Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649943
Changhyeong Lee, Junwoo Jason Son, Hakjun Lee, Soohee Han
This paper analyze the energy consumption of a new concept of tethered quadrotor system, downward tethered quadrotor (DTQ), to provide insight about using DTQ. DTQ is one of a tethered quadrotor system in which station is located above the flight level of the quadrotor. Thanks to its system layout, the tether can play an important role in energy efficient flight of the quadrotor by adjusting the tension properly. In order to extremize the advantage of DTQ, optimization problem for mechanical power consumption of DTQ in hovering states is formulated. The DTQ in this paper considers misalignment between tether link point on the quadrotor and center of mass (CoM) of the quadrotor to simulate real system. Numerical simulation illustrates efficiency of the proposed method.
{"title":"Energy Consumption Analysis of Downward-Tethered Quadcopter","authors":"Changhyeong Lee, Junwoo Jason Son, Hakjun Lee, Soohee Han","doi":"10.23919/ICCAS52745.2021.9649943","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649943","url":null,"abstract":"This paper analyze the energy consumption of a new concept of tethered quadrotor system, downward tethered quadrotor (DTQ), to provide insight about using DTQ. DTQ is one of a tethered quadrotor system in which station is located above the flight level of the quadrotor. Thanks to its system layout, the tether can play an important role in energy efficient flight of the quadrotor by adjusting the tension properly. In order to extremize the advantage of DTQ, optimization problem for mechanical power consumption of DTQ in hovering states is formulated. The DTQ in this paper considers misalignment between tether link point on the quadrotor and center of mass (CoM) of the quadrotor to simulate real system. Numerical simulation illustrates efficiency of the proposed method.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"1 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":"133784197","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}
Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649798
Danwen Li, Satoshi Nishikawa, K. Kiguchi
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.
{"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}
Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649904
I. Jeong, Youngbin Son, Junwoo Jason Son, Byeongho Song, Soohee Han
In this paper, the design method of illumination optics for solid-state AMCW (Amplitude Modulated Continuous Wave) LiDAR (Light Detection And Ranging) is suggested. To satisfy the horizontally long and vertically narrow atypical ROI (Region of Interest) of LiDAR, the concept of etendue is applied. For light sources placed in a rectangle shape, the short side of the source corresponds to the slow axis and the long side corresponds to the fast axis. In order to cover a light source placed in a rectangle, a translational design in which the cross section of the lens is extended in the axial direction is adopted. The refractive surface of the lens is modeled with two parametric curves, and parameters are adjusted to satisfy the given radiance criteria. In the design example, the design of a primary lens composed of one circular arc and a secondary lens formed by connecting circular arcs was derived. The designed optical system showed an efficiency of about 65% and a uniformity of 12%.
{"title":"Nonimaging Optical Irradiance Optimization for Enhanced ROI of Mobile Robot LiDAR","authors":"I. Jeong, Youngbin Son, Junwoo Jason Son, Byeongho Song, Soohee Han","doi":"10.23919/ICCAS52745.2021.9649904","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649904","url":null,"abstract":"In this paper, the design method of illumination optics for solid-state AMCW (Amplitude Modulated Continuous Wave) LiDAR (Light Detection And Ranging) is suggested. To satisfy the horizontally long and vertically narrow atypical ROI (Region of Interest) of LiDAR, the concept of etendue is applied. For light sources placed in a rectangle shape, the short side of the source corresponds to the slow axis and the long side corresponds to the fast axis. In order to cover a light source placed in a rectangle, a translational design in which the cross section of the lens is extended in the axial direction is adopted. The refractive surface of the lens is modeled with two parametric curves, and parameters are adjusted to satisfy the given radiance criteria. In the design example, the design of a primary lens composed of one circular arc and a secondary lens formed by connecting circular arcs was derived. The designed optical system showed an efficiency of about 65% and a uniformity of 12%.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"36 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":"117263118","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}
Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649902
Yuxiang Zhang, Xiaoling Liang, S. Ge, B. Gao, Tong-heng Lee
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.
{"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}
Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649893
Nguyen Duc Toan, Kim Gon-Woo
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.
