2021 21st International Conference on Control, Automation and Systems (ICCAS)最新文献_第8页

Reinforcement Learning-based Sim-to-Real Impedance Parameter Tuning for Robotic Assembly 基于强化学习的机器人装配阻抗参数拟实整定

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649923

Yong-Geon Kim, Min-Woo Na, Jae-Bok Song

{"title":"Reinforcement Learning-based Sim-to-Real Impedance Parameter Tuning for Robotic Assembly","authors":"Yong-Geon Kim, Min-Woo Na, Jae-Bok Song","doi":"10.23919/ICCAS52745.2021.9649923","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649923","url":null,"abstract":"When performing robotic assembly, a task should be conducted through force-based control such as impedance control. Using impedance control, it is possible to control the contact force by appropriately adjusting the impedance parameters. However, the impedance parameters should be set by the user because it is difficult to accurately recognize the dynamics of the contact environment, which takes a lot of time because it should be performed whenever the assembly task changes. Moreover, the parameters may not be optimal because it depends on the experience and skill level of the user. To this end, a reinforcement learning-based impedance parameter tuning method is proposed in this study. Since this method uses only the physics-based robotic simulation on the virtual environment, there is no risk of damaging the robots or parts and learning time can be significantly reduced. The proposed method was verified by assembling an HDMI connector with a tolerance of 0.03 mm. Impedance parameters were learned in the virtual environment and transferred to the real environment. Finally, it was confirmed that parameter tuning for impedance without the aid of the user is possible by using the proposed method.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"10 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":"131766765","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

Enhanced Dual Adversarial Network for Real Image Noise Removal and Generation using Edge Loss Function 基于边缘损失函数的实景图像噪声去除与生成的增强双对抗网络

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649822

Eunho Lee, Youngbae Hwang

引用次数: 0

End-to-End control of USV swarm using graph centric Multi-Agent Reinforcement Learning 基于图中心多智能体强化学习的USV群端到端控制

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649839

Kanghoon Lee, Kyuree Ahn, Jinkyoo Park

{"title":"End-to-End control of USV swarm using graph centric Multi-Agent Reinforcement Learning","authors":"Kanghoon Lee, Kyuree Ahn, Jinkyoo Park","doi":"10.23919/ICCAS52745.2021.9649839","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649839","url":null,"abstract":"The Unmanned Surface Vehicles (USVs), which operate without a person at the surface, are used in various naval defense missions. Various missions can be conducted efficiently when a swarm of USVs are operated at the same time. However, it is challenging to establish a decentralised control strategy for all USVs. In addition, the strategy must consider various external factors, such as the ocean topography and the number of enemy forces. These difficulties necessitate a scalable and transferable decision-making module. This study proposes an algorithm to derive the decentralised and cooperative control strategy for the USV swarm using graph centric multi-agent reinforcement learning (MARL). The model first expresses the mission situation using a graph considering the various sensor ranges. Each USV agent encodes observed information into localized embedding and then derives coordinated action through communication with the surrounding agent. To derive a cooperative policy, we trained each agent's policy to maximize the team reward. Using the modified prey-predator environment of OpenAI gym, we have analyzed the effect of each component of the proposed model (state embedding, communication, and team reward). The ablation study shows that the proposed model could derive a scalable and transferable control policy of USVs, consistently achieving the highest win ratio.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"21 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":"134356226","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: 3

An Instantaneous Impact Point Guidance for Rocket with Aerodynamics Control 基于空气动力学控制的火箭瞬时弹着点制导

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649922

Ki-Wook Jung, Chang-Hun Lee, Jun-Seong Lee, Sunghyuck Im, Keejoo Lee, Marco Sagliano, David Seelbinder

{"title":"An Instantaneous Impact Point Guidance for Rocket with Aerodynamics Control","authors":"Ki-Wook Jung, Chang-Hun Lee, Jun-Seong Lee, Sunghyuck Im, Keejoo Lee, Marco Sagliano, David Seelbinder","doi":"10.23919/ICCAS52745.2021.9649922","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649922","url":null,"abstract":"This paper aims to propose a new guidance algorithm for a rocket with aerodynamics control for launch operations, based on the concept of the instantaneous impact point (IIP). In this study, the rocket with aerodynamics control is considered with the purpose of reducing dispersion of the impact point after separation of the rocket for safety reasons. Since a very limited aerodynamic maneuverability is typically allowed for the rocket due to the structural limit, a guidance algorithm producing a huge acceleration demand is not desirable. Based on this aspect, the proposed guidance algorithm is derived directly from the underlying principle of the guidance process: forming the collision geometry towards a target point. To be more specific, the collision-ballistic-trajectory where the instantaneous impact point becomes the target point, and the corresponding heading error are first determined using a rapid ballistic trajectory prediction technique. Here, the trajectory prediction method is based on the partial closed-form solutions of the ballistic trajectory equations considering aerodynamic drag and gravity. And then, the proposed guidance algorithm works to nullify the heading error in a finite time, governed by the optimal error dynamics. The key feature of the proposed guidance algorithm lies in its simple implementation and exact collision geometry nature. Hence, the proposed method allows achieving the collision course with minimal guidance command, and it is a desirable property for the guidance algorithm of the rocket with the aerodynamics control. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed guidance algorithms.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"62 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":"134444185","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

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

Design and Fabrication of a Robotic Knee-Type Prosthetic Leg with a Two-Way Hydraulic Cylinder 双路液压缸机器人膝型假肢的设计与制造

2021 21st International Conference on Control, Automation and Systems (ICCAS) Pub Date : 2021-10-12 DOI: 10.23919/ICCAS52745.2021.9649965

J. W. Lee, H. J. Yoon, H. S. Woo

引用次数: 0

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

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

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

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