Cognitive Robotics最新文献_第7页

Optimization of energy consumption in industrial robots, a review 工业机器人能耗优化研究综述

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.05.003

Mohsen Soori , Behrooz Arezoo , Roza Dastres

{"title":"Optimization of energy consumption in industrial robots, a review","authors":"Mohsen Soori , Behrooz Arezoo , Roza Dastres","doi":"10.1016/j.cogr.2023.05.003","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.05.003","url":null,"abstract":"<div><p>Optimization of energy consumption in industrial robots can reduce operating costs, improve performance and increase the lifespan of the robot during part manufacturing. Choosing energy-efficient components such as motors, drives, and controllers can significantly reduce energy consumption in industrial robots. Over-sized motors and heavy robot arms can waste energy and decrease efficiency of industrial robots. By optimizing the robot programs and reducing idle time in robot operations, the amount of spent time can be reduced to minimize energy consumption of industrial robots. By using energy-efficient motors and drives, the amount of energy consumed by the robot can be reduced. Also, regular maintenance can reduce energy consumption of industrial robots by providing maximum efficiency for the robot's components. By implementing energy management systems, energy consumption of industrial robot can be monitored and analyzed to optimize energy consumption of industrial robot during working conditions. To minimize lost energy and reuse the energy usage during working times, regenerative braking can be used in the robots. The process of part manufacturing can be optimized in order to minimize the robot's movements and energy usage during working times of industrial robots. To analyze and optimize energy consumption in working schedules of industrial robots, different methodologies from recent published papers are reviewed in the study. Proper robot selection, energy-efficient robot motor and low wight robot arms, efficient programming of working schedules, regenerative braking system, regular maintenance of robot elements and optimized process of part production regarding the minimization of energy usage are discussed to optimize the energy consumption in industrial robots. As a result, future research works in the research field can be presented in order to optimize energy consumption, reduce operational costs, and increase sustainability of industrial robot operations in terms of productivity enhancement of part manufacturing.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 142-157"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49710401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Selection of PSO parameters based on Taguchi design-ANOVA- ANN methodology for missile gliding trajectory optimization 基于田口设计的导弹滑翔轨迹优化粒子群算法参数选择

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.05.002

Shubhashree Sahoo , Rabindra Kumar Dalei , Subhendu Kumar Rath , Uttam Kumar Sahu

{"title":"Selection of PSO parameters based on Taguchi design-ANOVA- ANN methodology for missile gliding trajectory optimization","authors":"Shubhashree Sahoo , Rabindra Kumar Dalei , Subhendu Kumar Rath , Uttam Kumar Sahu","doi":"10.1016/j.cogr.2023.05.002","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.05.002","url":null,"abstract":"<div><p>The proposed research deals with selection of particle swarm optimization (PSO) algorithm parameters for missile gliding trajectory optimization relying on Taguchi design of experiments, analysis of variance (ANOVA) and artificial neural networks (ANN). Population size, inertial weight and acceleration coefficients of PSO were chosen for the present study. The experiments have been designed as per Taguchi's design of experiments using L<sub>25</sub> orthogonal array for selection of better PSO parameters. Missile gliding trajectory is optimized by discretizing angle of attack as control parameter, consequent conversion of optimal control problem to nonlinear programming problem (NLP) and finally solving the problem using PSO with optimized parameters to obtain optimum angle of attack and realization of maximum gliding range. Simulation results portrayed that the gliding range is maximized and missile glide distance is enhanced compared to earlier experiments. The efficiency of proposed approach was verified via different test scenarios.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 158-172"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49710728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent health management based on analysis of big data collected by wearable smart watch 基于可穿戴智能手表大数据分析的智能健康管理

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2022.12.003

CHEN Xiao-Yong , YANG Bo-Xiong , ZHAO Shuai , DING Jie , SUN Peng , GAN Lin Lindy

引用次数: 0

Safe reinforcement learning for high-speed autonomous racing 高速自动驾驶赛车的安全强化学习

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.04.002

Benjamin D. Evans, Hendrik W. Jordaan, Herman A. Engelbrecht

{"title":"Safe reinforcement learning for high-speed autonomous racing","authors":"Benjamin D. Evans, Hendrik W. Jordaan, Herman A. Engelbrecht","doi":"10.1016/j.cogr.2023.04.002","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.04.002","url":null,"abstract":"<div><p>The conventional application of deep reinforcement learning (DRL) to autonomous racing requires the agent to crash during training, thus limiting training to simulation environments. Further, many DRL approaches still exhibit high crash rates after training, making them infeasible for real-world use. This paper addresses the problem of safely training DRL agents for autonomous racing. Firstly, we present a Viability Theory-based supervisor that ensures the vehicle does not crash and remains within the friction limit while maintaining recursive feasibility. Secondly, we use the supervisor to ensure the vehicle does not crash during the training of DRL agents for high-speed racing. The evaluation in the open-source F1Tenth simulator demonstrates that our safety system can ensure the safety of a worst-case scenario planner on four test maps up to speeds of 6 m/s. Training agents to race with the supervisor significantly improves sample efficiency, requiring only 10,000 steps. Our learning formulation leads to learning more conservative, safer policies with slower lap times and a higher success rate, resulting in our method being feasible for physical vehicle racing. Enabling DRL agents to learn to race without ever crashing is a step towards using DRL on physical vehicles.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 107-126"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49732926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Review on lane detection and related methods 车道检测及相关方法综述

