Warehouse robot market boosted by Covid pandemic and technological innovations

Purpose This paper aims to provide details of recent commercial and technological developments that are driving robotic warehouse automation. Design/methodology/approach Following a short introduction, this first provides a commercial background and identifies the factors driving the market growth. It then gives examples of robotics companies, products and applications that exploit innovations in artificial intelligence (AI). It then considers future prospects, and finally, brief conclusions are drawn. Findings Amazon’s acquisition of Kiva led to a community of new robot manufacturers and the realisation by major e-commerce companies that robotic automation would be required to maintain competitiveness. The Covid pandemic caused a surge in e-commerce and a critical shortage of labour, which further highlighted the need for automation and boosted robotic deployments. Recent advances in AI have resulted in a rapidly growing community of companies producing AI-powered robots which offer advanced capabilities such as mixed product picking, sorting and kitting. These are being deployed by a growing number of e-commerce and logistics companies and are paving the way towards ever-higher levels of warehouse automation. Full automation will soon become a reality. Originality/value This paper identifies the factors driving the rapidly developing warehouse robot business by considering the companies, products, technology and applications.

A Review of the State-of-Art, Limitations, and Perspectives of Machine Vision for Grape Ripening Estimation

This work highlights the most recent machine vision methodologies and algorithms proposed for estimating the ripening stage of grapes. Destructive and non-destructive methods are overviewed for in-field and in-lab applications. Integration principles of innovative technologies and algorithms to agricultural agrobots, namely, Agrobots, are investigated. Critical aspects and limitations, in terms of hardware and software, are also discussed. This work is meant to be a complete guide of the state-of-the-art machine vision algorithms for grape ripening estimation, pointing out the advantages and barriers for the adaptation of machine vision towards robotic automation of the grape and wine industry.

A comparative analysis of indicators and factors of robotization in Russia and Poland

Subject. This article analyzes the indicators of robot-based application and economic development, and explores the particularities of the robot-based application development in Russia and Poland. Objectives. The article aims to identify trends in the density of robot-based application in both countries, and the relationship between economic indicators and this density in order to use them to develop recommendations for accelerating the development of domestic robotics. Methods. For the study, we used the methods of analysis and modeling. Results. The article finds that in Poland, the density of robot-based application got increased at a significant rate due to the growth of production in the manufacturing industry. As the experience of Poland shows, the development of this industry and the export of high-tech products stimulate the use of industrial robots and the expansion of robotic automation of production. Conclusions. The introduction of robot-based application in production processes increases the competitiveness of the economy. This is especially true for Russia in the context of sanctions and the course for re-industrialization. In Russia, to increase the density of robot-based application, it is necessary to significantly increase the gross accumulation of fixed capital, the volume of industrial production and manufacturing industry, and primarily, the production of machinery and equipment, machine tool construction, and the electronic engineering industry.

Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table

Deburring is recognized as an ideal technology for robotic automation. However, since the low stiffness of the robot can affect the deburring quality and the performance of an industrial robot is generally inhomogeneous over its workspace, a cell setup must be found that allows the robot to track the toolpath with the desired performance. In this work, the problems of robotic deburring are addressed by integrating components commonly used in the machining industry. A rotary table is integrated with the robotic deburring cell to increase the effective reach of the robot and enable it to machine a large workpiece. A genetic algorithm (GA) is used to optimize the placement of the workpiece based on the stiffness of the robot, and a local minimizer is used to maximize the stiffness of the robot along the deburring toolpath. During cutting motions, small table rotations are allowed so that the robot maintains high stiffness, and during non-cutting motions, large table rotations are allowed to reposition the workpiece. The stiffness of the robot is modeled by an artificial neural network (ANN). The results confirm the need to optimize the cell setup, since many optimizers cannot track the toolpath, while for the successful optimizers, a performance imbalance occurs along the toolpath.

Robotic automation of “blind” and two-photon targeted patch-clamp electrophysiology in vivo

A review paper on the current state of the art in robotic automation of in vivo patch-clamp electrophysiology

Robotic automation of “blind” and two-photon targeted patch-clamp electrophysiology in vivo

A review paper on the current state of the art in robotic automation of in vivo patch-clamp electrophysiology

Digitalization of waste management: Insights from German private and public waste management firms

Policymakers, practitioners, and scholars have long-lauded digital technologies, such as smart waste containers or artificial intelligence for material recognition and robotic automation, as key enablers to more effective and efficient waste management. While these advances promise an increasingly digitalized future for collecting, sorting, and recycling waste material, little is known about the current extent of digitalization by waste management firms. Available studies focus on firms’ digitalization intentions, largely neglecting the level of actual adoption of digital technologies, and do not differentiate the level of digitalization alongside different steps of the waste management value chain. Our study reports on a cross-sectional descriptive survey that captures current digitalization efforts and strategies of 130 public and private waste management firms in Germany. We analyze their levels of digitalization along with different steps of the waste management value chain, explore their different objectives, approaches, and transformational measures with regard to digitalization. Our findings reveal that while the perceived importance of digitalization in the waste management sector continues to grow, the actual adoption of advanced digital technologies falls notably behind intentions reported in 2016 and 2017. We explore the reasons for this gap, point out so far largely ignored research opportunities, and derive recommendations for waste management firms and associations.