Pub Date : 2022-03-08DOI: 10.23919/SICEISCS54350.2022.9754518
L. Zhiyuan, Shunya Yamashita, Takeshi Hatanaka
This paper investigates persistent environmental monitoring of a target area using a drone network. We first design control barrier functions (CBFs) to enforce drones to meet various specifications including battery charging. The combination of coverage control and CBF-based online optimization is shown to achieve persistent monitoring while meeting the specifications. However, the CBF for battery charging enforces each drone to return to a preassigned charging station before battery exhaustion, which may degrade efficiency for the operation. To address the issue, we incorporate a high-level control mechanism for online assignment of charging stations into the persistent coverage control. In order to design the high-level controller, we present a distributed solution to the assignment problem based on so-called alternating direction method of multipliers. The overall system is finally demonstrated through simulation.
{"title":"Persistent Environmental Monitoring with Distributed Online Assignment of Charging Stations","authors":"L. Zhiyuan, Shunya Yamashita, Takeshi Hatanaka","doi":"10.23919/SICEISCS54350.2022.9754518","DOIUrl":"https://doi.org/10.23919/SICEISCS54350.2022.9754518","url":null,"abstract":"This paper investigates persistent environmental monitoring of a target area using a drone network. We first design control barrier functions (CBFs) to enforce drones to meet various specifications including battery charging. The combination of coverage control and CBF-based online optimization is shown to achieve persistent monitoring while meeting the specifications. However, the CBF for battery charging enforces each drone to return to a preassigned charging station before battery exhaustion, which may degrade efficiency for the operation. To address the issue, we incorporate a high-level control mechanism for online assignment of charging stations into the persistent coverage control. In order to design the high-level controller, we present a distributed solution to the assignment problem based on so-called alternating direction method of multipliers. The overall system is finally demonstrated through simulation.","PeriodicalId":391189,"journal":{"name":"2022 SICE International Symposium on Control Systems (SICE ISCS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121931781","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 : 2022-03-08DOI: 10.23919/SICEISCS54350.2022.9754368
Jiulin Zhu, N. Sebe
A support program that implements the overbounding approximation method for MATLAB and YALMIP is developed. The overbounding approximation is one of the methods that provide sufficient LMI constraints for the given BMI constraints. With this method, one can obtain an approximate solution to the given BMI problem. However, it is not easy for ordinary users to implement the method to solve BMI problems. To overcome the difficulty, we provide a function that finds initial solutions for optimization and solves BMI problems automatically. This function enables users to obtain solutions for the BMI problem easily.
{"title":"Development of Support Programs for Solving BMI Problems by Overbounding Approximation Method","authors":"Jiulin Zhu, N. Sebe","doi":"10.23919/SICEISCS54350.2022.9754368","DOIUrl":"https://doi.org/10.23919/SICEISCS54350.2022.9754368","url":null,"abstract":"A support program that implements the overbounding approximation method for MATLAB and YALMIP is developed. The overbounding approximation is one of the methods that provide sufficient LMI constraints for the given BMI constraints. With this method, one can obtain an approximate solution to the given BMI problem. However, it is not easy for ordinary users to implement the method to solve BMI problems. To overcome the difficulty, we provide a function that finds initial solutions for optimization and solves BMI problems automatically. This function enables users to obtain solutions for the BMI problem easily.","PeriodicalId":391189,"journal":{"name":"2022 SICE International Symposium on Control Systems (SICE ISCS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134098714","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 : 2022-03-08DOI: 10.23919/SICEISCS54350.2022.9754519
T. Kimura, K. Hoshino, Y. Asano, Akihiko Honda, Norizumi Motooka, T. Ohtsuka
The object used in this study is an unmanned aerial vehicle (a quadcopter) equipped with an internal control system that has a target position and yaw angle as input commands. Simple internal control systems in quadcopters often show limited performance, especially when quadcopters attempt to track trajectories with large velocities. This study applies a nonlinear model predictive control (NMPC) method to improve the tracking performance of quadcopters equipped with simple internal control systems. The NMPC is introduced because it can take the motion of the quadcopter arising from the internal control system into consideration. This study also handles the control of the yaw angle in addition to the tracking of the position, which is required in some applications, such as aerial photography and surveillance. We evaluated the proposed method through a simulation with the task of tracking a sinusoidal trajectory and facing its direction of movement. In the simulation, the proposed method showed a performance that could not be accomplished using only the internal control system. This result indicates that determining the command with NMPC improves the tracking performance of the position.
