A Practical Energy Modeling Method for Industrial Robots in Manufacturing

Xu, Wenjun; Liu, Huan; Liu, Jiayi; Zhou, Zude; Pham, Duc Truong

doi:10.1007/978-3-319-61994-1_3

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…An energy modeling method by measuring the total power for an industry robot was proposed by Xu et al [8]. This method avoids the problem of directly measuring relevant parameters inside the robot.…”

Section: Related Workmentioning

confidence: 99%

“…The energy problem of mobile robots has been paid more attention in order to meet requirements of reducing energy consumption. The energy consumption modeling of mobile robots [7] based on mathematical formulas can be more scientific to study the influence of operation states on energy consumption, which provides a guide to facilitate energy-efficient strategies [8]. Firstly, the robot itself can clearly understand the energy required for the robot's motion and the specific energy consumption of each part; therefore, the energy consumption can be reduced according to different situations and the existing energy support can be estimated.…”

Section: Introductionmentioning

confidence: 99%

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Energy Modeling and Power Measurement for Mobile Robots

2018

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To improve the energy efficiency of a mobile robot, a novel energy modeling method for mobile robots is proposed in this paper. The robot can calculate and predict energy consumption through the energy model, which provides a guide to facilitate energy-efficient strategies. The energy consumption of the mobile robot is first modeled by considering three major factors: the sensor system, control system, and motion system. The relationship between the three systems is elaborated by formulas. Then, the model is utilized and experimentally tested in a four-wheeled Mecanum mobile robot. Furthermore, the power measurement methods are discussed. The energy consumption of the sensor system and control system was at the milliwatt level, and a Monsoon power monitor was used to accurately measure the electrical power of the systems. The experimental results showed that the proposed energy model can be used to predict the energy consumption of the robot movement processes in addition to being able to efficiently support the analysis of the energy consumption characteristics of mobile robots.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Modeling and Power Measurement for Mobile Robots

2018

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“…"Usually, energy consumption modeling for industrial robots is based on dynamic parameters identification. However, since the parts of industrial robots are not easy to be disassembled and the sensor modules cannot be installed easily inside robots, it is difficult to obtain all such physical parameters through sensing method, in particular the torque data" [14]. In this context, [14] proposes a practical method of energy modeling by measuring the total power consumed by the robots, thus avoiding the problem of directly measuring the relevant parameters within the robots.…”

Section: Introductionmentioning

confidence: 99%

“…However, since the parts of industrial robots are not easy to be disassembled and the sensor modules cannot be installed easily inside robots, it is difficult to obtain all such physical parameters through sensing method, in particular the torque data" [14]. In this context, [14] proposes a practical method of energy modeling by measuring the total power consumed by the robots, thus avoiding the problem of directly measuring the relevant parameters within the robots. The experimental results of [14] show that the proposed energy modeling method can be used to predict the energy consumption of the robot.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Caballero¹,

Perafan

Rinaldy

et al. 2021

Electronics

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This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

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“…It is clarified by experiments that the motion system part consumes the overwhelming majority of energy, with up to more than 90%. Xu et al [11] present an energy consumption modelling method for an industry robot. Their method does not have to measure the relevant parameters inside the robot and mainly concerns torque modelling.…”

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confidence: 99%

Energy-Efficient Torque Distribution Optimization for an Omnidirectional Mobile Robot with Powered Caster Wheels

et al. 2019

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A mobile robot with no less than two powered caster wheels (PCWs) has the ability to perform omnidirectional motions and belongs to a redundantly actuated system. Redundant actuation will bring the issue of non-uniqueness of actuating torque distribution, and inappropriate choices of torque distribution schemes will lead to unexpected large required actuating torques and extra energy consumption. This paper proposes a new torque distribution optimization approach based on a gradient projection method (GPM) for the omnidirectional mobile robot (OMR) with direct drive PCWs. It can significantly reduce the maximal required actuating torque and the energy consumption of the system. The modular kinematic and dynamic modeling method is presented first, which is suitable for an arbitrary number of employed PCWs, as well as their install positions in the chassis. The detailed energy consumption model of the OMR, including output energy consumption and electrical energy loss, is formulated through experimental testing. The effectiveness of the proposed algorithms is validated by simulation examples. Lastly, the computational efficiency of the method is verified. components: the sensor system, control system, and motion system. It is clarified by experiments that the motion system part consumes the overwhelming majority of energy, with up to more than 90%. Xu et al. [11] present an energy consumption modelling method for an industry robot. Their method does not have to measure the relevant parameters inside the robot and mainly concerns torque modelling. A parameter estimation method is proposed for the torque modelling. Verstraten et al. [12] study several modeling methods that are commonly adopted. They investigate how well these methods can be used to describe the energy consumption of a DC motor while performing dynamic tasks. The conclusion resulted from their work provides guidelines in determining which factors should be included in the energy consumption model. In this paper, we aim to study the impacts of different torque distribution schemes on the energy consumption of the OMR with PCWs. Hence, we focus on formulating the energy consumption model based on the actuation torques. Besides theoretical energy consumption modelling, the accurate experimental measurement of energy consumption is necessary for the evaluation of energy consumption of a system combined with hardware and software. Laopoulos et al.[13] present a current measurement method that monitors the instantaneous supply current. The method can provide high-performance evaluation of energy consumption, especially for low-power applications. Roennau et al. [14] propose an energy consumption estimation based on an on-board current measurement for each joint individually and employ a non-linear function to fit the measurement. There are two different motors integrated in a single PCW, which are responsible for rolling and steering motions, respectively. The energy consumption modelling methods for the two motors are unified; detailed specifications of t...

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A Practical Energy Modeling Method for Industrial Robots in Manufacturing

Cited by 8 publications

References 20 publications

Energy Modeling and Power Measurement for Mobile Robots

Energy Modeling and Power Measurement for Mobile Robots

Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Energy-Efficient Torque Distribution Optimization for an Omnidirectional Mobile Robot with Powered Caster Wheels

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