirements, this study is conducted. In this study, long distance autonomous trajectory tracking for an orchard vehicle is studied. Besides longitudinal motion, lateral motion of the vehicle is also considered. The longitudinal and lateral errors are objected to keep into a region of less than 10 cm.

Car‐like robot kinematic modeling approach is used to create desired trajectory. In order to control longitudinal velocity and steering angle of the vehicle, a controller methodology is proposed. Stability of the controller proposed is shown by using Lyapunov stability approach.

The proposed model is adapted into a four‐wheeled autonomous orchard vehicle and tested in an experimental orchard for long distance autonomous drives. More than 15 km autonomous drive is successfully achieved and the details are presented in this paper.

In this study, long distance autonomous trajectory tracking for an orchard vehicle is focused. A model based control strategy, including the information about longitudinal and lateral motion of the vehicle, is constructed. A new approach to create steering angles for turning operations of the orchard vehicle is introduced. It is objected that the longitudinal and lateral errors should be less than 10 cm during the trajectory tracking task.

Different machines are already present in the human environment, easing human beings' daily life. In the future, this tendency will be accentuated by integration of numerous robots (e.g. wheeled robots, legged robots, humanoid robots, network sensors, etc.) in the human environment. A wide range of applications, such as those dealing with warehouse management, industrial assembling, military applications, daily‐life tasks, can benefit from multi‐robot systems. The purpose of this paper is to propose an intelligent system for industrial robotics in the logistic field, based on collaboration between heterogeneous robots.

The proposed infrastructure for this multi‐robot system is composed of a robots' network including one humanoid robot, wheeled robots, cameras, and remote computer. All devices can communicate between them by using wireless network. The goal of the humanoid robot is to lead the wheeled robots according to the environment and wheeled robots are used to carry a load. The camera allows providing complementary information about the environment; and thanks to machine learning, this control strategy allows complex tasks to be perormed for these logistic applications.

This concept is implemented on real robots within the frame of a demonstrator including the above‐mentioned kind of robots. The preliminary results, obtained during experimentations, prove the feasibility of the presented strategy for real applications.

The main originalities of this work are, on the one hand, the use of an heterogeneous multi‐robots system for logistic tasks, and on the other hand, the proposed machine learning allows a collaboration task between heterogeneous robots in an autonomous manner.

Recent years have seen a technological change, Industry 4.0, in the manufacturing industry. Human–robot cooperation, a new application, is increasing and facilitating collaboration without fences, cages or any kind of separation. The purpose of the paper is to review mainstream academic publications to evaluate the current status of human–robot cooperation and identify potential areas of further research.

A systematic literature review is offered that searches, appraises, synthetizes and analyses relevant works.

The authors report the prevailing status of human–robot collaboration, human factors, complexity/ programming, safety, collision avoidance, instructing the robot system and other aspects of human–robot collaboration.

This paper identifies new directions and potential research in practice of human–robot collaboration, such as measuring the degree of collaboration, integrating human–robot cooperation into teamwork theories, effective functional relocation of the robot and product design for human robot collaboration.

This paper will be useful for three cohorts of readers, namely, the manufacturers who require a baseline for development and deployment of robots; users of robots-seeking manufacturing advantage and researchers looking for new directions for further exploration of human–machine collaboration.

The purpose of this study is to analyse the factors influencing intention to adopt 3D design digital technologies (3DDTs) by Colombian firms.

A conceptual framework was developed using technology-organization-environment (TOE) and technology acceptance model (TAM) theoretical frameworks. From a sample of 138 firms, a structural equation model was analysed with partial least squares (SEM-PLS).

The study identified that perceived usefulness in the technological dimension; technological competence and top management support in the organizational dimension; and competitive pressures in the environmental dimension, are variables affecting intention to adopt 3DDT. The effects of the mediating variables with respect to intention to adopt the technology are also analysed, such as perceived usefulness on the effect of ease of use; top management support on the effect of technological competence and financial readiness; and competitive pressures on the effect of stakeholder pressure. The model explained 71.1% of the 3DDT intention to adopt.

The model can be used as a guideline to ensure a positive outcome of the 3DDT adoption in organizations. The results could be useful to understand a technological adoption process for digital transformation.

