In this paper, a new type of foldable winged unmanned aerial vehicle is designed, and the structure of the aircraft is analyzed. The dynamic model is established and an improved sliding mode control algorithm is used to control the attitude of the aircraft. In addition, a centroid position control method using attitude compensation is designed for the coupling characteristics of this aircraft. The simulation proves that the attitude and position of the new aircraft can be effectively controlled.
{"title":"Hover Control of New type ducted aircraft","authors":"Peijun Liu, Hongbin Deng, Shikun Wang, Kewei Li, Yiran Wei","doi":"10.1109/ICUS48101.2019.8995931","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8995931","url":null,"abstract":"In this paper, a new type of foldable winged unmanned aerial vehicle is designed, and the structure of the aircraft is analyzed. The dynamic model is established and an improved sliding mode control algorithm is used to control the attitude of the aircraft. In addition, a centroid position control method using attitude compensation is designed for the coupling characteristics of this aircraft. The simulation proves that the attitude and position of the new aircraft can be effectively controlled.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123521941","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 : 2019-10-01DOI: 10.1109/ICUS48101.2019.8996056
Shiming Zhao, Yingqiu Xu, Yingzi Tan
In this paper, a method of positioning grasping points and grasping posture on strange objects by using geometric constraints is proposed. The input to the algorithm is point clouds of the objects and the geometric parameters of the gripper of the manipulator. The output of the algorithm is a set of grasping points and grasping posture, which can be expected to be the best target for the manipulator to grasp the object. The algorithm first determines the grasping geometric parameters of the gripper, and then sets a series of necessary conditions for grasping the objects successfully. After that, a large number of grasp point samples are carried out on the point cloud and the necessary conditions are used for filtering to obtain a set of potential grasping points. Finally, a kind of weighted calculation method is used to obtain the best grasps. The algorithm does not need to know the objects in advance, and it can get a better grasping effect on some objects with special shape (such as ring). The algorithm is provided as a ROS package for download at https://github.com/ZSM2019/Geometry_Grasp.
{"title":"Detecting Grasping Positions and Postures in 3D Point Clouds by Geometric Constraints","authors":"Shiming Zhao, Yingqiu Xu, Yingzi Tan","doi":"10.1109/ICUS48101.2019.8996056","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8996056","url":null,"abstract":"In this paper, a method of positioning grasping points and grasping posture on strange objects by using geometric constraints is proposed. The input to the algorithm is point clouds of the objects and the geometric parameters of the gripper of the manipulator. The output of the algorithm is a set of grasping points and grasping posture, which can be expected to be the best target for the manipulator to grasp the object. The algorithm first determines the grasping geometric parameters of the gripper, and then sets a series of necessary conditions for grasping the objects successfully. After that, a large number of grasp point samples are carried out on the point cloud and the necessary conditions are used for filtering to obtain a set of potential grasping points. Finally, a kind of weighted calculation method is used to obtain the best grasps. The algorithm does not need to know the objects in advance, and it can get a better grasping effect on some objects with special shape (such as ring). The algorithm is provided as a ROS package for download at https://github.com/ZSM2019/Geometry_Grasp.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125374215","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 : 2019-10-01DOI: 10.1109/ICUS48101.2019.8996025
Peng Siting, Zhao Qian, Ma Yichao, Xu Cheng
As an important device for target interception, The seeker is used to search, recognize and track the target autonomously. Infrared semi-strapdown seekers are widely used in applications due to their low cost and small size. However, one non-negligible disadvantage of semi-strapdown infrared seekers is that the observability of target tracking model is poor, especially for the interception of high-speed maneuvering weapons. In this paper, the target tracking model ( i.e., the filtering estimation model) based on the infrared simi-strapdown seeker is established. The observability of the target tracking model is analyzed and the evaluation method of the observability is studied. Simulation results show that the observability of the filtering estimation model can be improved by increasing the relative motion between missile and target which results in faster changes of the line-of-sight (LOS) angle. According to the results on the observability analysis, the desired trajectory is obtained through the design of proper guidance law. Nonlinear filtering algorithm is also introduced for the trajectory design. Simulation experiments are conducted for the integrated filtering and guidance, and simulation results illustrate the effectiveness of the proposed observability enhancement method.