{"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}
Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649844
Hyeon-Woo Na, P. Park
This paper analyzes the stability of sampled-data multi-agent systems with a weighted consensus protocol by the use of looped-functional and free matrix based integral inequality. In the existing stability analysis of the multi-agent system, the typical Lyapunov-functional was used, but a less conservative solution can be obtained by using the looped-functional which is developed for the single-agent system. In addition, when analyzing the stability using Lyapunov-functional, integral inequality is used to obtain the upper bound of the integral term. A larger maximum sampling interval can be obtained by using the free matrix based integral inequality which is developed in time-delay system recently. Therefore, in this paper, the Lyapunov-functional including the looped-functional was constructed, the stability condition was relaxed using the free matrix based integral inequality, and the system was confirmed to be stable at the larger sampling interval compared to the existing literature through experimental examples.
{"title":"Application of free matrix based integral inequality: sampled-data multi-agent system","authors":"Hyeon-Woo Na, P. Park","doi":"10.23919/ICCAS52745.2021.9649844","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649844","url":null,"abstract":"This paper analyzes the stability of sampled-data multi-agent systems with a weighted consensus protocol by the use of looped-functional and free matrix based integral inequality. In the existing stability analysis of the multi-agent system, the typical Lyapunov-functional was used, but a less conservative solution can be obtained by using the looped-functional which is developed for the single-agent system. In addition, when analyzing the stability using Lyapunov-functional, integral inequality is used to obtain the upper bound of the integral term. A larger maximum sampling interval can be obtained by using the free matrix based integral inequality which is developed in time-delay system recently. Therefore, in this paper, the Lyapunov-functional including the looped-functional was constructed, the stability condition was relaxed using the free matrix based integral inequality, and the system was confirmed to be stable at the larger sampling interval compared to the existing literature through experimental examples.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"6 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":"114806198","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}
Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649968
Wooseok Oh, Hwiyeon Yoo, Timothy Ha, Songhwai Oh
The vision-based 3D reconstruction methods have various advantages and can be used in various applications such as navigation. Although various vision-based methods are being studied, it is difficult to reconstruct many parts at once with a general camera because of a small FOV. To solve this problem, we propose a coarse but lightweight reconstruction method using a camera with a unique structure called a compound eye with various advantages such as large FOV. In the process, we devise a network that performs depth estimation on a compound eye structure to obtain a depth image containing 3D information from an RGB image. We tested our methods by collecting data using a compound eye camera implemented in a Gazebo simulation and simulation scenes we created. As a result, our 3D reconstruction method using the data we collected and the confidence score from our depth estimation result, can capture the environment with a high recall of 97.51 %.
{"title":"Vision-Based 3D Reconstruction Using a Compound Eye Camera","authors":"Wooseok Oh, Hwiyeon Yoo, Timothy Ha, Songhwai Oh","doi":"10.23919/ICCAS52745.2021.9649968","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649968","url":null,"abstract":"The vision-based 3D reconstruction methods have various advantages and can be used in various applications such as navigation. Although various vision-based methods are being studied, it is difficult to reconstruct many parts at once with a general camera because of a small FOV. To solve this problem, we propose a coarse but lightweight reconstruction method using a camera with a unique structure called a compound eye with various advantages such as large FOV. In the process, we devise a network that performs depth estimation on a compound eye structure to obtain a depth image containing 3D information from an RGB image. We tested our methods by collecting data using a compound eye camera implemented in a Gazebo simulation and simulation scenes we created. As a result, our 3D reconstruction method using the data we collected and the confidence score from our depth estimation result, can capture the environment with a high recall of 97.51 %.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"26 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":"116679141","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}
Pub Date : 2021-10-12DOI: 10.5302/j.icros.2021.21.0143
Choi Keonghun, J. Ha
In the case of the unmanned surveillance system field, even if it is the same object, the detection result will be different depending on the state of the object and the configuration of the surrounding environment. Therefore, artificial intelligence for unmanned surveillance needs to understand the environment on the image, understand the state of the object within the image, and understand the relationship between them. For this purpose, in this study, a transformed transformer structure that can receive a single image, which is 2D data, as an input, unlike splitting one image into a certain size and using it as an input, is presented, and the effect between neighboring pixels is considered by using a segmentation model to which it is applied. A possible background classification model was constructed.
