Mechanically responsive materials that are able to sense and respond to external stimuli have important applications in soft robotics and the formation of artificial muscles, such as intelligent ...electronics, prosthetic limbs, comfort-adjusting textiles and miniature actuators for microfluidics. However, previous artificial muscles based on polymer materials are insufficient in generating large actuations, fast responses, diverse deformation modes and high cycle performances. To this end, carbon nanotubes (CNTs) are proposed as promising candidates to be assembled into artificial muscles, as they are lightweight, robust and have high surface-to-volume ratios. This protocol describes a reproducible biomimetic method for preparing a family of hierarchically arranged helical fiber (HHF) actuators that are responsive to solvents and vapors. These HHFs are produced through helical assembly of CNTs into primary fibers and further twisting of the multi-ply primary fibers into a helical structure. A large number of nanoscale gaps between the CNTs and micron-scale gaps between the primary fibers ensure large volume changes and fast responses upon the infiltration of solvents and vapors (e.g., water, ethanol, acetone and dichloromethane) by capillarity. The modes of shape transformations can be modulated precisely by controlling how the CNTs are assembled into primary fibers, multi-ply primary fibers, HHFs and hierarchical springs. This protocol provides a prototype for preparing actuators with different fiber components. The overall time required for the preparation of HHF actuators is 17 h.
How does the increasing use of robots affect the mental health of workers? To investigate this question, we combine individual mental health data from the German Socioeconomic Panel with data on the ...stock of robots in 14 manufacturing sectors provided by the International Federation of Robotics for the period 2002–2018. Using mediation analysis and an instrumental variable approach, we find that higher robot intensity is associated with deteriorating mental health, an effect that is mainly driven by worries about job security and a lower sense of achievement on the job. A heterogeneity analysis reveals that higher robot intensity has particularly severe negative effects on the mental health of workers close to retirement, in low-skilled occupations and performing routine jobs. Women and men are affected similarly, as are workers of all educational levels. Our results indicate the presence of hidden (health) costs of automation that policymakers need to address.
•We investigate the effect of automation on the mental health of workers in Germany.•To do so, we apply a mediation analysis and an instrumental variable approach.•Higher robot intensity is associated with deteriorating mental health.•Worries about job security and feelings of achievement are mediators of the effect.•A heterogeneity analysis reveals crucial differences across groups.•Workers who are older, low skilled or in routine intensive jobs are affected most.
The article deals with the concept of building an automated system for the harvesting of apple crops. This system is a robotic complex mounted on a tractor cart, including an industrial robot and a ...packaging system with a container for fruit collection. The robot is equipped with a vacuum gripper and a vision system. A generator for power supply, a vacuum pump for the gripper and an equipment control system are also installed on the cart. The developed automated system will have a high degree of reliability that meets the requirements of operation in the field.
Abstract
Robot program interpreter is an important tool for robot to realize real-time control and fast feedback. In order to simplify the process of robot programming and improve the interpretation ...efficiency of robot program, an easy-to-use and efficient industrial robot program interpreter is designed and implemented in this paper. The interpreter divides the robot program into two parts: variable definition and instruction call. The process of interpretation includes lexical analysis, syntactic analysis, semantic analysis and instruction interpretation modules. Using flex and bison tools to assist in the generation of lexical and syntactic analysis programs, this paper proposes child-sibling notation (CSN) to construct a syntax tree. In semantic analysis, the red-black tree structure of the map container is used to create a symbol table and record variable information. By the way of presetting type checking codes, errors in the program can be reported and handled. Finally, the interpreter traverses the syntax tree with depth-first algorithm, and calls the corresponding control function while interpreting the instruction sentence to execute the motion of robot. The experimental results show that the designed interpreter has high efficiency and stability in interpreting the robot program and meets the operational requirements of industrial robots.
