2021 21st International Conference on Control, Automation and Systems (ICCAS)最新文献_第9页

Energy Consumption Analysis of Downward-Tethered Quadcopter 下系留四轴飞行器的能耗分析

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649943

Changhyeong Lee, Junwoo Jason Son, Hakjun Lee, Soohee Han

引用次数: 3

Control of ankle joint motion change induced by vibration stimulation on the Gastrocnemius muscle during continuous joint motion 连续关节运动中腓肠肌振动刺激引起的踝关节运动变化的控制

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649798

Danwen Li, Satoshi Nishikawa, K. Kiguchi

{"title":"Control of ankle joint motion change induced by vibration stimulation on the Gastrocnemius muscle during continuous joint motion","authors":"Danwen Li, Satoshi Nishikawa, K. Kiguchi","doi":"10.23919/ICCAS52745.2021.9649798","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649798","url":null,"abstract":"It has been shown that changes in movement caused by reflex phenomena and kinesthetic illusion can be induced by giving vibratory stimulation on specific muscles during limb movements. By using this phenomenon, there is a possibility that the lower-limb perception-assist, in which inappropriate motion is automatically modified, is realized. In this study, change of ankle joint motion by giving the vibration stimulation on the gastrocnemius muscle in the lower-limb is studied by performing the experiment of adding vibration stimulation to the gastrocnemius muscle during the movement of ankle joint dorsiflexion and plantar flexion under several frequency ranges. The results indicate that the amount of motion change varies depending on the frequency of the vibration stimulation. Based on the characteristics of motion change with respect to vibration stimulation, a method to control the change of the user's ankle joint dorsiflexion motion by adjusting the frequency of the vibration stimulation to achieve the target motion, which is different from the user's intended motion, is evaluated. It is confirmed that there is a possibility that the vibration stimulation can be applicable to modify the human ankle joint motion for the perception-assist using frequency change.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122446086","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

Nonimaging Optical Irradiance Optimization for Enhanced ROI of Mobile Robot LiDAR 提高移动机器人激光雷达ROI的非成像光辐照度优化

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649904

I. Jeong, Youngbin Son, Junwoo Jason Son, Byeongho Song, Soohee Han

引用次数: 0

Barrier Lyapunov Function-Based Safe Reinforcement Learning Algorithm for Autonomous Vehicles with System Uncertainty 基于Barrier Lyapunov函数的不确定性自动驾驶汽车安全强化学习算法

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649902

Yuxiang Zhang, Xiaoling Liang, S. Ge, B. Gao, Tong-heng Lee

{"title":"Barrier Lyapunov Function-Based Safe Reinforcement Learning Algorithm for Autonomous Vehicles with System Uncertainty","authors":"Yuxiang Zhang, Xiaoling Liang, S. Ge, B. Gao, Tong-heng Lee","doi":"10.23919/ICCAS52745.2021.9649902","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649902","url":null,"abstract":"Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. For such safety-critical systems, it will certainly be a requirement that safe performance should be ensured even during the reinforcement learning period in the presence of system uncertainty. To address this issue, a Barrier Lyapunov Function-based safe reinforcement learning algorithm (BLF-SRL) is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges the Barrier Lyapunov Function item into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning when unknown bounded system uncertainty exists. More specifically, the overall system control is optimized with the optimized backstepping technique under the framework of Actor-Critic, which optimizes the virtual control in every backstepping subsystem. Wherein, the optimal virtual control is decomposed into Barrier Lyapunov Function items; and also with an adaptive item to be learned with deep neural networks, which achieves safe exploration during the learning process. Eventually, the principle of Bellman optimality is satisfied through iteratively updating the independently approximated actor and critic to solve the Hamilton-Jacobi-Bellman equation in adaptive dynamic programming. More notably, the variance of control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with motion control problems for autonomous vehicles through appropriate comparison simulations.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115208446","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

Environment Exploration for Mapless Navigation based on Deep Reinforcement Learning 基于深度强化学习的无地图导航环境探索

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649893

Nguyen Duc Toan, Kim Gon-Woo

{"title":"Environment Exploration for Mapless Navigation based on Deep Reinforcement Learning","authors":"Nguyen Duc Toan, Kim Gon-Woo","doi":"10.23919/ICCAS52745.2021.9649893","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649893","url":null,"abstract":"In recent years, reinforcement learning has attracted researchers' attention with the AlphaGo event. Especially in autonomous mobile robots, the reinforcement learning approach can be applied to the mapless navigation problem. The Robot can complete the set tasks well and works well in different environments without maps and ready-made path plans. However, for reinforcement learning in general and mapless navigation based on reinforcement learning in particular, exploitation and exploration balance are issues that need to be carefully considered. Specifically, the fact that the agent (Robot) can discover and execute actions in a particular working environment plays a significant role in improving the performance of the reinforcement learning problem. By creating some noise during the convolutional neural network training, the above problem can be solved by some popular approaches today. With outstanding advantages compared to other approaches, the Boltzmann policy approach has been used in our problem. It helps the Robot explore more thoroughly in complex environments, and the policy is also more optimized.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117105691","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: 2

Application of free matrix based integral inequality: sampled-data multi-agent system 基于自由矩阵的积分不等式在抽样数据多智能体系统中的应用

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649844

Hyeon-Woo Na, P. Park

引用次数: 0

Vision-Based 3D Reconstruction Using a Compound Eye Camera 使用复眼相机的基于视觉的3D重建

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649968

Wooseok Oh, Hwiyeon Yoo, Timothy Ha, Songhwai Oh

引用次数: 2

Visual surveillance transformer 可视监控变压器

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.5302/j.icros.2021.21.0143

Choi Keonghun, J. Ha

引用次数: 0

Trajectory Prediction & Path Planning for an Object Intercepting UAV with a Mounted Depth Camera 安装深度相机的目标拦截无人机的轨迹预测与路径规划

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649912

Jasper Z. Tan, A. Dasgupta, Arjun Agrawal, S. Srigrarom

引用次数: 0

Deep Reinforcement Learning based Autonomous Air-to-Air Combat using Target Trajectory Prediction 基于目标轨迹预测的深度强化学习自主空对空作战

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649876

J. Yoo, Donghwi Kim, D. Shim

引用次数: 5