{"title":"Application of Nonlinear Model Predictive Control to Quadcopter Equipped with Internal Control System","authors":"T. Kimura, K. Hoshino, Y. Asano, Akihiko Honda, Norizumi Motooka, T. Ohtsuka","doi":"10.23919/SICEISCS54350.2022.9754519","DOIUrl":"https://doi.org/10.23919/SICEISCS54350.2022.9754519","url":null,"abstract":"The object used in this study is an unmanned aerial vehicle (a quadcopter) equipped with an internal control system that has a target position and yaw angle as input commands. Simple internal control systems in quadcopters often show limited performance, especially when quadcopters attempt to track trajectories with large velocities. This study applies a nonlinear model predictive control (NMPC) method to improve the tracking performance of quadcopters equipped with simple internal control systems. The NMPC is introduced because it can take the motion of the quadcopter arising from the internal control system into consideration. This study also handles the control of the yaw angle in addition to the tracking of the position, which is required in some applications, such as aerial photography and surveillance. We evaluated the proposed method through a simulation with the task of tracking a sinusoidal trajectory and facing its direction of movement. In the simulation, the proposed method showed a performance that could not be accomplished using only the internal control system. This result indicates that determining the command with NMPC improves the tracking performance of the position.","PeriodicalId":391189,"journal":{"name":"2022 SICE International Symposium on Control Systems (SICE ISCS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126745036","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 : 2022-03-08DOI: 10.23919/SICEISCS54350.2022.9754373
Steve Lash, F. Saleheen, Chang-Hee Won
Here we present a hybrid hierarchical statistical control approach for the control of robotic manipulators. The bimodal dynamic imaging system considered in this paper utilizes two robotic manipulators to move the source and detector imaging modules. As this system contains both continuous and discrete dynamics, hybrid system control techniques are applied. The robotic arms used in this research are comprised of compliant joints, which have been shown to introduce process noise into the system. To address this, a full-state feedback statistical controller is developed to minimize joint angle variations for the system. The statistical controllers for the two robot arms are then coordinated using a hierarchical controller. Finally, the feasibility of the hybrid hierarchical statistical controller is demonstrated with numerical simulations.
{"title":"Hybrid Hierarchical Statistical Control of Robotic Manipulators","authors":"Steve Lash, F. Saleheen, Chang-Hee Won","doi":"10.23919/SICEISCS54350.2022.9754373","DOIUrl":"https://doi.org/10.23919/SICEISCS54350.2022.9754373","url":null,"abstract":"Here we present a hybrid hierarchical statistical control approach for the control of robotic manipulators. The bimodal dynamic imaging system considered in this paper utilizes two robotic manipulators to move the source and detector imaging modules. As this system contains both continuous and discrete dynamics, hybrid system control techniques are applied. The robotic arms used in this research are comprised of compliant joints, which have been shown to introduce process noise into the system. To address this, a full-state feedback statistical controller is developed to minimize joint angle variations for the system. The statistical controllers for the two robot arms are then coordinated using a hierarchical controller. Finally, the feasibility of the hybrid hierarchical statistical controller is demonstrated with numerical simulations.","PeriodicalId":391189,"journal":{"name":"2022 SICE International Symposium on Control Systems (SICE ISCS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133134432","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 : 2022-03-08DOI: 10.23919/SICEISCS54350.2022.9754516
Keigo Watanabe, Maierdan Maimaitimin, Yutaka Takashima, I. Nagai
In loading work with a helicopter or a crane, the body itself recognizes an environment, and in order to develop a system that judges whether the commands and operations by human are safe, it needs to realize semantic recognition of the environment, distance recognition to an obstacle, and classification and pursuit of moving objects with high precision using sensors. A method of realizing semantic segmentation, depth estimation and optical flow simultaneously from a camera image had been proposed with a multitasking DNN that took account of the posture and speed of a drone considering a loading work in the air, and it was proved to be useful in a simulated environment. Note however that the model learned in the simulation environment is not a thing suitable for the environmental recognition in a real world. Therefore, this paper aims to develop an environmental recognition system that can be used in the real world by conducting a domain adaptation with adversarial learning. The usefulness of the domain adaptation technique in the proposed multitasking DNN is verified by carrying out environmental recognition of the actual image acquired from the boom tip of a crane.