The effect of disruptive technologies unrelated to the energy sector, such as additive manufacturing (AM), tends to be overlooked in energy scenarios. The present research assessed the potential ...effect of AM on the global energy demand in four energy scenarios for 2050 with extended versus limited globalisation and limited versus extensive adoption of AM. These scenarios were developed and applied for two cases, namely the aerospace sector and the construction sector, analysing the effect of AM on each phase in the value chain. In the aerospace sector, energy savings of 5–25% can be made, with the largest effect in the use phase because of weight reduction. In the construction sector, energy savings of 4–21% are achievable, with the largest effects in the feedstock, transport and use phases. Extrapolated to the global energy demand in 2050, a reduction of 26–138EJ/yr, equivalent to 5–27% of global demand is achievable. It is recommended that energy policymakers should consider integrating AM and other disruptive technologies, such as robotics and the Internet of Things, into their long-term energy planning, policies and programmes, including Nationally Determined Contributions under the Paris Agreement on climate change.
•The potential effect of additive manufacturing on global energy use was assessed.•Disruptive technologies unrelated to energy tend to be overlooked in energy scenarios.•Potential energy savings in aerospace are 5–25% and in construction 4–21% by 2050.•Additive manufacturing can lead to a 5–27% reduction in global energy use in 2050.•Energy policy makers should integrate disruptive technologies into their policies.
Multimodal data fusion (MMDF) is the process of combining disparate data streams (of different dimensionality, resolution, type, etc.) to generate information in a form that is more understandable or ...usable. Despite the explosion of data availability in recent decades, as yet there is no well-developed theoretical basis for multimodal data fusion, i.e., no way to determine a priori which approach is best suited to combine an arbitrary set of available data to achieve a stated goal for a given application. This has resulted in exploration of a wide variety of approaches across numerous domains but as yet very little integration of conclusions at a meta (cross-disciplinary) level. In response, this manuscript poses the following questions: (1) How convergent (or divergent) are approaches within single disciplines? (2) How similar are the challenges posed across different disciplines, i.e., might there be opportunity for successes in MMDF achieved in one field to inform progress in other areas as well? and (3) Where are the outstanding gaps in MMDF research, and what does this imply as targets for high impact research in the coming years? To begin to answer these questions, an apples-to-apples comparison of the literature of nine stakeholder-centric engineering domains (civil engineering, transportation, energy, environmental engineering, food engineering, critical care (healthcare), neuroscience, manufacturing/automation, and robotics) was created by quantifying the numbers and dimensionalities of modalities and sensors in each published project and classifying the algorithms used and purposes for which they are used. Within disciplines, it is shown there is often a tendency for use of similar methodologies, both in choice of level of fusion and data algorithm class. Yet this analysis also reveals that many problem types (defined by data dimensionality, modality number and type, and fusion purpose) are shared across different domains and are approached differently in those domains, e.g., transportation problems have similar characteristics to critical care, food science, robotics, and civil engineering. Of the disciplines studied, most (>75%) share problem characteristics with 3–5 others; to support leveraging these resources, lookup tables indexed by data dimensions, number of modalities, etc. are provided as a starting point for cross-disciplinary MMDF literature searches for new applications. Critical gaps identified are (1) a drop off of the number of published studies with increasing number of distinct modalities and (2) a dearth of publications tackling challenges with high dimensionality inputs, especially time-series 2D and 3D data. These gaps may point to topics where algorithm development will be fruitful to enable future solutions as video and other high-dimensionality sensors decrease in price. Finally, the lack of a shared vocabulary across disciplines makes analyses like the one conducted here challenging, as does the often implicit incorporation of expert knowledge into design; therefore progress toward a better leveraging of the current state of knowledge and toward a theoretical MMDF framework depends critically on improved cross-disciplinary communication and coordination on this topic.
•Multimodal data fusion approaches are used across widely disparate disciplines.•Potentially fruitful cross-disciplinary insights are identified via a meta-analysis.•Fusion algorithm development is needed for high dimensionality and time-series data.•Shared vocabulary for multimodal data fusion could spur successes across disciplines.