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.05.004

Weiyu Hao

引用次数: 0

Reinforcement learning for swarm robotics: An overview of applications, algorithms and simulators 群体机器人的强化学习：应用、算法和模拟器综述

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.07.004

Marc-Andrė Blais, Moulay A. Akhloufi

{"title":"Reinforcement learning for swarm robotics: An overview of applications, algorithms and simulators","authors":"Marc-Andrė Blais, Moulay A. Akhloufi","doi":"10.1016/j.cogr.2023.07.004","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.07.004","url":null,"abstract":"<div><p>Robots such as drones, ground rovers, underwater vehicles and industrial robots have increased in popularity in recent years. Many sectors have benefited from this by increasing productivity while also decreasing costs and certain risks to humans. These robots can be controlled individually but are more efficient in a large group, also known as a swarm. However, an increase in the quantity and complexity of robots creates the need for an adequate control system. Reinforcement learning, an artificial intelligence paradigm, is an increasingly popular approach to control a swarm of unmanned vehicles. The quantity of reviews in the field of reinforcement learning-based swarm robotics is limited. We propose reviewing the various applications, algorithms and simulators on the subject to fill this gap. First, we present the current applications on swarm robotics with a focus on reinforcement learning control systems. Subsequently, we define important reinforcement learning terminologies, followed by a review of the current state-of-the-art in the field of swarm robotics utilizing reinforcement learning. Additionally, we review the various simulators used to train, validate and simulate swarms of unmanned vehicles. We finalize our review by discussing our findings and the possible directions for future research. Overall, our review demonstrates the potential and state-of-the-art reinforcement learning-based control systems for swarm robotics.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 226-256"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49710706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Development of a Pneumatic Conveyor Robot for Color Detection and Sorting 色彩检测分拣气动输送机器人的设计与开发

Cognitive Robotics Pub Date : 2022-03-01 DOI: 10.1016/j.cogr.2022.03.001

Mohammadreza Lalegani Dezaki, Saghi Hatami, A. Zolfagharian, M. Bodaghi

引用次数: 8

Panoptic segmentation network based on fusion coding and attention mechanism 基于融合编码和注意机制的全视分割网络

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.08.001

Jiarui Zhang, Penghui Tian

引用次数: 0

Spread-based elite opposite swarm optimizer for large scale optimization 面向大规模优化的基于spread的精英逆向群优化算法

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.03.005

Li Zhang, Yu Tan

引用次数: 0

Research on improved full-factor deep information mining algorithm 改进的全因子深度信息挖掘算法研究

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.01.001

Yun Man , Xu Fei , Liu Jun , Zhang Qian

{"title":"Research on improved full-factor deep information mining algorithm","authors":"Yun Man , Xu Fei , Liu Jun , Zhang Qian","doi":"10.1016/j.cogr.2022.01.001","DOIUrl":"https://doi.org/10.1016/j.cogr.2022.01.001","url":null,"abstract":"<div><p>In the use of fire-fighting physics platform for fire alarm data correlation analysis, there are often problems such as too much data volume and insufficient accuracy of the analysis results. For such questions, this paper establishes a full-factor secondary mining mechanism for fire accidents based on the fire big data based on the correlation analysis algorithm and the clustering algorithm. The association algorithm is used to conduct full-factor primary mining on the fire-related factors in the data warehouse, and the common-sense accident attributes in the association rules are extracted. Then use the K-means clustering algorithm, where the cluster center is the relevant attribute in the fire accident record, and perform the second combined clustering of the accident elements to achieve in-depth information mining of all factors of the fire accident. Experimental results show that the improved full-factor deep information mining algorithm proposed in this paper can effectively filter 31.6% of meaningless mining results compared to the traditional single mining algorithm. It shows that the algorithm in this paper can more accurately dig out the relationship between data, and can provide more effective decision support for fire management and other work.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"2 ","pages":"Pages 30-38"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241322000015/pdfft?md5=0dd4e3a0dc308e9e12201330a7437e1f&pid=1-s2.0-S2667241322000015-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136555883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0