2023 8th International Conference on Control and Robotics Engineering (ICCRE)最新文献_第5页

Control Design and Implementation of Robotic Massage Based on Finger Pressing Motion Analysis 基于手指按压运动分析的按摩机器人控制设计与实现

2023 8th International Conference on Control and Robotics Engineering (ICCRE) Pub Date : 2023-04-21 DOI: 10.1109/ICCRE57112.2023.10155612

Naoya Harada, T. Akiduki, M. Kitazaki, R. Tasaki

引用次数: 0

Research on Deep Spatio-Temporal Model and Its Application in Situation Prediction 深度时空模型及其在态势预测中的应用研究

2023 8th International Conference on Control and Robotics Engineering (ICCRE) Pub Date : 2023-04-21 DOI: 10.1109/ICCRE57112.2023.10155597

Qi Feng, Jinhui Zhang, Xiaoguang Gao, Maoqing Li, Chenxi Ning

引用次数: 0

A Study on the Application of ORBSLAM3 on a Mobile Differential Drive Robot ORBSLAM3在移动差动驱动机器人上的应用研究

2023 8th International Conference on Control and Robotics Engineering (ICCRE) Pub Date : 2023-04-21 DOI: 10.1109/ICCRE57112.2023.10155617

Siong Yuen Kok, Wei Yu, D. Ng

引用次数: 0

Automatic Detection of Natural Disaster Effect on Paddy Field from Satellite Images Using Deep Learning Techniques 基于深度学习技术的卫星图像水田自然灾害影响自动检测

2023 8th International Conference on Control and Robotics Engineering (ICCRE) Pub Date : 2023-04-02 DOI: 10.1109/ICCRE57112.2023.10155582

Tahmid Alavi Ishmam, Amin Ahsan Ali, Md Ahsraful Amin, A. M. Rahman

{"title":"Automatic Detection of Natural Disaster Effect on Paddy Field from Satellite Images Using Deep Learning Techniques","authors":"Tahmid Alavi Ishmam, Amin Ahsan Ali, Md Ahsraful Amin, A. M. Rahman","doi":"10.1109/ICCRE57112.2023.10155582","DOIUrl":"https://doi.org/10.1109/ICCRE57112.2023.10155582","url":null,"abstract":"This paper aims to detect rice field damage from natural disasters in Bangladesh using high-resolution satellite imagery. The authors developed ground truth data for rice field damage from the field level. At first, NDVI differences before and after the disaster are calculated to identify possible crop loss. The areas equal to and above the 0.33 threshold are marked as crop loss areas as significant changes are observed. The authors also verified crop loss areas by collecting data from local farmers. Later, different bands of satellite data (Red, Green, Blue) and (False Color Infrared) are useful to detect crop loss area. We used the NDVI different images as ground truth to train the DeepLabV3plus model. With RGB, we got IoU 0.41 and with FCI, we got IoU 0.51. As FCI uses NIR, Red, Blue bands and NDVI is normalized difference between NIR and Red bands, so greater FCI's IoU score than RGB is expected. But RGB does not perform very badly here. So, where other bands are not available, RGB can use to understand crop loss areas to some extent. The ground truth developed in this paper can be used for segmentation models with very high resolution RGB only images such as Bing, Google etc.","PeriodicalId":285164,"journal":{"name":"2023 8th International Conference on Control and Robotics Engineering (ICCRE)","volume":"394 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122886564","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

Deep-Reinforcement-Learning-Based Path Planning for Industrial Robots Using Distance Sensors as Observation 基于深度强化学习的工业机器人距离传感器路径规划

2023 8th International Conference on Control and Robotics Engineering (ICCRE) Pub Date : 2023-01-14 DOI: 10.1109/ICCRE57112.2023.10155608

Teham Bhuiyan, Linh Kästner, Yifan Hu, Benno Kutschank, Jens Lambrecht

{"title":"Deep-Reinforcement-Learning-Based Path Planning for Industrial Robots Using Distance Sensors as Observation","authors":"Teham Bhuiyan, Linh Kästner, Yifan Hu, Benno Kutschank, Jens Lambrecht","doi":"10.1109/ICCRE57112.2023.10155608","DOIUrl":"https://doi.org/10.1109/ICCRE57112.2023.10155608","url":null,"abstract":"Traditionally, collision-free path planning for industrial robots is realized by sampling-based algorithms such as RRT (Rapidly-exploring Random Tree), PRM (Probabilistic Roadmap), etc. Sampling-based algorithms require long computation times, especially in complex environments. Furthermore, the environment in which they are employed needs to be known beforehand. When utilizing these approaches in new environments, a tedious engineering effort in setting hyperparameters needs to be conducted, which is time- and cost-intensive. On the other hand, DRL (Deep Reinforcement Learning) has shown remarkable results in dealing with complex environments, generalizing new problem instances, and solving motion planning problems efficiently. On that account, this paper proposes a Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We propose an easily reproducible method to train an agent in randomized scenarios achieving generalization for unknown environments. We evaluated our model against state-of-the-art sampling- and DRL-based planners in several experiments containing static and dynamic obstacles. Results show the adaptability of our agent in new environments and the superiority in terms of path length and execution time compared to conventional methods. Our code is available on GitHub [1].","PeriodicalId":285164,"journal":{"name":"2023 8th International Conference on Control and Robotics Engineering (ICCRE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124424599","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

Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets 影响、关注、影响:自动驾驶数据集的早期评估

2023 8th International Conference on Control and Robotics Engineering (ICCRE) Pub Date : 2023-01-05 DOI: 10.1109/ICCRE57112.2023.10155607

Daniel Bogdoll, Jonas Hendl, Felix Schreyer, Nisha S. Gowda, Michael Farber, J. Zollner

引用次数: 4