{"title":"Visual surveillance transformer","authors":"Choi Keonghun, J. Ha","doi":"10.5302/j.icros.2021.21.0143","DOIUrl":"https://doi.org/10.5302/j.icros.2021.21.0143","url":null,"abstract":"In the case of the unmanned surveillance system field, even if it is the same object, the detection result will be different depending on the state of the object and the configuration of the surrounding environment. Therefore, artificial intelligence for unmanned surveillance needs to understand the environment on the image, understand the state of the object within the image, and understand the relationship between them. For this purpose, in this study, a transformed transformer structure that can receive a single image, which is 2D data, as an input, unlike splitting one image into a certain size and using it as an input, is presented, and the effect between neighboring pixels is considered by using a segmentation model to which it is applied. A possible background classification model was constructed.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"289 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":"120913060","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}
Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649912
Jasper Z. Tan, A. Dasgupta, Arjun Agrawal, S. Srigrarom
A novel control & software architecture using ROS C++ is introduced for object interception by a UAV with a mounted depth camera and no external aid. Existing work in trajectory prediction focused on the use of off-board tools like motion capture rooms to intercept thrown objects. The present study designs the UAV architecture to be completely on-board capable of object interception with the use of a depth camera and point cloud processing. The architecture uses an iterative trajectory prediction algorithm for non-propelled objects like a ping-pong ball. A variety of path planning approaches to object interception and their corresponding scenarios are discussed, evaluated & simulated in Gazebo. The successful simulations exemplify the potential of using the proposed architecture for the onboard autonomy of UAVs intercepting objects.
{"title":"Trajectory Prediction & Path Planning for an Object Intercepting UAV with a Mounted Depth Camera","authors":"Jasper Z. Tan, A. Dasgupta, Arjun Agrawal, S. Srigrarom","doi":"10.23919/ICCAS52745.2021.9649912","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649912","url":null,"abstract":"A novel control & software architecture using ROS C++ is introduced for object interception by a UAV with a mounted depth camera and no external aid. Existing work in trajectory prediction focused on the use of off-board tools like motion capture rooms to intercept thrown objects. The present study designs the UAV architecture to be completely on-board capable of object interception with the use of a depth camera and point cloud processing. The architecture uses an iterative trajectory prediction algorithm for non-propelled objects like a ping-pong ball. A variety of path planning approaches to object interception and their corresponding scenarios are discussed, evaluated & simulated in Gazebo. The successful simulations exemplify the potential of using the proposed architecture for the onboard autonomy of UAVs intercepting objects.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"3 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":"114557829","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}
Pub Date : 2021-10-12DOI: 10.23919/ICCAS52745.2021.9649876
J. Yoo, Donghwi Kim, D. Shim
This study designed an intelligent control system for autonomous air-to-air combat and verified it in a realtime flight simulation. Previous studies of aerial combat have required significant effort to design agile control actions for different engagement conditions. In this work, optimal flight control under random engagement conditions was performed by using reinforcement learning and recurrent neural networks. A target trajectory was predicted using Sequence-to-Sequence model with LSTM, for occupying an advantageous location from an enemy aircraft in a close engagement. In addition, this study proposed an algorithm with improved performance compared to the existing algorithm. The result of the study confirmed that the maneuvers of trained agent were similar to the performance of human pilots and the future position of the enemy was tracked by own ship aircraft.
{"title":"Deep Reinforcement Learning based Autonomous Air-to-Air Combat using Target Trajectory Prediction","authors":"J. Yoo, Donghwi Kim, D. Shim","doi":"10.23919/ICCAS52745.2021.9649876","DOIUrl":"https://doi.org/10.23919/ICCAS52745.2021.9649876","url":null,"abstract":"This study designed an intelligent control system for autonomous air-to-air combat and verified it in a realtime flight simulation. Previous studies of aerial combat have required significant effort to design agile control actions for different engagement conditions. In this work, optimal flight control under random engagement conditions was performed by using reinforcement learning and recurrent neural networks. A target trajectory was predicted using Sequence-to-Sequence model with LSTM, for occupying an advantageous location from an enemy aircraft in a close engagement. In addition, this study proposed an algorithm with improved performance compared to the existing algorithm. The result of the study confirmed that the maneuvers of trained agent were similar to the performance of human pilots and the future position of the enemy was tracked by own ship aircraft.","PeriodicalId":411064,"journal":{"name":"2021 21st International Conference on Control, Automation and Systems (ICCAS)","volume":"59 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":"128375946","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}
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