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The purpose of this paper is to present the methodology and the results on the design and development of an autonomous, golf ball picking robot, for driving ranges.

The strategy followed to develop a commercial product is presented, based on prior identification requirements, which consist of picking up golf balls on a driving range in a safe and efficient way.

A fully working prototype robot has been developed. It uses two driving wheels and a third cast wheel, and pushes a standard gang which collects the balls from the ground. A hybrid information system was implemented in order to provide a statistically relevant prediction of golf balls location, to optimize the path the robot has to follow in order to reduce time and cost. Autonomous navigation was developed and tested on a simulation environment.

Preliminary results showed that the new path planning algorithm Twin‐RRT* is able to form closed loop trajectories and improve the result over time. Kinematic constraints were already taken into account on the algorithm. This sampling based algorithm has potential usage in solving other TPP (Travelling Purchaser Problem) related problems.

The prototype feasibility is being tested in real driving ranges. It has autonomy of up to 8 h per day. It is capable of collecting up to 1,200 balls in one single journey. It weighs 130 kg and is capable of climbing slopes of up to 22°. The maximum speed is 8 km/h and the robot takes 140 min to completely sweep a 25,000 m² field at 7.2 km/h (2 m/s) average speed.

There are about 30,000 golf practice fields, of which 18,000 are located in the USA and Canada. In some countries the golf industry represents more than 15 per cent of tourism GNP. In a typical practice field, about 10,000 balls have to be picked up every day.

An important contribution of this paper is the algorithm for path planning in order to optimize the ball pick up task, reducing time and cost. There are two patents are pending concerning the technological novelties of this work.

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 research is to provide the necessarily and resourceful information regarding range sensors to select the best fit sensor for robust autonomous navigation. Autonomous navigation is an emerging segment in the field of mobile robot in which the mobile robot navigates in the environment with high level of autonomy by lacking human interactions. Sensor-based perception is a prevailing aspect in the autonomous navigation of mobile robot along with localization and path planning. Various range sensors are used to get the efficient perception of the environment, but selecting the best-fit sensor to solve the navigation problem is still a vital assignment.

Autonomous navigation relies on the sensory information of various sensors, and each sensor relies on various operational parameters/characteristic for the reliable functioning. A simple strategy shown in this proposed study to select the best-fit sensor based on various parameters such as environment, 2 D/3D navigation, accuracy, speed, environmental conditions, etc. for the reliable autonomous navigation of a mobile robot.

This paper provides a comparative analysis for the diverse range sensors used in mobile robotics with respect to various aspects such as accuracy, computational load, 2D/3D navigation, environmental conditions, etc. to opt the best-fit sensors for achieving robust navigation of autonomous mobile robot.

This paper provides a straightforward platform for the researchers to select the best range sensor for the diverse robotics application.

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 purpose of this paper is to propose going one step further in the simulation tools related to agriculture by integrating fleets of mobile robots for the execution of precision agriculture techniques. The proposed new simulation environment allows the user to define different mobiles robots and agricultural implements.

With this computational tool, the crop field, the fleet of robots and the different sensors and actuators that are incorporated into each robot can be configured by means of two interfaces: a configuration interface and a graphical interface, which interact with each other.

The system presented in this article unifies two very different areas – robotics and agriculture – to study and evaluate the implementation of precision agriculture techniques in a 3D virtual world. The simulation environment allows the users to represent realistic characteristics from a defined location and to model different variabilities that may affect the task performance accuracy of the fleet of robots.

This simulation environment, the first in incorporating fleets of heterogeneous mobile robots, provides realistic 3D simulations and videos, which grant a good representation and a better understanding of the robot labor in agricultural activities for researchers and engineers from different areas, who could be involved in the design and application of precision agriculture techniques. The environment is available at the internet, which is an added value for its expansion in the agriculture/robotics family.

The purpose of this paper is to propose a generic platform for a robotic mobile system, seeking to obtain a support tool for under-graduation and graduation activities. Another objective was to gather knowledge in the mobile robotic area in order to provide practical solutions for industrial problems.

The proposed new integrated platform would serve as didactic material for many disciplines, shown to be an ideal platform to teach DC motor drives, stepper motor and motion-control systems. To reach this objective, the ability of the robot to plan its motion autonomously is of vital importance. The control of a mobile robot in dynamic and unstructured environments typically requires efficient processing of data/information to ensure precise navigation and many other applications. Path planning is also one common method of auto-navigation. After the computation of the shortest path, mobile robot can navigate safely and without occlusion.

