2021 24th International Conference on Mechatronics Technology (ICMT)最新文献_第5页

Advanced Simulation Tool to Develop Efficient Thermal Management Systems for Electric Vehicles 先进的仿真工具开发高效的电动汽车热管理系统

2021 24th International Conference on Mechatronics Technology (ICMT) Pub Date : 2021-12-18 DOI: 10.1109/ICMT53429.2021.9687213

I. M. Sofi, T. Dinh, Araan Mohanadass, J. Jeffs, Q. T. Trương, Ngoc Minh Truong Bui

引用次数: 0

Modeling of Onshore Wave Energy Converter: Inverse Dynamic Analysis and Thermal Prediction 陆上波浪能量转换器建模:逆动力分析和热预报

2021 24th International Conference on Mechatronics Technology (ICMT) Pub Date : 2021-12-18 DOI: 10.1109/ICMT53429.2021.9687282

A. Senatore, Alex De Simone, Martina Travaglino, Mario Pisaturo, Veronica D'Urso

引用次数: 0

Research on a New Independent Metering System for Boom Excavator 一种新型臂架挖掘机独立计量系统研究

2021 24th International Conference on Mechatronics Technology (ICMT) Pub Date : 2021-12-18 DOI: 10.1109/ICMT53429.2021.9687258

T. Nguyen, T. Do, K. Ahn

引用次数: 3

A Smart Grasping System for Handling Irregular, Naturally Varying Objects 一种处理不规则、自然变化物体的智能抓取系统

2021 24th International Conference on Mechatronics Technology (ICMT) Pub Date : 2021-12-18 DOI: 10.1109/ICMT53429.2021.9687240

Zhicong Deng, L. Holibar, E. Wester

{"title":"A Smart Grasping System for Handling Irregular, Naturally Varying Objects","authors":"Zhicong Deng, L. Holibar, E. Wester","doi":"10.1109/ICMT53429.2021.9687240","DOIUrl":"https://doi.org/10.1109/ICMT53429.2021.9687240","url":null,"abstract":"This paper presents the design, integration, and validation of a smart grasping system for handling irregular, naturally varying objects. The system consists of a 6-axis robot, a soft robotic gripper, a vision sensor and a computer. A grasping algorithm utilizing reinforcement learning is imple-mented to provide the flexibility and adaptiveness required to handle object variations. Benchmark testing were conducted on simple objects and the system achieved a 68 % grasp success rate after 1500 training iterations. Improvements to the system were then implemented including the repositioning of the vision sensor, a reset mechanism and a collision avoidance algorithm. A grasp success rate of 80% was achieved with the improved system. Kumara (sweet potato) was selected in this case as an example of irregular, naturally varying objects. Initial training and testing with kumara proved to be challenging and a pre-training approach with annotated images were proposed and implemented. Human grasping experience was incorporated into the grasping system via the pre-training and a 71 % grasp success rate was achieved after 1500 iterations.","PeriodicalId":258783,"journal":{"name":"2021 24th International Conference on Mechatronics Technology (ICMT)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130287467","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

Adaptive Optimal Control for Upper Exoskeleton following Saturation Function 基于饱和函数的上肢外骨骼自适应优化控制

2021 24th International Conference on Mechatronics Technology (ICMT) Pub Date : 2021-12-18 DOI: 10.1109/ICMT53429.2021.9687228

D. Phu, Bien Minh Tri

引用次数: 0

RNN Based Knee Joint Muscular Torque Estimation of a Knee Exoskeleton for Stair Climbing 基于RNN的爬楼梯膝关节外骨骼关节肌肉力矩估计

2021 24th International Conference on Mechatronics Technology (ICMT) Pub Date : 2021-12-18 DOI: 10.1109/ICMT53429.2021.9687219

Chun-Yi Kuo, Dun-Yan Wu, Chi-Ying Lin

{"title":"RNN Based Knee Joint Muscular Torque Estimation of a Knee Exoskeleton for Stair Climbing","authors":"Chun-Yi Kuo, Dun-Yan Wu, Chi-Ying Lin","doi":"10.1109/ICMT53429.2021.9687219","DOIUrl":"https://doi.org/10.1109/ICMT53429.2021.9687219","url":null,"abstract":"This study presents the use of a recurrent neural network to estimate knee joint muscular torques for the development of assistive control strategies of a knee exoskeleton in stair climbing applications. To identify the correct timing of giving assistive torques during the stair climbing process, integrating with a lower limb dynamic model with the foot-force measured data is a common way to derive the knee joint torque profile for gait analysis. However, this estimation method which requires the installation of pressure sensors on the sole of the feet has drawbacks including the inconvenience of exoskeleton wearing and increased moving difficulty. The fact that stair climbing is a sequential movement thus allows us to apply a recurrent neural network to obtain the relationship between the knee joint muscular torque and lower limb gait. Stair climbing experiments on a knee exoskeleton wearer reveal that the trained neural network is able to perform the desired knee joint torque estimation whose results can be applied to derive proper assistive torques in the presence of human-robot interaction.","PeriodicalId":258783,"journal":{"name":"2021 24th International Conference on Mechatronics Technology (ICMT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129076457","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