{"title":"Observability Enhancement of the Target Tracking Model Based on Infrared Semi-strapdown Seeker","authors":"Peng Siting, Zhao Qian, Ma Yichao, Xu Cheng","doi":"10.1109/ICUS48101.2019.8996025","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8996025","url":null,"abstract":"As an important device for target interception, The seeker is used to search, recognize and track the target autonomously. Infrared semi-strapdown seekers are widely used in applications due to their low cost and small size. However, one non-negligible disadvantage of semi-strapdown infrared seekers is that the observability of target tracking model is poor, especially for the interception of high-speed maneuvering weapons. In this paper, the target tracking model ( i.e., the filtering estimation model) based on the infrared simi-strapdown seeker is established. The observability of the target tracking model is analyzed and the evaluation method of the observability is studied. Simulation results show that the observability of the filtering estimation model can be improved by increasing the relative motion between missile and target which results in faster changes of the line-of-sight (LOS) angle. According to the results on the observability analysis, the desired trajectory is obtained through the design of proper guidance law. Nonlinear filtering algorithm is also introduced for the trajectory design. Simulation experiments are conducted for the integrated filtering and guidance, and simulation results illustrate the effectiveness of the proposed observability enhancement method.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126609993","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}
The paper is about redunant moving agent with intelligent unmanned control. The main task is to complete the construction of redundant fault-tolerant control system of deep neural network, including the infrastructure construction of unmanned agent simulation, the initialization of agent parameters, the construction of redundant controller, and the construction of reinforcement learning decision model. The main purpose is to generate simulated floating point data to train the model, including designing the expected rate and path, kinematics simulation, and training data generation. The kinematics simulation scene construction and decision-making model training use deep learning, whose effect of the system performance is significant.
{"title":"An Intelligent Unmanned Control Method for Redunant Moving Agent","authors":"Ying Zhang, Leiyan Tao, Minfeng Wei, Jian Cao, Siwen Xu, Xing Zhang","doi":"10.1109/ICUS48101.2019.8995957","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8995957","url":null,"abstract":"The paper is about redunant moving agent with intelligent unmanned control. The main task is to complete the construction of redundant fault-tolerant control system of deep neural network, including the infrastructure construction of unmanned agent simulation, the initialization of agent parameters, the construction of redundant controller, and the construction of reinforcement learning decision model. The main purpose is to generate simulated floating point data to train the model, including designing the expected rate and path, kinematics simulation, and training data generation. The kinematics simulation scene construction and decision-making model training use deep learning, whose effect of the system performance is significant.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122779729","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 : 2019-10-01DOI: 10.1109/ICUS48101.2019.8996086
Yang Cheng, Z. Shui, Cheng Xu, Tianyu Feng, Yiyang Jiang
The traditional predictive correction algorithm requires a large number of iterative calculations for the predicted trajectory, which greatly occupies a large amount of computing resources, so that the real-time solution of the guidance command can not be guaranteed, and the guidance accuracy will have a large impact. And the prediction correction guidance requires the algorithm to have the ability of selfadaptation and intelligent learning. Therefore, this paper proposes a cross-cycle iterative hypersonic UAV predictive correction guidance method based on reinforcement learning. The parametric control variable (CVP) method is used to construct the parametric model of the guidance command. The actor-critic-based reinforcement learning method is used to solve the guidance command in real time, and the guidance information is effectively transmitted in the adjacent guidance solution cycle. The guidance error converges to within the allowable accuracy range during the cross-cycle iteration. Monte Carlo simulation shows that the proposed method has good adaptability to initial conditions and flight parameter uncertainty, and can guarantee the real-time performance of the guidance command while achieving high-precision guidance.
{"title":"Cross-cycle iterative unmanned aerial vehicle reentry guidance based on reinforcement learning","authors":"Yang Cheng, Z. Shui, Cheng Xu, Tianyu Feng, Yiyang Jiang","doi":"10.1109/ICUS48101.2019.8996086","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8996086","url":null,"abstract":"The traditional predictive correction algorithm requires a large number of iterative calculations for the predicted trajectory, which greatly occupies a large amount of computing resources, so that the real-time solution of the guidance command can not be guaranteed, and the guidance accuracy will have a large impact. And the prediction correction guidance requires the algorithm to have the ability of selfadaptation and intelligent learning. Therefore, this paper proposes a cross-cycle iterative hypersonic UAV predictive correction guidance method based on reinforcement learning. The parametric control variable (CVP) method is used to construct the parametric model of the guidance command. The actor-critic-based reinforcement learning method is used to solve the guidance command in real time, and the guidance information is effectively transmitted in the adjacent guidance solution cycle. The guidance error converges to within the allowable accuracy range during the cross-cycle iteration. Monte Carlo simulation shows that the proposed method has good adaptability to initial conditions and flight parameter uncertainty, and can guarantee the real-time performance of the guidance command while achieving high-precision guidance.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114136576","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 : 2019-10-01DOI: 10.1109/ICUS48101.2019.8996023
Hao Dong, Shaohang Xu, Da Li, Yuqi Guo, Junqiang Xi
For unmanned ground vehicles, the longitudinal motion control based on desired acceleration, provided by the upper planning module, has static errors. And the commonly used Proportion-Integration (PI) controller tracks the desired speed directly, prone to overshoot and oscillation. In order to overcome these problems, a method based on acceleration replanning is proposed in this paper, considering the dynamic, steady-state and real-time requirements. Simplified nonlinear longitudinal dynamics models are established. Then, 4 parts of the controller are designed based on the models: switching logic based on coast-down; acceleration replanning module by means of backstepping and feedback linearization; throttle adaptive controller and brake controller. Errors of velocity and acceleration can converge to zero quickly meanwhile without overshoot and oscillation, theoretically. Finally, the MATLAB/ Simulink TruckSim co-simulation shows that the designed controller performs better than the PI controller, with speed’s average error reducing by 52%. Besides, the designed controller controls the pedals more smoothly, for it makes full use of the powertrain.
