Energy Consumption Of Robot Research Articles

Multirobot Task Planning Method Based on the Energy Penalty Strategy

In multirobot task planning, the goal is to meet the multi-objective requirements of the optimal and balanced energy consumption of robots. Thus, this paper introduces the energy penalty strategy into the GA (genetic algorithm) to achieve the optimization of the task planning of multiple robots in different operation scenarios. First, the algorithm model is established, after which the objective function is constructed by taking the energy excess of the relative average energy consumption of each robot as the penalty energy, along with the total energy consumption of multiple robots. In the genetic operation, two-segment chromosome coding is used to realize the iterative optimization of the number and task sequences of robots through diversified cross and mutation operators. Then, in the task scenario with obstacles, the A* (A-Star) algorithm and GA are used to plan the optimal obstacle avoidance path and to realize the secondary optimization of the robot task sequence without changing the number of tasks. During optimization, the energy penalty strategy imposes punishment on the objective function through the size of the penalty energy, enabling the robot energy consumption to reach an equilibrium state by maintaining the total energy consumption at the minimum. Finally, the MATLAB platform is used to conduct the simulation experiments to compare with basic genetic algorithms and penalty function algorithms, after which the optimal allocation scheme and energy consumption iteration of the algorithm are analyzed under different robot numbers, task numbers, and task scenarios, and the simulation results include robot task sequences, total energy consumption, average energy consumption, and standard deviation of energy consumption.

Climbing Strategy of Variable Topology Cellular Space Robots Considering Configuration Optimization

In optimizing the energy consumption of a cellular space robot (CSR) climbing in orbit, traditional trajectory planning methods do not include the configuration as a decision variable; thus, it is difficult to obtain an effective energy-optimal climbing strategy for CSRs. To solve this kind of problem, first, we considered the effect of configuration on the kinetic parameters of the robot and established the kinetic equations of a variable-size cellular space robot by combining the spin volume and Lagrange equations. Second, we constructed a higher-order continuous joint trajectory using a cubic spline curve and combined it with a robot dynamics model to establish a robot climbing energy consumption model. Finally, for the energy consumption problem of climbing in cellular space robots, we proposed a comprehensive optimization algorithm considering the configuration and climbing time. Compared with another algorithm in the literature, the algorithm proposed in this paper has stronger global optimization capability and convergence. The simulation results have important theoretical significance and engineering practical value for the study of space robot on-orbit climbing trajectory optimization strategies.

Scheduling a dual gripper material handling robot with energy considerations

Robotic cells are serial production systems that consist of a number of machines and a material handling robot that transfers parts between the machines. Energy consumption of a robot during its move between two machines can be decreased by carefully selecting its speed. This paper presents models and algorithms that make robot speed and robot activity sequencing decisions together for a dual-gripper robot that can handle two parts simultaneously. We consider both robot energy consumption and cycle time objectives so that a decision maker can observe the trade off between energy cost and throughput rate of the system, and select the best solution among alternatives. Computational experiments demonstrate that proposed approaches can generate efficient solutions for practical size instances, and speed control can significantly conserve energy.

Comparative Assessment of Environmental/Energy Performance under Conventional Labor and Collaborative Robot Scenarios in Greek Viticulture

The viticultural sector is facing a significant maturation phase, dealing with environmental challenges to reduce agrochemical application and energy consumption, while labor shortages are increasing throughout Europe and beyond. Autonomous collaborative robots are an emerging technology and an alternative to the scarcity of human labor in agriculture. Additionally, collaborative robots could provide sustainable solutions to the growing energy demand of the sector due to their skillful precision and continuous labor. This study presents an impact assessment regarding energy consumption and greenhouse gas emissions of collaborative robots in four Greek vineyards implementing a life cycle assessment approach. Eight scenarios were developed in order to assess the annual production of four Vitis vinifera L. cultivars, namely, Asyrtiko, Cabernet Sauvignon, Merlot, and Tempranillo, integrating data from two wineries for 3 consecutive years. For each conventional cultivation scenario, an alternative was developed, substituting conventional viticultural practices with collaborative robots. The results showed that collaborative robots’ scenarios could achieve a positive environmental and energy impact compared with conventional strategies. The major reason for lower impacts is fossil fuel consumption and the efficiency of the selected robots, though there are limitations regarding their functionality, lifetime, and production. The alternative scenarios have varying energy demand and environmental impact, potentially impacting agrochemical usage and requiring new policy adjustments, leading to increased complexity and potential controversy in farm management. In this context, this study shows the benefits of collaborative robots intended to replace conventional practices in a number of viticultural operations in order to cope with climate change impacts and excessive energy consumption.

