Optimization of energy consumption in industrial robots, a review

Soori, Mohsen; Arezoo, Behrooz; Dastres, Roza

doi:10.1016/j.cogr.2023.05.003

Cited by 35 publications

(14 citation statements)

References 102 publications

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“…The author's previous study [37] introduced an energy-saving trajectory planning method where the joint angular trajectory of a flexible manipulator is represented by a combination of a cycloid function and a power series. Numerical simulations demonstrated that this method conserves more energy compared to neural networks [32]. However, the physical phenomena discussed in Section 3 suggest that energy savings can also be achieved by significantly deflecting the manipulator in the negative direction immediately following activation.…”

Section: Trajectory Planning Methods Focused On Flexibility Character...mentioning

confidence: 99%

“…However, the aforementioned studies focused only on vibration control, and methods targeting energy conservation were limited to manipulator systems with rigid links. Soori et al [32] conducted a comprehensive review of the literature on the optimization of energy consumption in industrial robots, in which they reviewed 136 papers published between the years 2004 and 2023. Focusing on industrial robots, autonomous vehicles, and embedded systems, Vásárhelyi et al [33] provided a systematic review of the classification and analysis of various methodologies and solutions developed to improve the energy performance of robotic systems.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Saving Breakthrough in the Point-to-Point Control of a Flexible Manipulator

Abe

2024

Applied Sciences

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This study aims to contribute academically valuable insights into energy-efficient drives for the positioning control of flexible structures. It focuses on the point-to-point (PTP) motion control of a flexible manipulator to suppress residual vibration and reduce driving energy simultaneously. The driving energy for PTP motion is influenced by the initial deflection of the flexible manipulator. Considering this phenomenon, the study proposes a trajectory planning method for the joint angle of a flexible manipulator. In this method, the evaluation function is defined as the sum of drive torques, and its minimization through particle swarm optimization generates an optimal trajectory that minimizes drive energy and suppresses residual vibration. Numerical simulations indicate that significant energy savings can be achieved by actively deforming the manipulator. These simulation results are corroborated by experimental data, which demonstrate the practical applicability and effectiveness of the proposed method.

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Section: Trajectory Planning Methods Focused On Flexibility Character...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-Saving Breakthrough in the Point-to-Point Control of a Flexible Manipulator

Abe

2024

Applied Sciences

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“…This EC is directly proportional to the operating time as in the case of robots with welding equipment. (e) Environmental factors such as ambient temperature, moving constraints, trajectory points (linked motions consume more energy than non-linked ones [38]), trajectory precision (using final points which force the robot to stop at designed points is more energy intensive than using fly-by points which are used to bring the tool control point in a certain point vicinity, not reducing the robot speed to zero [12]). (f) Alternation between stand-by mode, which uses mechanic brakes and working state, where electric breaks are activated, greatly influences EC in industrial applications where equipment is enabled only during production time.…”

Section: Overview Of Factors Affecting Energy Consumption By Industri...mentioning

confidence: 99%

“…An analysis of the most efficient strategies that optimize EC of industrial robots is presented in [12]. Thus, optimizing path planning and trajectory generation involves efficient programming to limit unnecessary robot movements and changes in directions of joint displacements [13], reduce overall travel distances and tune parameters of the speed profile (cruise speed, acceleration, deceleration), select the best energy-saving robot configuration (e.g., righty/lefty) for the assigned motions [14], apply minimum jerk trajectory control [15], and generally use the continuous-path motion feature [16].…”

Section: Introductionmentioning

confidence: 99%

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

Răileanu,

Borangiu,

Lențoiu

et al. 2024

Sustainability

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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.

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“…Energy consumption in industrial applications is another significant concern in the literature. The research ranges from maximizing solar energy extraction in electric vehicles using BLDC motors [ 18 ] and reducing energy consumption in industrial robots by selecting appropriate motors, drives, controllers, and techniques to minimize idle time [ 19 ].Regarding the optimal selection of motors and reducers, a method is presented in [ 20 ] that optimizes servo-axis component selection without iterative processes. It creates an electromechanical model that considers electrical and mechanical factors, with a size index for precise part selection.…”

Section: Introductionmentioning

confidence: 99%

Selection/control concurrent optimization of BLDC motors for industrial robots

Padilla-García¹,

Cervantes-Culebro

Rodríguez-Ángeles

et al. 2023

PLoS ONE

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This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLDC motors are considered with trapezoidal back-emf, where the three-phase (a,b,c) dynamics of motors are modeled in a mechatronic powertrain model of the robot for the selection and control problem, defining it as a multi-objective dynamic optimization problem with static and dynamic constraints. Since the mechanical and electrical actuators’ parameters modify the robot’s performance, the selection process considers the actuators’ parameters, their control input, operational limits, and the mechanical output to the transmission of the robot joints. Then, three objective functions are to be minimized, the motor’s energy consumption, the tracking error, and the total weight of installed motors on the robot mechanism. The control parameterization approach via a cascade controller with PI controllers for actuators’ voltage and a PID controller for actuators’ torque is used to solve the multi-objective dynamic optimization problem. Based on simulations of the closed-loop system, a Pareto front is obtained to examine trade-offs among the objective functions before implementing any actuators in the existing robotic system. The proposed method is tested on an experimental platform to verify its effectiveness. The performance of an industrial robot with the actuators originally installed is compared with the results obtained by the synergic approach. The results of this comparison show that 10.85% of electrical power can be saved, and the trajectory tracking error improved up to 57.41% using the proposed methodology.

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Optimization of energy consumption in industrial robots, a review

Cited by 35 publications

References 102 publications

Energy-Saving Breakthrough in the Point-to-Point Control of a Flexible Manipulator

Energy-Saving Breakthrough in the Point-to-Point Control of a Flexible Manipulator

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

Selection/control concurrent optimization of BLDC motors for industrial robots

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