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Research on motion trajectory planning of the robotic arm of a robot

ARTIFICIAL LIFE AND ROBOTICS 2022年第3期27卷 561-567页

作者： Miao, Xinghua Fu, Huansen Song, Xiangqian Taizhou Univ Sch Shipping & Mechatron Engn 93 Jichuan East Rd Taizhou 225300 Jiangsu Peoples R China

The emergence of robots has replaced repetitive manual labor, and good robotic arm route planning can effectively improve work efficiency. This paper briefly introduced the motion model and trajectory planning method of robotic arms. The motion trajectory of robot arms was optimized by the genetic algorithm-improved particle swarm optimization (PSO) algorithm, and simulation experiments were carried out. The results showed that the improved PSO algorithm converged faster and had the lowest fitness after stable convergence;the arm had continuous and smooth changes in angle, angular velocity and angular acceleration and consumed the shortest time while moving on the route planned by the improved particle swarm algorithm, and the improved PSO algorithm took the shortest time to compute the route.

关键词： Cross variation particle swarm optimization algorithm Robotic arm Trajectory planning

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Maintenance Scheduling of Semiconductor Production Equipment Based on particle swarm optimization 11

Maintenance Scheduling of Semiconductor Production Equipment...

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11th IEEE International Conference on Software Engineering and Service Science (IEEE ICSESS)

作者： Zhao, Chengwei Zhang, Hesheng Fan, Chenghao Gao, Zhiyuan Zhu, Xiaojing Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

ISBN: (纸本)9781728165790

In this paper, we study the problem of wafer production equipment maintenance scheduling. In order to shorten the maintenance time and ensure the continuity of the production, we proposed an improved particle swarm optimization (PSO) algorithm. The proposed hybrid algorithm is based on standard PSO, and integrated PSO with Simulated Annealing (SA) with probabilistic jump ability to avoid PSO trapping in local optimum. Besides, adaptive adjustment method of inertia weight is used, and add disturbance to the velocity update formula of the algorithm to ensure the fast optimization of particles in the early stage and avoid premature convergence. Experiment results indicate that the proposed algorithm is significant in terms of the maintenance time and downtime loss compared to conventional approaches.

关键词： Equipment maintenance particle swarm optimization algorithm scheduling

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Improved PSO-BPNN Multi-Parameter Identification Method and Its Application in Battery Thermal Network Model 2

Improved PSO-BPNN Multi-Parameter Identification Method and ...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Jiang, Tingzhi Ma, Mingyao Hu, Qiong Song, Qiwei Hefei University of Technology National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei China Sungrow Power Supply Co. Ltd Power Generation Sys Hefei China

ISBN: (纸本)9798350313765

Traditional battery thermal models either lack accuracy or have high computational costs, making it difficult for battery management systems to monitor battery temperature online. In this study, a three-dimensional thermal network model was developed specifically for a 120Ah square lithium-ion battery, significantly reducing the computational burden while ensuring relatively accurate outputs at multiple temperature points. Firstly, the heat generation rate of the entire battery during charging and discharging processes was obtained using an accelerated adiabatic calorimeter (ARC). Then, based on the positions of the electrode tabs and temperature measurement points, the battery was divided, and a three-dimensional thermal network model architecture was constructed. Special particle swarm algorithms and neural networks were used to fit the thermal parameters of the thermal network model. Finally, discharge temperature experiments were conducted to validate the feasibility of the thermal network model. © 2023 IEEE.

关键词： BP neural network particle swarm optimization algorithm Square lithium-ion battery Three-dimensional battery thermal network model

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Implementation of Demand Side Management Using PSO algorithm

Implementation of Demand Side Management Using PSO Algorithm

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Vardhan, Reddi Vivek Vaishnavi, Jonnalagadda Shyamala, Pachila Krishna Sai Siri, Gadi Sree Anand, R. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798350308167

In the present-day scenario, the management of grids plays a vital role. Demand-side management here acts as an important tool in developing trends. to meet the total power demand during peak loads, like reducing the total power demand in those situations. This paper focuses on the optimization of load consumption curves in smart grids using load-shifting techniques. And also, this paper discusses the optimization of load consumption through a PSO algorithmic approach, which in turn focuses on day-ahead load schedules concerning DSM, which is responsible for a low cost. © 2023 IEEE.

