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In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches.

A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper.

The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems.

The usage of the proposed approach can be valuable to industrial corporations, as it allows for improved workflows, maximization of available robotic operations and improvement of efficiency.

To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

The growing complexity of industrial robot work‐cells calls for the use of advanced orchestration techniques to promote flexibility and reusability. This paper aims to present a solution based on service‐oriented platforms that endorses the separation of concerns, coordination and execution.

This paper starts with the evaluation of available tools for the orchestration and service generation. Endorsing the missing features depicted in that evaluation, the paper describes developments of concepts and software and the evaluation made.

From the early evaluations made in this paper, the SCXML‐based purposed language is more adapted to the industrial robotic cell scenario than existing alternatives. The generation of services allow the integration without knowledge from any programming language.

This approach's main drawback, as described by some users, was the lack of some programming features: simple math operations and conditional statements.

This paper fulfils two partially unsolved problems: adequate languages for orchestration of service oriented on the device level and purposes techniques for the specification of services using robot programming languages.

This paper aims to propose an automated framework for agile development and simulation of robotic palletizing cells. An automatic offline programming tool, for a variety of robot brands, is also introduced.

This framework, named AdaptPack Studio, offers a custom-built library to assemble virtual models of palletizing cells, quick connect these models by drag and drop, and perform offline programming of robots and factory equipment in short steps.

Simulation and real tests performed showed an improvement in the design, development and operation of robotic palletizing systems. The AdaptPack Studio software was tested and evaluated in a pure simulation case and in a real-world scenario. Results have shown to be concise and accurate, with minor model displacement inaccuracies because of differences between the virtual and real models.

An intuitive drag and drop layout modeling accelerates the design and setup of robotic palletizing cells and automatic offline generation of robot programs. Furthermore, A* based algorithms generate collision-free trajectories, discretized both in the robot joints space and in the Cartesian space. As a consequence, industrial solutions are available for production in record time, increasing the competitiveness of companies using this tool.

The AdaptPack Studio framework includes, on a single package, the possibility to program, simulate and generate the robot code for four different brands of robots. Furthermore, the application is tailored for palletizing applications and specifically includes the components (Building Blocks) of a particular company, which allows a very fast development of new solutions. Furthermore, with the inclusion of the Trajectory Planner, it is possible to automatically develop robot trajectories without collisions.

In this study, a critical literature review was utilized in order to provide a clear review of the relevant existing studies. The literature was analyzed using the meta-synthesis technique to evaluate and integrate the findings in a single context.

Digital transformation in construction requires employing a wide range of various technologies. There is significant progress of research in adopting technologies such as unmanned aerial vehicles (UAVs), also known as drones, and immersive technologies in the construction industry over the last two decades. The purpose of this research is to assess the current status of employing UAVs and immersive technologies toward digitalizing the construction industry and highlighting the potential applications of these technologies, either individually or in combination and integration with each other.

The key findings are: (1) UAVs in conjunction with 4D building information modeling (BIM) can be used to assess the project progress and compliance checking of geometric design models, (2) immersive technologies can be used to enable controlling construction projects remotely, applying/checking end users’ requirements, construction education and team collaboration.

A detailed discussion around the application of UAVs and immersive technologies is provided. This is expected to support gaining an in-depth understanding of the practical applications of these technologies in the industry.

The review contributes a needed common basis for capturing progress made in UAVs and immersive technologies to date and assessing their impact on construction projects. Moreover, this paper opens a new horizon for novice researchers who will conduct research toward digitalized construction.