An Efficient Computation for Energy Optimization of Robot Trajectory

Due to the wide distribution and high energy-saving potential of industrial robots, energy optimization techniques of industrial robots attract increasing attention. Dynamic time-scaling methods can optimize the energy consumption of robots only by stretching or shrinking reference trajectories in the time dimension. Dynamic time-scaling methods show advantages over other energy-saving techniques because time-scaling methods can achieve energy savings without hardware investment. Traditional dynamic time-scaling methods need to search the possible state transitions from the initial configuration to the final configuration to obtain the optimal solution. The integration of robot transient power has to be carried out for each possible state transition, which directly leads to intensive computation. Therefore, in this article, an efficient computation method for robot trajectory optimization is proposed. Based on parameter separation, an energy characteristic parameter model based on the dynamic time-scaling is developed, which describes the energy consumption during the state transition as a function of the scaling parameters. Based on the energy characteristic parameter model, the dynamic programming algorithm can replace the integral operation of transient power with the substitution calculations, which avoids intensive computation. Experimental results show that the proposed method can achieve 33.7% less energy consumption, and the computation time can be reduced by nearly 99%.

Motion Planning of an Inchworm Robot Based on Improved Adaptive PSO

Focusing on the motion energy consumption of a self-developed inchworm robot’s peristaltic gait, based on the “error tracking” of cubic polynomial programming in Cartesian space and seventh polynomial programming in joint space, we propose an optimal motion planning method of energy consumption considering both kinematic and dynamic constraints. Firstly, we offer a mathematical description of the energy consumption and space curve similarity operator. Secondly, we describe the mathematical models of the robot trajectory and path that were established in terms of their dynamics and kinematics. Then, we propose a motion planning method based on improved adaptive particle swarm optimization (PSO) to accelerate the convergence speed of the algorithm and ensure the accuracy of the model calculation. Finally, we outline the simulation test carried out to measure the inchworm-like robot’s creeping gait. The results show that the motion path obtained by using the planning method proposed in this paper is the one with the least energy consumption by the robot among all the comparison paths. Moreover, compared with other algorithms, it was found that the result obtained by using the algorithm proposed in this paper is the one with the shortest solution time and the lowest energy consumption under the same iteration times. The calculation results verify the feasibility and effectiveness of the planning method.

Energetically Optimal Trajectory for a Redundant Planar Robot by Means of a Nested Loop Algorithm

This work presents an optimal point-to-point trajectory planning method based on fourth degree polynomials to reduce the energy consumption of a redundant planar robot in the XY plane. The contribution of this study focuses on the development of a two-level nested optimization algorithm, which manages to optimize three elements: the Optimal Weighting Vector that establishes the influence of each joint on the total energy consumption of the robotic equipment; the optimal start and finish configurations of the trajectory; and the vector corresponding to the fifth coefficient of the trajectory generator polynomial. The tests are carried out with different optimization techniques and objective functions related to energy consumption to determine their best combination and achieve the minimum possible computation time and energy savings, according to the test conditions used. Furthermore, the energetic performance of the redundant manipulator is compared with its non-redundant version, limiting its operation to only two degrees of freedom in all its variants.

Automatic Control of Mobile Industrial Robot Based on Multiobjective Optimization Strategy

In order to solve the optimal cascade mobile path selection problem when mobile industrial robots repair network coverage holes, a cascade mobile path selection optimization method considering the number and energy of intermediate cascade nodes is proposed. By calculating the energy availability of intermediate cascade nodes, this method further obtains the energy availability and decisive energy of each path, selects the optimal cascade mobile path from the perspective of multiobjective optimization, effectively balances the energy consumption of each mobile industrial robot, makes full use of the energy of the whole network, and prolongs the survival time of the network. Simulation results show that the optimization method has higher network energy efficiency than the standard cascaded mobile method.

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.

Predicting Energy Consumption of Ground Robots on Uneven Terrains

Optimizing energy consumption for robot navigation in fields requires energy-cost maps. However, obtaining such a map is still challenging, especially for large, uneven terrains. Physics-law-based energy models work for uniform, flat surfaces but do not generalize well to these terrains. Furthermore, slopes make the energy consumption at every location directional and add to the complexity of data collection and energy prediction. In this paper, we address these challenges in a data-driven manner. We consider a function which takes terrain geometry and robot motion direction as input and outputs expected energy consumption. The function is represented as a ResNet-based neural network whose parameters are learned from field-collected data. The prediction accuracy of our method is within 12% of the ground truth in our test environments that are unseen during training. We compare our method to a baseline method in the literature: a method using a basic physics-based model. We demonstrate that our method significantly outperforms it by more than 10% measured by the prediction error. More importantly, our method generalizes better when applied to test data from new environments with various slope angles and navigation directions.