{"title":"Unsupervised Domain Adaptation for Environmental Recognition in Crane Operations","authors":"Keigo Watanabe, Maierdan Maimaitimin, Yutaka Takashima, I. Nagai","doi":"10.23919/SICEISCS54350.2022.9754516","DOIUrl":"https://doi.org/10.23919/SICEISCS54350.2022.9754516","url":null,"abstract":"In loading work with a helicopter or a crane, the body itself recognizes an environment, and in order to develop a system that judges whether the commands and operations by human are safe, it needs to realize semantic recognition of the environment, distance recognition to an obstacle, and classification and pursuit of moving objects with high precision using sensors. A method of realizing semantic segmentation, depth estimation and optical flow simultaneously from a camera image had been proposed with a multitasking DNN that took account of the posture and speed of a drone considering a loading work in the air, and it was proved to be useful in a simulated environment. Note however that the model learned in the simulation environment is not a thing suitable for the environmental recognition in a real world. Therefore, this paper aims to develop an environmental recognition system that can be used in the real world by conducting a domain adaptation with adversarial learning. The usefulness of the domain adaptation technique in the proposed multitasking DNN is verified by carrying out environmental recognition of the actual image acquired from the boom tip of a crane.","PeriodicalId":391189,"journal":{"name":"2022 SICE International Symposium on Control Systems (SICE ISCS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121475341","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 : 2022-03-08DOI: 10.23919/SICEISCS54350.2022.9754523
Daiki Kanai, K. Nonaka, K. Sekiguchi
In this study, a stabilizing controller is proposed based on model predictive control for a leg/wheel mobile robot. To assure the controller’s stability, after applying extended linearization, we employ linear matrix inequality (LMI) to design parameters that guarantee stability and feasibility. Converting kinematic constraints between a body and legs into LMI forms, we realize a controller with the stability and the kinematic constraints. A numerical simulation is conducted, and it is confirmed that a robot can move on a curved surface with a small error while satisfying the terminal condition.
{"title":"LMI-based Control Synthesis for a Leg/Wheel Mobile Robot","authors":"Daiki Kanai, K. Nonaka, K. Sekiguchi","doi":"10.23919/SICEISCS54350.2022.9754523","DOIUrl":"https://doi.org/10.23919/SICEISCS54350.2022.9754523","url":null,"abstract":"In this study, a stabilizing controller is proposed based on model predictive control for a leg/wheel mobile robot. To assure the controller’s stability, after applying extended linearization, we employ linear matrix inequality (LMI) to design parameters that guarantee stability and feasibility. Converting kinematic constraints between a body and legs into LMI forms, we realize a controller with the stability and the kinematic constraints. A numerical simulation is conducted, and it is confirmed that a robot can move on a curved surface with a small error while satisfying the terminal condition.","PeriodicalId":391189,"journal":{"name":"2022 SICE International Symposium on Control Systems (SICE ISCS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126630096","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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