The complexity of robotic path planning problems in industrial manufacturing increases significantly with the current trends of product individualization and flexible production systems. In many ...industrial processes, a robotic tool has to follow a desired manufacturing path most accurately, while certain deviations, also called process tolerances and process windows, are allowed. In this work, a path planning framework is proposed, which systematically incorporates all process degrees of freedom (DoF), tolerances and redundant DoF of the considered manufacturing process as well as collision avoidance. Based on the specified process DoF and tolerances, the objective function and the hard and soft constraints of the underlying optimization problem can be easily parametrized to find the optimal joint-space path. By providing the analytical gradients of the objective function and the constraints and utilizing modern multi-core CPUs, the computation performance can be significantly improved. The proposed path planning framework is demonstrated for an experimental drawing process and a simulated spraying process. The planner is able to solve complex planning tasks of continuous manufacturing paths by systematically exploiting the process DoF and tolerances while allowing to move through singular configurations, which leads to solutions that cannot be found by state-of-the-art concepts.
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•Flexible robotic path planning for complex industrial processes.•Path planning framework considers the process properties.•Process properties are process tolerances and windows, constraints, redundant DoF.•Optimization-based approach computes multiple joint-space paths in parallel.•Validation of path planner with two different scenarios (drawing/spraying process).
Soft actuators are of great technological interest and one class of these is made from ionic polymer-metal composites (IPMCs). It has been established that replacement of water with an ionic liquid ...(IL) in IPMCs results in larger actuation response and considerably longer operating life. However, the rate of displacement of IL-based IPMCs is very low. In the current work, IPMC actuators were fabricated using Nafion membrane and an imidazolium-based IL. The effects of incorporating the IL with and without Li+ ions were followed using electromechanical and electrochemical measurements and were compared with the corresponding behavior of water-based Li+-exchanged and un-exchanged IPMC actuators. The addition of Li+ ions to the IL-based system resulted in dramatic increases in the capacitance, ionic conduction, operating life and in the displacement rate of the actuator. This strategy is of considerable interest for enabling the use of IPMC-based soft actuators in medicine and robotics.
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•Preparation of Li incorporated IL-swollen IPMC actuators.•Enhancing effect of Li ions on ionic conduction and capacitance of IL-swollen IPMCs.•Li ions increased rate of displacement and operating life of IL-swollen IPMCs.
By automating tasks with precision and efficiency, industrial robots help minimize resource utilization and emissions, making them indispensable allies in our quest to minimize our ecological ...footprint. The core intention of the present article is to scrutinize the impact of industrial robots on the ecological footprint in ten leading industrial artificial intelligence nations (Singapore, South Korea, Japan, Germany, Sweden, Denmark, USA, China, France, and Italy) from 2007 to 2020. Prior investigations have chosen panel data methodologies to detect the association between industrial robots and ecological footprint. Nonetheless, these studies often overlooked the variations in this relationship across different countries. In contrast, this article choses the Quantile-on-Quantile approach to assess this relationship on a country-specific basis. This methodology offers a comprehensive global perspective while delivering tailored insights relevant to each nation. The findings suggest that industrial robots improve environmental quality by decreasing ecological footprint across different data quantiles in chosen nations. The findings also underline that the asymmetries between our variables differ from country to country. These revelations underscore the importance of policymakers carefully assessing and skillfully managing strategies related to both industrial robots and the ecological footprint.
•The study analyzes the industrial robots-ecological footprint nexus.•A novel methodology, ‘Quantile-on-Quantile (QQ)’, is used.•The data for top ten industrial artificial intelligence nations is used.•Industrial robots are used to represent industrial artificial intelligence.•It is found that industrial robots reduce ecological footprint.
The increasing use of robotics in the work of co-workers poses some new problems in terms of occupational safety and health. In the workplace, industrial robots are being used increasingly. During ...operations such as repairs, unmanageable, adjustment, and set-up, robots can cause serious and fatal injuries to workers. Collaborative robotics recently plays a rising role in the manufacturing filed, warehouses, mining agriculture, and much more in modern industrial environments. This development advances with many benefits, like higher efficiency, increased productivity, and new challenges like new hazards and risks from the elimination of human and robotic barriers.
In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.
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