Modeling and analysis on low energy consumption foot trajectory for hydraulic actuated quadruped robot

According to the energy consumption characteristics of hydraulic actuator, the valuable foot trajectory characterized by using segmented cubic spline interpolation curve in the swing phase is proposed firstly to reduce the energy consumption of quadruped robots, which is implemented by using controlling parameters tf to change the duration of leg raising and falling in one gait cycle, and then realized the directly control to the time ratio between the piston extension and retraction. Then, the total energy consumption of the hydraulic actuated quadruped robot SCalf-II is modeled. Meanwhile, the parameters of the foot trajectory that have a large impact on the energy consumption are determined. Finally, simulation analysis and verification experiments of the robot moving with constant speeds at the key parameters are performed. The results show that for the given foot trajectory, the optimization ranges of the gait cycle and duration of leg lifting from the lowest to highest are determined in which the energy required for the robot locomotion is at a relatively low level.

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.

Data-Driven Energy Estimation of Individual Instructions in User-Defined Robot Programs for Collaborative Robots

Traditionally, the objective of industrial production focuses on fast and low-cost production, regardless of resources and energy consumption (EC). However, in alignment with the UN Sustainable Development Goals (SDG), governments worldwide have proposed regulations to reduce resources and energy. In their production lines, an increasing number of companies are using collaborative robots (cobots). Cobots are programmed to accomplish their task as fast as possible, ignoring the robot's EC. This letter estimates the cobot EC from individual instructions of user-defined robot programs. Thus, the user has an additional design parameter to create energy-optimal programs. In the literature, current EC estimation models for manipulators are not reliable or have not been assessed to test the model's reliability. Our modeling methodology possesses three steps: motion planning, dynamic model, and EC model. Using cobots of different sizes (UR3e and UR10e) and loading, we collected over 55000 samples per case and trained the model to identify the model's unknown parameters. The model estimated the power consumption of a testing dataset with a maximum RMS error of 6 [W] - 3.85%. In the final experiment, the complete system was tested using a user-defined program composed of six instructions. The results showed an accurate estimation of the power profile with an RMS error of 2.39 [W] and 4.23 [W] for UR3e and UR10e.

Design and Preliminary Testing of a Magnetic Spring as an Energy-Storing System for Reduced Power Consumption of a Humanoid Arm

The increasing use of robots in the industry, the growing energy prices, and higher environmental awareness have driven research to find new solutions for reducing energy consumption. In additional, in most robotic tasks, energy is used to overcome the forces of gravity, but in a few industrial applications, the force of gravity is used as a source of energy. For this reason, the use of magnetic springs with actuators may reduce the energy consumption of robots performing trajectories due their high-hardness magnetic properties of energy storage. Accordingly, this paper proposes a magnetic spring configuration as an energy-storing system for a two DoF humanoid arm. Thus, an integration of the magnetic spring system in the robot is described. A control strategy is proposed to enable autonomous use. In this paper, the proposed device is modeled and analyzed with simulations as: mechanical energy consumption and kinetic energy rotational and multibody dynamics. Furthermore, a prototype was manufactured and validated experimentally. A preliminary test to check the interaction between the magnetic spring system with the mechanism and the trajectory performance was carried out. Finally, an energy consumption comparison with and without the magnetic spring is also presented.

C-Legged Hexapod Robot Design Guidelines Based on Energy Analysis

C-legged hexapod robots offer a balanced trade-off between the robust stability of wheeled robots and the increased-motion capabilities of legged robots, and therefore, are currently of great interest. This article investigates the impact of mass, leg radius, and angular velocity on the energy consumption of C-legged hexapod robots, in order to develop a set of design guidelines that maximize the robot’s performance. The kinematic model of a single C-leg system is obtained and used to determine the system’s energy consumption associated with gravitational potential energy (GPE) and kinetic energy (KE) variations. Both the kinematic model and energy model are validated in a custom-made test bench. Our results show that the kinematic model very accurately predicts the trajectory of the system in space, but due to the varying load experienced by the motor, the system lags compared to the model predictions. Furthermore, the energy model has been also validated experimentally and successfully predicts the motor consumption periods. Using the energy model, it has been concluded that the angular velocity of the leg and the leg radius have an exponential relationship with motor peak power demand—directly affecting the motor selection. On the other hand, the mass is inversely proportional to the robot efficiency, and therefore, must be kept as low as possible.

Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot.