The proposed model integrates some contributions from the TAM and TOE theories and identifies some novel mediating effects that improve its predictive and explanatory power. Furthermore, this is a pioneering study in empirical research on 3DDT in the context of a developing country, specifically in Colombia. The findings from this study provide a foundation for other studies, as well as constructive insights for digital transformation, due to its infancy in an emerging economy.

This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

This article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.

This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.

The proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.

The existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.

Collaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and users. For technologies like collaborative robotics and autonomous driving, which focus on closing the gap between humans and machines, the physical, psychological and emotional needs of human individuals becoming increasingly important in order to ensure effective and safe human–machine interaction. The authors' goal was to conceptualize ways to combine experience from both fields and transfer artificial intelligence knowledge from one to another. By identifying transferable meta-knowledge, the authors will increase quality of artificial intelligence applications and raise safety and contextual awareness for users and environment in both fields.

First, the authors presented autonomous driving and collaborative robotics and autonomous driving and collaborative robotics' connection to artificial intelligence. The authors continued with advantages and challenges of both fields and identified potential topics for transferrable practices. Topics were divided into three time slots according to expected research timeline.

The identified research opportunities seem manageable in the presented timeline. The authors' expectation was that autonomous driving and collaborative robotics will start moving closer in the following years and even merging in some areas like driverless and humanless transport and logistics.

The authors' findings confirm the latest trends in autonomous driving and collaborative robotics and expand them into new research and collaboration opportunities for the next few years. The authors' research proposal focuses on those that should have the most positive impact to safety, complement, optimize and evolve human capabilities and increase productivity in line with social expectations. Transferring meta-knowledge between fields will increase progress and, in some cases, cut some shortcuts in achieving the aforementioned goals.

The purpose of this paper is to provide a review of the interdisciplinary research field, autonomous industrial mobile manipulation (AIMM), with an emphasis on physical implementations and applications.

Following an introduction to AIMM, this paper investigates the missing links and gaps between the research and developments efforts and the real‐world application requirements, in order to bring the AIMM technology from laboratories to manufacturing environments. The investigation is based on 12 general application requirements for robotics: sustainability, configuration, adaptation, autonomy, positioning, manipulation and grasping, robot‐robot interaction, human‐robot interaction, process quality, dependability, and physical properties.

The concise yet comprehensive review provides both researchers (academia) and practitioners (industry) with a quick and gentle overview of AIMM. Furthermore, the paper identifies key open issues and promising research directions to realize real‐world integration and maturation of the AIMM technology.

This paper reviews the interdisciplinary research field, autonomous industrial mobile manipulation (AIMM).

The following paper details a “Q&A interview” conducted by Joanne Pransky, Associate Editor of Industrial Robot Journal, to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned successful business leader, regarding the commercialization and challenges of bringing technological inventions to the market while overseeing a company. The paper aims to discuss these issues.

The interviewee is Dr William “Red” Whittaker, Fredkin Research Professor of Robotics, Robotics Institute, Carnegie Mellon University (CMU); CEO of Astrobotic Technology; and President of Workhorse Technologies. Dr Whittaker provides answers to questions regarding the pioneering experiences of some of his technological wonders in land, sea, air, underwater, underground and space.

As a child, Dr Whittaker built things and made them work and dreamed about space and robots. He has since then turned his dreams, and those of the world, into realities. Dr Whittaker’s formal education includes a BS degree in civil engineering from Princeton and MS and PhD degrees in civil engineering from CMU. In response to designing a robot to cleanup radioactive material at the Three Mile Island nuclear plant, Dr Whittaker established the Field Robotics Center (FRC) in 1983. He is also the founder of the National Robotics Engineering Center, an operating unit within CMU’s Robotics Institute (RI), the world’s largest robotics research and development organization. Dr Whittaker has developed more than 60 robots, breaking new ground in autonomous vehicles, field robotics, space exploration, mining and agriculture. Dr Whittaker’s research addresses computer architectures for robots, modeling and planning for non-repetitive tasks, complex problems of objective sensing in random and dynamic environments and integration of complete robot systems. His current focus is Astrobotic Technology, a CMU spin-off firm that is developing space robotics technology to support planetary missions. Dr Whittaker is competing for the US$20m Google Lunar XPRIZE for privately landing a robot on the Moon.