关键词： day ahead load scheduling Demand Side Management particle swarm optimization algorithm

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Bus-Priority Intersection Signal Control System Based on Wireless Sensor Network and Improved particle swarm optimization algorithm

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SENSOR LETTERS 2012年第8期10卷 1823-1829页

作者： Ma, Chang Xi Li, Yin Zhen He, Rui Chun Qi, Bo Wu, Fang Sun, Li Diao, Ai Xia Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China

In order to make buses have the priority to bus and ease the intersection congestion, this paper designs bus-priority intersection signal control system based on wireless sensor network and improved layered parallel particle swarm optimization (ILPPSO) algorithm. Dynamic network mode of wireless sensor network is used to collect the information of bus flow by adding nodes to it. The channelization method of intersections is improved by setting up bus lane. We build signal timing optimization model based on the traffic data collected by wireless sensor network and use improved particle swarm optimization algorithm to solve it. Finally, based on VISSIM soft, the signal control system is tested by programming. The results show that the signal control system can ease bus flow of intersections quickly and reduce vehicle's delay efficiently.

关键词： Bus-Priority Intersection Signal Control System Wireless Sensor Network particle swarm optimization algorithm

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Nonlinear System Identification Using Adaptive Volterra Model Optimized with Sine Cosine algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第11期47卷 14411-14422页

作者： Singh, Sandeep Rawat, Tarun Kumar Ashok, Alaknanda Uttarakhand Tech Univ Dehra Dun Uttarakhand India Maharaja Surajmal Inst Technol New Delhi India Netaji Subhas Univ Technol New Delhi India GB Pant Univ Agr & Technol Pantnagar Uttarakhand India

This paper presents the handling of nonlinear system identification problem based on Volterra model. Gradient-based algorithms are generally applied to solve system identification problems. However, these algorithms have the limitation of getting trapped in the local minimum. In the presented work, a novel population-based optimization algorithm popularly known as sine cosine algorithm (SCA) is being utilized for the identification of nonlinear discrete-time system. The SCA uses mathematical sine and cosine functions for the purpose of optimization. SCA is responsible for the creation of multiple random solutions and moving them towards best solution while maintaining proper balance between the exploitation and exploration phases of optimization. The performance evaluation of the applied SCA is carried out in terms of coefficient evaluation, mean square error and convergence profile. Two different examples for nonlinear system are presented in this work so as to demonstrate the validity of the employed algorithm. Performance analysis of the proposed approach with the existing state-of-the-art algorithms proves that the SCA outperforms the other algorithms.

关键词： Nonlinear system Volterra model Sine cosine algorithm Gravitational search algorithm particle swarm optimization algorithm Cuckoo search algorithm

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Application of Support Vector Machine Based on particle swarm optimization in Short-Term Load Forecasting of Honghe Power Network

Application of Support Vector Machine Based on Particle Swar...

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International Conference on Cyber Security Intelligence and Analytics (CSIA)

作者： Hua, Jing Xiong, Wei Niu, Lin Cao, Linlin Honghe Univ Engn Coll Mengzi 610000 Yunnan Peoples R China

ISBN: (纸本)9783030152352;9783030152345

In order to predict short-term load accurately and effectively, a short-term load forecasting model (PSO-SVM) based on particle swarm optimization (PSO) and support vector machine (SVM) is proposed. The parameters of the support vector machine are regarded as the velocity and position of a particle, and the optimal support vector machine parameters are found through continuous updating of the speed and position of the example. It can overcome support vector machine algorithm's shortcoming. The model of short-term load forecasting of the Red River power grid is established according to the optimal parameters, and the model performance is simulated. Try. The simulation results show that, compared with the SVM prediction model, PSO-SVM not only speeds up the optimization speed of SVM, but also improves the precision of load forecasting, and is more suitable for the need of short-term load forecasting in regional power grid.