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

Multi-objective optimization of greening scheduling problems of part feeding for mixed model assembly lines based on the robotic mobile fulfillment system

Since greening scheduling problems are drawing increasing attention from researchers and modern manufacturing enterprises, and the energy consumption is a substantial problem regarding the greening and sustainability, the aim of this paper is to construct an energy-saving scheduling scheme to carry out the part feeding tasks of mobile robots in the automobile mixed model assembly lines. The objective of minimizing the total energy consumption of mobile robots is jointly incorporated with the operational criterions when implementing part feeding tasks. Due to the NP-hardness nature of the proposed greening problem, a multi-objective disturbance and repair strategy enhanced cohort intelligence (MDRCI) algorithm is established to deal with the multi-objective problem. Computational results indicate that the enhanced strategies are of great significance to the MDRCI algorithm and it outperforms the other benchmark algorithms on both global search capability and search depth. In addition, the energy-saving strategy and disturbance and repair strategy are validated by comparison experiments. Furthermore, managerial insights are illustrated to make trade-offs between the total line-side inventory level and the energy consumption, jointly making it helpful in the greening scheduling process of the practical production. The achievements acquired in this paper may be inspiring for further researches on the energy-related production scheduling problem.

Development of a braking energy recovery device for an agricultural robot

Designing and creating new agricultural machines, their introduction into agricultural production and effective use requires taking into account and objectively evaluating many factors determined by the requirements of industry and the specifics of agricultural production. The scientific and technical process in the field of automation and mechanization of agricultural production is aimed at reducing specific energy costs, increasing the productivity and manageability of equipment, as well as compliance with environmental standards. To solve these tasks, mobile robots are actively used that can independently perform various operations related to sowing, tillage, plant care and harvesting. A promising method for improving the efficiency of agricultural machinery is the use of braking energy recovery, its accumulation and use during further acceleration. For this purpose, a device was developed and designed to reduce the energy consumption of agricultural robots without reducing the dynamic qualities and performance of the equipment.

A Hybrid Method for Industrial Robot Navigation

Robot navigation in dynamic unknown environments is a challenging issue in the field of autonomous mobile robot control. This paper presents a hybrid robust method for navigating an industrial robot in an environment that contains dynamic obstacles. The objectives are to find the shortest path, to minimize the energy consumption of robot, to make the smoothness of the generated paths and to tackle dynamic obstacles. Robots employed in industrial environments demand considerable autonomy and require high level of accuracy and manoeuvrability at the same time. Besides, no collision is tolerable along the way. A single-objective optimization method based on path criteria fails to satisfy all of the requirements. This paper proposes a hybrid algorithm including the whale optimization algorithm (WOA) for path planning, a learnable function approximation network for making smoothness of the generated paths and a fuzzy logic controller to avoid obstacle collision. In this algorithm, WOA optimizes the best path to be taken from the start to goal position. Once a sequence of points is candidate and segments of path are merged, a radial basis function is trained to provide a smooth movement path in the dynamic environment while trying to maximize the safety margin. To further improve the safety of navigation, a fuzzy-based obstacle avoidance algorithm is executed when the robot is placed in the vicinity of an obstacle. Fuzzy decisions are made based on values of distance information. The proposed hybrid method for path planning and obstacle avoidance issues was implemented and evaluated in dynamic environments including specific shaped obstacles. A GUI-based simulation platform was designed in Matlab environment for testing the proposed algorithm. Implementation results indicate that the proposed algorithm has yielded in smooth non-marginal goal-directed navigation with acceptable performance metrics. Meanwhile, collisions to dynamic obstacles were adaptively and non-rigidly avoided. Such a model-free hybrid algorithm for path planning and obstacle avoidance can improve autonomy in industrial operation and decrease computational complexity.

Hexapod Robot Gait Switching for Energy Consumption and Cost of Transport Management Using Heuristic Algorithms

Due to the prospect of using walking robots in an impassable environment for tracked or wheeled vehicles, walking locomotion is one of the most remarkable accomplishments in robotic history. Walking robots, however, are still being deeply researched and created. Locomotion over irregular terrain and energy consumption are among the major problems. Walking robots require many actuators to cross different terrains, leading to substantial consumption of energy. A robot must be carefully designed to solve this problem, and movement parameters must be correctly chosen. We present a minimization of the hexapod robot’s energy consumption in this paper. Secondly, we investigate the reliance on power consumption in robot movement speed and gaits along with the Cost of Transport (CoT). To perform optimization of the hexapod robot energy consumption, we propose two algorithms. The heuristic algorithm performs gait switching based on the current speed of the robot to ensure minimum energy consumption. The Red Fox Optimization (RFO) algorithm performs a nature-inspired search of robot gait variable space to minimize CoT as a target function. The algorithms are tested to assess the efficiency of the hexapod robot walking through real-life experiments. We show that it is possible to save approximately 7.7–21% by choosing proper gaits at certain speeds. Finally, we demonstrate that our hexapod robot is one of the most energy-efficient hexapods by comparing the CoT values of various walking robots.