Energy Consumption Of Industrial Robots Research Articles

Analysis of Energy Consumption of Robotic Welding Stations

Analysis of the energy consumption of industrial robots during the implementation of a technological task can increase efficiency and minimize production costs, as well as extend the service life of robots, taking into account the rational use of energy sources. An important challenge in designing sustainable production processes is the selection of energy-saving technological equipment supporting the work of robots. Additionally, optimization of robot program codes, including parameters and robot movement paths, determines the energy efficiency of the robotic station. Welding is one of the most popular areas of process robotization due to its ability to increase production efficiency and improve the quality of part assembly compared to manual work. In these studies, an exemplary process of robotic welding of aluminum alloy parts was designed, analyzed, and optimized with an orientation towards sustainable development guidelines. This work also presents a review of current publications discussing the authors’ contribution to achieving energy efficiency in robotic welding processes. The purpose of this analysis is also to indicate directions for the development of future research work in order to minimize energy consumption in production processes with an appropriate level of product quality and increase production efficiency.

Selected Issues, Methods, and Trends in the Energy Consumption of Industrial Robots

Industrial robots, like all machines, require energy to operate, which is why energy efficiency in industrial robotics has been a subject of consideration in recent years in many scientific and industrial centers. Interest in the topic is especially noticeable in Industry 4.0. Research on energy efficiency stems from the emergence of new possibilities in terms of making strategic decisions related to manufacturing processes. As energy-efficient production is an essential part of sustainable development, the energy efficiency of industrial robots must be considered. The need to reduce costs while maintaining quality and increasing production efficiency has necessitated the implementation of modern solutions aimed at reducing electricity consumption. The rational use of electrical energy, especially in the industrial sector, significantly reduces production costs and, consequently, contributes to a company’s profits and competitiveness. This article aims to provide an overview of energy efficiency issues based on recently published articles. This article discusses the appropriate selection of robots, their programming, energy-efficient trajectory planning, and the monitoring of the operation of the robotic system to minimize energy consumption. Typical industrial applications of robots are also mentioned and discussed.

Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design

The paper describes the development of an optimization model for the layout of an industrial robot relative to known locations of served machines and operations to be performed. Robotized material handling applications, defined by trajectories (paths, speed profiles) and final points, are considered in this research. An energy-monitoring framework set up by joint velocities provides input data that are fed to the optimization model. The physical placement of the robot base stands for the decisional variables, while the objective function is represented by the total distance covered by individual joints along established task routes transposed into energy consumption. The values of the decisional variables are restricted by trajectory constraints (waypoints on paths), joint operating values and link dimensions. Modelling technique and practical results using the Microsoft Solver optimization tool from Excel for Microsoft 365, Version 2312 are reported for SCARA-type robots. The performance of the optimization model is compared with actual measurements of consumed energy on an Adept Cobra S600 SCARA robot.

Optimization of Energy Consumption of Industrial Robots Using Classical PID and MPC Controllers

Industrial robots have a key role in the concept of Industry 4.0. On the one hand, these systems improve quality and productivity, but on the other hand, they require a huge amount of energy. Energy saving solutions have to be developed and applied to provide sustainable production. The purpose of this research is to develop the optimal control strategy for industrial robots in order to minimize energy consumption. Therefore, a case study was conducted for the development of two control strategies to be applied to the RV-2AJ Mitsubishi robot arm with 5 DOF, where the system is a nonlinear one. The first examined controller is the classical linear proportional integral derivative (PID) controller, while the second one is the linear model predictive control (MPC) controller. In our study, the performances of both the classical PID model and the linear MPC controller were compared. As a result, it was found that the MPC controller in the execution of the three defined reference trajectories [(1) curve motion, (2) N-shaped motion, and (3) circle motion] was always faster and required less energy consumption, whereas in terms of precision the PID succeeded in executing the trajectory more precisely than the MPC but with higher energy consumption. The main contribution of the research is that the performances of the two control strategies with regard to a complex dynamic system were compared in the case of the execution of three different trajectories. The evaluations show that the MPC controller is, on the one hand, more energy efficient; on the other hand, it provides a shorter cycle time compared to the PID controller.

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone.