Dr Whittaker coined the term “field robotics” to describe his research that centers on robots in unconstrained, uncontrived settings, typically outdoors and in the full range of operational and environmental conditions: robotics in the “natural” world. The Field Robotics Center has been one of the most successful initiatives within the entire robotics industry. As the Father of Field Robotics, Dr Whittaker has pioneered locomotion technologies, navigation and route-planning methods and advanced sensing systems. He has directed over US$100m worth of research programs and spearheaded several world-class robotic explorations and operations with significant outreach, education and technology commercializations. His ground vehicles have driven thousands of autonomous miles. Dr Whittaker won DARPA’s US$2m Urban Challenge. His Humvees finished second and third in the 2005 DARPA’s Grand race Challenge desert race. Other robot projects have included: Dante II, a walking robot that explored an active volcano; Nomad, which searched for meteorites in Antarctica; and Tugbot, which surveyed a 1,800-acre area of Nevada for buried hazards. Dr Whittaker is a member of the National Academy of Engineering. He is a fellow of the American Association for Artificial Intelligence and served on the National Academy of Sciences Space Studies Board. Dr Whittaker received the Alan Newell Medal for Research Excellence. He received Carnegie Mellon’s Teare Award for Teaching Excellence. He received the Joseph Engelberger Award for Outstanding Achievement in Robotics, the Advancement of Artificial Intelligence’s inaugural Feigenbaum Prize for his contributions to machine intelligence, the Institute of Electrical and Electronics Engineers Simon Ramo Medal, the American Society of Civil Engineers Columbia Medal, the Antarctic Service Medal and the American Spirit Honor Medal. Science Digest named Dr Whittaker one of the top 100 US innovators for his work in robotics. He has been recognized by Aviation Week & Space Technology and Design News magazines for outstanding achievement. Fortune named him a “Hero of US Manufacturing”. Dr Whittaker has advised 26 PhD students, has 16 patents and has authored over 200 publications. Dr Whittaker’s vision is to drive nanobiologics technology to fulfillment and create nanorobotic agents for enterprise on Earth and beyond (Figure 1).

IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots.

For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making.

AuR has identified the core concepts and domains needed to create an ontology for autonomous robots.

AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance.

Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors’knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots.

The purpose of this paper is to provide a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned entrepreneur regarding his pioneering efforts in starting robotic companies and commercializing technological inventions. The paper aims to discuss these issues.

The interviewee is Brennard Pierce, a world-class robotics designer and serial entrepreneur. Pierce is currently consulting in robotics after exiting from his latest startup as cofounder and chief robotics officer of Bear Robotics. Pierce discusses what led him to the field of robotics, the success of Bear Robotics, the challenges he’s faced and his future goals.

Pierce received a Bachelor of Science in computer science from Exeter University. He then founded his first startup, 5TWO, a custom software company. Always passionate about robotics as a hobby and now wanting to pursue the field professionally, he sold 5TWO to obtain a Master of Science, Robotics degree from the newly formed Bristol Robotics Lab (BRL) at Bristol University. After BRL, where he designed and built a biped robot that learned to walk using evolutionary algorithms, he joined the Robotics Research team at Carnegie Mellon University where he worked on a full-size humanoid robot for a large electronics company, designing and executing simple experiments for balancing. He then spent the next six years as a PhD candidate and robotics researcher at the Technical University Munich (TUM), Institute for Cognitive Science, where he built a compliant humanoid robot and a new generation of field programmable gate array-based robotic controllers. Afterwards, Pierce established the robotic startup Robotise in Munich to commercialize the omni-directional mobile platforms that he had developed at TUM. A couple of years later, Pierce left Robotise to cofound Bear Robotics, a Silicon Valley based company that brings autonomous robots to the restaurant industry. He remained at Bear Robotics for four years as chief robotics officer. He is presently a robotics consultant, waiting for post-COVID before beginning his next robotic startup.

Pierce is a seasoned roboticist and a successful entrepreneur. He has 15+ years’ of unique experience in both designing robotic hardware and writing low level embedded and high level cloud software. During his career he has founded three companies, managed small to middle sized interdisciplinary teams, and hired approximately 100 employees of all levels. Pierce’s robotic startup in Munich, Robotise, was solely based on his idea, design and implementation for an autonomous mobile delivery system. The third company he cofounded, Bear Robotics, successfully raised a $32m Series A funding lead by SoftBank. Bear Robotics is the recipient of the USA’s National Restaurant Association Kitchen Innovation Award; Fast Company’s World Changing Ideas Awards; and the Hospitality Innovation Planet 2020 Award.