关键词： Short term load forecasting Support vector machine particle swarm optimization algorithm

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Principle and Application Research of particle swarm optimization 5

Principle and Application Research of Particle Swarm Optimiz...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Zhang, Zhiqiang Wang, Le Hu, Jiongsong Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou Peoples R China Univ South China Sch Comp Sci Hengyang Peoples R China

ISBN: (纸本)9781665423144

With the increasing size and complexity of modern industrial production, the requirements for control systems are becoming higher and higher. The particle swarm optimization algorithm is a population-based stochastic optimization method. This paper first introduces the origin, principle and implementation steps of PSO algorithm, and then introduces the application of particle swarm algorithm in control system. In this paper, the fuzzy control strategy based on particle swarm optimization is proposed to improve the performance of fuzzy control. The design idea and implementation of the control strategy are described in detail. Finally, the system simulation is carried out with Matlab, and the simulation results are compared and analyzed. The results show that the control strategy can effectively improve the dynamic quality and steady-state accuracy of the control system, and has a good practical application prospect.

关键词： particle swarm optimization algorithm principle chaotic control system system simulation

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Digital Twin System for Home Service Robot Based on Motion Simulation

Digital Twin System for Home Service Robot Based on Motion S...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Jiang, Zhengsong Tian, Guohui Cui, Yongcheng Liu, Tiantian Gu, Yu Wang, Yifei School of Control Science and Engineering Shandong University Jinan China School of Information University of California Berkeley United States

ISBN: (纸本)9781665430654

In order to improve the task execution capability of home service robot, and to cope with the problem that purely physical robot platforms cannot sense the environment and make decisions online, a method for building digital twin system for home service robot based on motion simulation is proposed. A reliable mapping of the home service robot and its working environment from physical space to digital space is achieved in three dimensions: geometric, physical and functional. In this system, a digital space-oriented URDF file parser is designed and implemented for the automatic construction of the robot geometric model. Next, the physical model is constructed from the kinematic equations of the robot and an improved particle swarm optimization algorithm is proposed for the inverse kinematic solution. In addition, to adapt to the home environment, functional attributes are used to describe household objects, thus improving the semantic description of the digital space for the real home environment. Finally, through geometric model consistency verification, physical model validity verification and virtual-reality consistency verification, it shows that the digital twin system designed in this paper can construct the robot geometric model accurately and completely, complete the operation of household objects successfully, and the digital twin system is effective and practical. © 2023 IEEE.

关键词： Digital Twin Motion Simulation particle swarm optimization algorithm Service Robot

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UAV Localization Model Based on Multi-Dimensional Eigenvalue-Kalman Filter 6

UAV Localization Model Based on Multi-Dimensional Eigenvalue...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Tian, Mengxian Shandong University of Science and Technology Tai'an China

ISBN: (纸本)9798350305623

With the rapid development of science and technology, drone technology is gradually becoming an indispensable part of human life. With the increasing demand for complex tasks of UAVs from all walks of life, a single UAVs will not be able to effectively complete the task, and UAVs cluster gradually comes into people's perspective, and the resulting positioning of UAVs cluster has become the focus of current research. Azimuth-only passive positioning is a method that can adjust the position of UAVs and maintain formation. Several drones in the team send signals, and the rest of the drones passively receive signals to obtain direction information for positioning, and then adjust the position of the drones. This paper establishes Kalman filter algorithm model based on multidimensional eigenvalues, path planning model based on Bayesian estimation, and uses triangulation positioning method, particle swarm optimization algorithm and other methods to optimize azimuthing-only passive positioning of UAVs in formation flight. This method effectively improves the accuracy of UAV positioning and plays an important role in solving practical problems. © 2023 IEEE.

关键词： Bayesian estimation path planning model Kalman filter algorithm particle swarm optimization algorithm triangulation positioning method

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