A transfer-learning based energy consumption modeling method for industrial robots

Industrial robots have been widely utilized in factories to replace or help employees performing tasks such as handling, welding, and assembly. With the increasing breadth of installation and deployment of industrial robots, it is critical to predict and optimize their energy consumption in order to assure their environmentally friendly characteristics. While the data-driven modeling method based on multi-layer perception has been demonstrated to be a viable way for revealing quantitative relationships between operating parameters and energy consumption for industrial robots regardless of physical process or case-specific information, there are still some limitations in terms of prediction range, modeling efficiency, and sample size requirement. To accelerate model rebuilding and improve model accuracy without collecting a large number of samples simultaneously, this paper proposes a transfer-learning-based model creation method for energy consumption of industrial robots, where the associated operating parameters are analyzed, and structure adjustment strategies for multi-layer perception are planned for various industrial robot systems, then, schemes for the reuse of parameters in well-trained networks on source domain and the fine-tunning of multi-layer perception model are formulated to efficiently build the accurate energy consumption model on target domains. Experiments are conducted on two distinct robot systems, the Epson C4 and the Siasun SR10C, with nonidentical operating parameter structures and sample sizes. The results demonstrate that transfer learning models with fine-tuning strategy outperform previous data-driven modeling methods in terms of model accuracy and modeling efficiency in both of these situations.

A data-driven method for optimizing the energy consumption of industrial robots

With strong reliability and maneuverability, industrial robots play an essential role in the transformation and upgrading of the manufacturing industry, known as industry 4.0. Due to their mass application, it is essential to optimize the energy consumption of industrial robots considering the rising cost of energy resources and deteriorating eco-environment. State of the art solutions have mainly focused on hardware improvement and trajectory-planning-oriented software approaches, implementation of which is, nevertheless, restricted by the commercial feature of control algorithms of industrial robots. In order to explore the potential on energy saving, a data-driven approach for practical energy consumption optimization is proposed in this paper. Different from traditional energy consumption modelling where kinematic and dynamic behaviors of industrial robots are the major concern, this study use an artificial neural network to accurately reveal the quantitative relations between operating parameters and energy consumption, then genetic algorithm to optimize the adjustable parameters to minimize the consumed energy. Experiments are designed and performed on an Epson C4 6-DoF industrial robot. The results demonstrate the applicability and effectiveness of the proposed method in assisting the operating parameter selection and energy saving.

Minimization of the Energy Consumption in Industrial Robots through Regenerative Drives and Optimally Designed Compliant Elements

This paper describes a method for reducing the energy consumption of industrial robots and electrically actuated mechanisms performing cyclic tasks. The energy required by the system is reduced by outfitting it with additional devices able to store and recuperate energy, namely, compliant elements coupled in parallel with axles and regenerative motor drives. Starting from the electromechanical model of the modified system moving following a predefined periodic path, the relationship between the electrical energy and the stiffness and preload of the compliant elements is analyzed. The conditions for the compliant elements to be optimal are analytically derived. It is demonstrated that under these conditions the compliant elements are always beneficial for reducing the energy consumption. The effectiveness of the design method is verified by applying it to two test cases: a five-bar mechanism and a SCARA robot. The numerical validations show that the system energy consumption can be reduced up to the 77.8% while performing a high-speed, standard, not-optimized trajectory.

Extensive experimental investigation for the optimization of the energy consumption of a high payload industrial robot with open research dataset

Abstract The optimization of the energy consumption of Industrial Robots (IRs) has been widely investigated. Unfortunately, on the field, the prediction and optimization strategies of IRs energy consumption still lack robustness and accuracy, due to the elevated number of parameters involved and their sensitivity to environmental working conditions. The purpose of this paper is to present, and share with the research community, an extensive experimental campaign that can be useful to validate virtual prototypes computing the energy consumption of robotic cells. The test cell, comprising a high payload IR equipped with multiple sensors and different payloads, is firstly described. The testing procedures are then presented. Experimental results are analyzed providing novel qualitative and quantitative evaluations on the contribution and relevance of different power losses and system operating conditions, clearly depicting the nonlinear relation between the energy consumption and various freely programmable parameters, thus paving the way to optimization strategies. Finally, all the experimental tests data are provided in the form of an open research dataset, along with custom Matlab scripts for plotting graphs and maps presented in this paper. These tests, which are verifiable via the shared dataset, consider the overall measured IR energy consumption (as drawn from the electric network) and highlight that, in some industrially interesting case scenarios, optimization potentials for energy savings of more than 50% are possible.