The developed platform is an integrated system for intelligent software middleware to coordinate many activities in the field of electric drives, robotics, autonomous systems and artificial intelligence.

As a result of the study, this paper contributed to research in the industrial development, principally in the fields of industrial robotics and also in different application purposes such as entertainment, personal use, welfare, education, rehabilitation, etc.

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.

To follow and maintain an appropriate distance to the selected target person (STP), the mobile robot is required to have capabilities: the human detection and tracking and an efficient following strategy with a smooth manner that does not appear threatening to the STP and surroundings. The efficient following strategy must integrate the STP position and the obstacle information to achieve smooth and safe human-following behaviors, especially in unknown environments where robot does not have understandings in advance. The purpose of this study is to propose a robust-adaptive-behavior strategy for mobile robots.

This paper presents a robust-adaptive-behavior strategy (RABS) based on the fuzzy inference mechanism to help the robot follow the STP effectively in various unknown environments with the real-time obstacle avoidance, both indoor and outdoor and on different robot platforms. In which, the traversability of robots’ unknown surrounding environments is analyzed by using the STP position and the obstacle information obtained from the two dimensional laser scan, whose purpose is to choose the highest-traversability-score direction (HTSD) and an adaptive-safe-following distance (ASFD). Then, the HTSD, the ASFD and the current velocity of the robot are considered as inputs of the fuzzy system to adjust its velocity smoothly.

The proposed RABS is verified by a set of experiments using a real big-heavy autonomous mobile robot (BH-AMR), with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg), aiding smart factories. The obtained results have shown that the proposed RABS equips the BH-AMR with the ability to follow the STP smoothly and safely even when the robot is moving at the maximum speed 1.5 (m/s).

In this paper, the autonomous mobile robot considers all environments as unknown even when it is working in mapped environments. This limitation is presented clearly in the future works section.

This proposed method can be used to help the autonomous mobile robot support persons in factories, hospitals, restaurants, supermarkets or at the airports.

This paper presents a RABS, including three new features: a fuzzy-based solution to help human-following robots maintain an appropriate distance to the STP safely and smoothly with the maximum velocity 1.5 (m/s); the proposed fuzzy-based solution, an adaptive vector field histogram and a new approach for the STP tracking is combined to follow the STP and avoid the collision simultaneously in unknown indoor and outdoor environments; the proposed RABS is considered for BH-AMRs (with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg)) to serve real tasks in smart factories.

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).

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 Jack Morrison, CEO and Co-Founder, Scythe Robotics. Morrison shares how he and his co-founders started this innovative company, the milestones and challenges he’s faced and his long-term goals.

Morrison received Bachelor of Arts degrees in Computer Science and German from Bowdoin College. He attended The George Washington University as a PhD student in Computer Science but left to co-found Replica Labs, a producer of software that turns any mobile phone into a high-quality 3D scanner. Morrison served as Replica’s CTO until it was acquired by Occipital in 2016, where he stayed on as a computer vision engineer until co-founding Scythe Robotics in April 2018.

While mowing his lawn in Colorado, Jack Morrison had a sudden insight: what if he could apply the latest robotics technology he was so familiar with to the challenge of commercialized landscaping? In 2018, Morrison teamed up with Replica Labs co-founder Isaac Roberts and Occipital’s Davis Foster, to create Scythe Robotics, a company that builds autonomous robotics solutions for the $105bn commercial landscaping industry. In June 2021, Scythe Robotics emerged from stealth with over $18m in funding with its first commercial product: a transformational, all-electric, fully autonomous mower designed to keep crew productivity high while also increasing the quality of cut and worker safety. The machine features eight high dynamic range cameras and a suite of other sensors that enable it to operate safely in dynamic environments by identifying and responding to the presence of humans, animals and other potential obstacles. Simultaneously, the machine captures valuable property and mower performance data, which helps landscape contractors improve workflow, identify upsell opportunities, schedule more efficiently and manage labor costs. The all-electric powertrain is quiet, emissions-free and radically more reliable than gas-powered manual mowers. Scythe Robotics’ business model is based on Robot as a Service. Instead of buying machines outright, customers are billed by acres mowed. This massively reduces contractors’ expenses and eliminates substantial costs. Scythe Robotics is headquartered in Boulder, Colorado and has offices in Vero Beach, FL and Austin, TX. Scythe is the recipient of the 2020 ALCC (Associated Landscape Contractors CO) Innovation Winner and the 2021 Colorado OEDIT Advanced Industries Grantee.