Cognitive Robotics最新文献_第6页

Co-attention learning cross time and frequency domains for fault diagnosis 跨时频域的故障诊断共注意学习

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.03.001

Ping Luo , Xinsheng Zhang , Ran Meng

引用次数: 0

Adjustable Convergence Rate Prescribed Performance with Fractional-Order PID Controller for Servo Pneumatic Actuated Robot Positioning 基于分数阶PID控制器的伺服气动机器人定位可调收敛速率规定性能

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.04.004

Mohd Iskandar Putra Azahar, Addie Irawan, R.M.T. Raja Ismail

{"title":"Adjustable Convergence Rate Prescribed Performance with Fractional-Order PID Controller for Servo Pneumatic Actuated Robot Positioning","authors":"Mohd Iskandar Putra Azahar, Addie Irawan, R.M.T. Raja Ismail","doi":"10.1016/j.cogr.2023.04.004","DOIUrl":"https://doi.org/10.1016/j.cogr.2023.04.004","url":null,"abstract":"<div><p>This study presents the method for optimal error tracking in position control for a servo pneumatic actuated robot grasper system using a new adjustable convergence rate prescribed performance control (ACR-PPC). It focuses on improving the feedback controller and the fractional-order proportional-integral-derivative (FOPID) controller used for the position control of each robot's finger. Multiple features were considered such as tracking error, rising time, faster transient response with finite-time convergence, oscillation reduction, and pressure stabilization in the pneumatic system. Experiments were conducted using a single finger of a tri-finger pneumatic gripper (TPG) robot, actuated by a single proportional valve with a double-acting cylinder (PPVDC). Two types of input trajectories were tested: step and sine wave inputs, which are common and critical for pneumatic systems. The results show that the proposed method eliminates oscillation and achieves high tracking performance within the prescribed bounds and minimal overshoot as well. The oscillation was suppressed with minimal overshoot and fast response was achieved by tuning the formulated adjustable prescribe performance function, thus improving the rising time response without significant loss of performance.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"3 ","pages":"Pages 93-106"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49732631","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

A colorization method for historical videos 一种历史视频的着色方法

Cognitive Robotics Pub Date : 2023-01-01 DOI: 10.1016/j.cogr.2023.07.001

Xin Jin, Yiqing Rong, Ke Liu, Chaoen Xiao, Xiaokun Zhang

引用次数: 0

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