Optimization of the energy consumption of industrial robots for automatic code generation

At present, energy consumption strongly affects the financial payback period of industrial robots, as well as the related manufacturing process sustainability. Henceforth, during both design and manufacturing management stages, it becomes crucial to assess and optimize the overall energy efficiency of a robotic cell by means of digital manufacturing tools. In practice, robotic plant designers and managers should be able to provide accurate decisions also aimed at the energy optimization of the robotic processes. The strong scientific and industrial relevance of the topic has led to the development of many solutions but, unfortunately, state of the art industrial manipulators are equipped with closed controllers, which heavily limit the feasibility and performance of most of the proposed approaches. In light of the aforementioned considerations, the present paper presents a novel simulation tool, seamlessly interfaced with current robot offline programming tools used in industrial practices, which allows to automatically compute energy-optimal motion parameters, thus reducing the robot energy consumption, while also keeping the same productivity and manufacturing quality. The main advantage of this method, as compared to other optimization routines that are not conceived for direct integration with commercial industrial manipulators, is that the computed parameters are the same ones settable in the robot control codes, so that the results can automatically generate ready-to-use energy-optimal robot code. Experimental tests, performed on a KUKA Quantec KR210 R2700 prime industrial robot, have confirmed the effectiveness of the method and engineering tool.

Relevant factors for the energy consumption of industrial robots

This work investigates the energy consumption of industrial robots in the context of automotive industry. The purpose is to identify the most influencing parameters and variables and to propose best practices with focus on energy efficiency. The analysis approach is composed of three experiments performed in a simulation environment that test different values of programming parameters and variables, such as joint speed, acceleration, robot payload. The first experiment focuses on energy consumption of robots at standstill. The second one considers the robot moving along different paths. Finally, the third one analyses how the joint friction is affected by load, speed and temperature and how it influences the energy consumption. Results show that at standstill, it is important to reduce dwell time, select an energy efficient position and reduce the programmed value of the timer responsible for turning off the servomotors. While moving, it is important to select maximum continuous termination for intermediate points and avoid low speeds. Regarding friction variation, results show that at high motor speed, low temperatures increase energy consumption. In order to evaluate the contribution of the best practices in a real environment, they are applied to a welding robotic cell of an automotive industry.

An analysis of the effect of gravitational load on the energy consumption of industrial robots

The gravitational load has a great impact on industrial robots’ torque. Much research has gone into investigating this parameter in order to find an effective solution for achieving high performance of the robot systems. However, the existing investigations are still limited to analyze the influence of the gravitational load on the robot torque behavior. An analysis of the direct influence of gravitational load on electrical energy consumption has not yet been explored. This paper provides a model based approach for analyzing the effect of the gravitational load to the energy consumption. A mechatronic simulation tool is used for analyzing robot energy consumption. The results show that the gravitational load has an influence on the energy consumption of high-mass industrial robots, especially during upward movement.

Reducing the energy consumption of industrial robots in manufacturing systems

Reducing the energy consumption of industrial robots (IR) that are used in manufacturing systems has become a main focus in the development of green production systems. This is due to the reality that almost all automated manufacturing processes are using IR as the main component. Thus, reducing the energy consumption of IR will automatically reduce operating costs and CO2 emissions. Therefore, a method for reducing the energy consumption of IR in manufacturing systems is desired. Firstly, this paper presents a literature survey of the research in energy consumption analysis of IR that is used in manufacturing processes. The survey found that current research in this field is focused on the development of simulation models of IR that are able to be used to predict its energy consumption. Secondly, a modular model to analyze power consumption and dynamic behavior of IR is developed. Afterward, an experimental investigation is carried out to validate and estimate the accuracy of the model developed. The investigation shows that the developed modular model can be conveniently used to optimize the industrial robot’s operating parameters, which are commonly needed for production planning and at the process optimization stage. In addition, the investigation shows that the process constraints, environment layout, productivity requirement, as well as the robot payload and operating speed are the key factors that must be considered for optimizing the productivity and efficiency of IR.

Energy Planning of Manufacturing Systems with Methods-Energy Measurement (MEM) and Multi-Domain Simulation Approach

Energy costs play a decisive role in the operation costs of automotive production companies. Therefore, energy planning in an early conception and planning stage becomes an important topic. This is because the early conception and planning stage has the greatest potential to influence the energy consumption of manufacturing technologies since about 70-80 % of the energy costs are committed during this stage. However, lifetime cost and specifically energy consumption are currently not a determining factor at this stage. The reason is that the prediction of energy costs for complex manufacturing systems are challenging. Previous research approaches in the area of energy planning are limited to detailed planned production. A standardized approach to determine the energy consumption rates at an early stage does not exist. In this context, theEffiPLASproject has therefore proposed to solve this challenge. The aim of this project is to develop a Methods-Energy Measurement approach with elementary energy elements to support the planning process at an early stage, and to develop a modular simulation model for calculating the energy consumption of industrial robots, which complements the energy prediction. In this paper, the basic concept of elementary energy units and their value determination techniques is presented, and the simulation model is outlined. The developed approach will help to predict the prospective energy consumption of complex production equipment so that energy costs can be accounted for in an improved manner within a life-cycle costing comparative analyses.