The technology used for the 3D printing of buildings from concrete is currently a very relevant and developing topic and appears to be especially advantageous in terms of sustainable production. An important aspect of the sustainability assessment is the energy efficiency of the printing robots. Printing robots consume a significant amount of energy when printing. It is important to analyse this energy thoroughly and to be able to predict it in order to optimise the movement and control of printing robots to reduce energy consumption. In that paper, we analyse in detail the energy consumption of printing robots, which has not yet been thoroughly investigated in the context of 3D printing building applications. We present a methodology to develop an energy consumption model for a printing robot, specifically developed and optimized for this technology. Our methodology incorporates an innovative approach to determine reduced-efficiency maps, allowing for the inclusion of difficult-to-measure drive efficiency parameters in the model. This results in a comprehensive model of the energy consumption of the printing robot, reflecting its operating characteristics in a real-world environment. An open control system of the printing robot is used for the measurement of energy quantities, and specially developed software tools are introduced. We also present the first direct comparison of the energy consumption of different printing robots when following a uniform printing trajectory. The comparison is made based on the presented methodology to obtain and compare actual energy data from workplaces with printing robots. The methodology combines measured data with energy simulations from ABB RobotStudio, enabling energy comparisons between industrially articulated robots and real printing robots, including the ABB IRB4600, the gantry printing robot, and the printing robot. The experiments clearly demonstrate that the kinematic structure of printing robots significantly affects their energy consumption in 3D printing concrete. Based on the conducted methodologies and analyses, we identify key aspects of energy consumption of printing robots in 3D Construction Printing or 3D Concrete Printing (3DCP) technology. In doing so, we bring a new perspective and provide a basis for further research and development in this previously understudied area.

Three-dimensional concrete printing technology is currently a very topical and developing subject. There is a large number of applications worldwide where this technology can be used. In connection with this technology, the development of custom industrial robotic systems and their control is essential. Conventional closed-loop control system platforms do not provide sufficiently flexible solutions. This paper presents a control system for a unique printing robot that, thanks to its openness and unified platform, will enable simple and fast analysis and testing of key aspects in terms of control and guidance of the printing robot for additive manufacturing applications in the construction industry. The aim of this paper is to introduce the concept of an open PLC-based control system and to demonstrate its usefulness in the task of designing and implementing model-based control. All steps, from the analysis of the printing robot itself and identification of inertial parameters to the actual design and implementation of the control, can be executed in a unified Matlab/Simulink environment using various add-ons and toolboxes thanks to the open control system platform. This solution brings significant savings in terms of programming and prototyping time. The open control system was used to control an experimental model of a printing robot, serving as a test bed for the final version of the printing robot, and the results obtained were evaluated.

Robotic workplaces are now commonplace and not just in modern automotive companies. Robots help to reduce the cycle time of assembly lines and enhance quality assurance regarding handmade production. The demands placed on a robotic workplace have changed greatly since the first standalone installation. Complex assembly lines incorporating robotic cell and multi robot cooperation represented a typical automotive arrangement even before the Industry 4.0 era. The complexity of the system leads to new challenges in control and optimization of each robotic controller, cell and workshop. Robots typically use technology extension, e.g. for welding, gluing, painting and testing. Overall reliability of process controls and robot controls are one of the factors which determine the financial outlay of the entire workshop. This article discusses some possible techniques and measures required to achieve the desired situation in a robotic workplace. All of these techniques underwent testing in the welding shops of the Skoda Auto, a.s. company.

This paper describes the development of an algorithm robotic plug-in of a charging system for mobile platform. In the first chapter, there is a short overview of possibilities of automatic plug-in system, including proprietary industrial solution. In the main part, there is a description of the system based on UR robot with build-in force torque sensors and Intel RealSense Camera. This camera combines IR depth lens with regular RGB camera and six DOF inertial sensor, which is used in our application too. The conventional solution of this problem is usually based on RGB image processing in various state of the art, from simple pattern matching, neural network, or genetic algorithm to complex AI solution. The quality of the solution mostly depends on robustness of image processing. In our cases, we use simple sensor fusion. Thanks to multiple information and constrain of values, we can assume, if the algorithm is proceeding successfully or not. The system uses the internal parameters of the robotic arm, e.g., end-effector position and orientation and force-torque information in tool center point. The next information is RGB camera image and camera depth image, and the inertial unit build in camera. The other important information is the location of the vehicle inlet on the mobile platform, where the shape of mobile platform is considered as a constrain for image processing. The system is validated only on a physical model with CCS type 2 plug and vehicle inlet, because the mobile platform is under construction by another team.

BACKGROUND Frailty affects more than 20% of older adults and increases falls, disability, and health care use. Traditional rehabilitation is limited by accessibility barriers. Interactive digital health interventions (DHIs), particularly mobile health (mHealth) programs integrating wearable activity trackers with behavioral coaching, may offer scalable alternatives. OBJECTIVE This systematic review and meta-analysis examined the effectiveness of interactive DHIs compared with conventional rehabilitation for physical function and physical activity in older adults with frailty or prefrailty, and whether intervention modality and behavioral coaching components influence outcomes. METHODS The authors searched MEDLINE, Embase, Cochrane Library, Airiti, CNKI, and WanFang through August 31, 2023, for randomized controlled trials (RCTs) involving adults aged ≥65 years with frailty or prefrailty. Primary outcomes included grip strength, Timed Up and Go (TUG), gait speed, and the Fried Frailty Index; secondary outcomes were moderate-to-vigorous physical activity (MVPA) and step count. Duplicate records were identified and removed automatically using PICO Portal (Evidence Partners). All subsequent screening of titles, abstracts, and full-text articles was performed manually by two independent reviewers without the use of automated eligibility tools; disagreements were resolved by a third reviewer. Two reviewers independently screened studies, extracted data, and assessed risk of bias (RoB 2); certainty of evidence was rated using GRADE. Random-effects meta-analyses were performed with prespecified subgroup and sensitivity analyses. RESULTS Nine RCTs (358 participants) were included. Pooling all trials, DHIs showed no clear superiority over conventional rehabilitation for primary functional outcomes, with low to very low certainty of evidence. DHIs produced a robust, clinically important increase in MVPA of 14.31 minutes per valid day (95% CI 12.45–16.16; 2 trials; I2 = 0%). Sensitivity analyses excluding high risk-of-bias trials revealed clinically meaningful improvements in gait speed (mean difference [MD] 0.11 m/s), Fried Frailty Index (MD −1.00 points), and daily step count (approximately 480 additional steps). Modality-based subgroup analyses showed that mHealth programs incorporating behavioral coaching achieved superior improvements in grip strength (MD 2.90 kgf) and TUG (MD −6.57 seconds, exceeding the minimal clinically important difference) compared with exergame-based interventions. Findings for the Fried Frailty Index, MVPA, and step count should be interpreted with caution, as each was informed by only two trials (73 participants total). CONCLUSIONS DHIs may increase MVPA and may improve selected functional outcomes, including gait speed and frailty status, when implemented with sufficient fidelity, but the certainty of evidence remains low to very low. mHealth interventions integrating wearable activity tracking and behavioral coaching appear more promising than stand- alone exergames, although confirmatory trials are needed before firm clinical prioritization. Future trials should evaluate long-term outcomes, economic impacts, and implementation fidelity. CLINICALTRIAL PROSPERO registration number CRD42023462594; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023462594