{"title":"A Longitudinal Motion Control Method for Unmanned Truck Based on Acceleration Replanning","authors":"Hao Dong, Shaohang Xu, Da Li, Yuqi Guo, Junqiang Xi","doi":"10.1109/ICUS48101.2019.8996023","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8996023","url":null,"abstract":"For unmanned ground vehicles, the longitudinal motion control based on desired acceleration, provided by the upper planning module, has static errors. And the commonly used Proportion-Integration (PI) controller tracks the desired speed directly, prone to overshoot and oscillation. In order to overcome these problems, a method based on acceleration replanning is proposed in this paper, considering the dynamic, steady-state and real-time requirements. Simplified nonlinear longitudinal dynamics models are established. Then, 4 parts of the controller are designed based on the models: switching logic based on coast-down; acceleration replanning module by means of backstepping and feedback linearization; throttle adaptive controller and brake controller. Errors of velocity and acceleration can converge to zero quickly meanwhile without overshoot and oscillation, theoretically. Finally, the MATLAB/ Simulink TruckSim co-simulation shows that the designed controller performs better than the PI controller, with speed’s average error reducing by 52%. Besides, the designed controller controls the pedals more smoothly, for it makes full use of the powertrain.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122041250","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 : 2019-10-01DOI: 10.1109/ICUS48101.2019.8996073
Xiaoxiao Xie, Yan Ding, Xinliang Huang
Landmark detection and recognition algorithm is a very important technology for vision-based Unmanned Aerial Vehicles (UAVs) autonomous pitching. The deformation and rotation of landmarks and the background distraction will be the challenges for detection and recognition. Based on Support Vector Machine (SVM) and the appearance features of landmarks, a landmark detection and recognition algorithm is proposed in this paper. The algorithm presents a landmark detection scheme based on ellipse detection which forms ellipses by optimized arcs and estimates parameters in a decomposed space using Hough transform. To get better edge features, a segmentation is designed to reduce the background noise. Due to the lack of direction information of landmarks in detection procedure, a SVM classifier with a multi-direction voting mechanism is presented for recognition. We expand the training sample set through the affine transformation and make a vote on classification results from multiple directions to achieve accurate landmark recognition. Experimental results show that our landmark detection and recognition algorithm is effective on the UAV platform and the adaptability to the environment is strong.
{"title":"A Landmark Detection and Recognition Algorithm for UAV Autonomous Pitching","authors":"Xiaoxiao Xie, Yan Ding, Xinliang Huang","doi":"10.1109/ICUS48101.2019.8996073","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8996073","url":null,"abstract":"Landmark detection and recognition algorithm is a very important technology for vision-based Unmanned Aerial Vehicles (UAVs) autonomous pitching. The deformation and rotation of landmarks and the background distraction will be the challenges for detection and recognition. Based on Support Vector Machine (SVM) and the appearance features of landmarks, a landmark detection and recognition algorithm is proposed in this paper. The algorithm presents a landmark detection scheme based on ellipse detection which forms ellipses by optimized arcs and estimates parameters in a decomposed space using Hough transform. To get better edge features, a segmentation is designed to reduce the background noise. Due to the lack of direction information of landmarks in detection procedure, a SVM classifier with a multi-direction voting mechanism is presented for recognition. We expand the training sample set through the affine transformation and make a vote on classification results from multiple directions to achieve accurate landmark recognition. Experimental results show that our landmark detection and recognition algorithm is effective on the UAV platform and the adaptability to the environment is strong.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129875901","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 : 2019-10-01DOI: 10.1109/ICUS48101.2019.8996010
Yuxiao Wang, Yaochen Li, Ming Zeng, Zikun Dong, Jian Yuan, Ziwei Wang
In this paper, we propose a bottom-up approach to estimate the geometric layout of indoor images using latent variables. By utilizing latent variables to model subregions, the estimation accuracy of scene layout is implicitly improved. The proposed method consists of three sub-tasks: feature extraction, subregion classification and geometric layout classification. Firstly, the location features are extracted to roughly estimate the basic indoor structure. The influence of illumination, rich color, and foreground occlusion can be eliminated. Secondly, N-slack SSVM is applied to efficiently classify the location features extracted in the previous step. Finally, the bag-of-words model is combined with cosine similarity and information divergence filtering to improve the fault tolerance of the geometric layout classification task. The classification accuracy can reach 0.982, which well demonstrate the effectiveness of the proposed approach.
{"title":"Bottom-up Estimation of Geometric Layout for Indoor Images","authors":"Yuxiao Wang, Yaochen Li, Ming Zeng, Zikun Dong, Jian Yuan, Ziwei Wang","doi":"10.1109/ICUS48101.2019.8996010","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8996010","url":null,"abstract":"In this paper, we propose a bottom-up approach to estimate the geometric layout of indoor images using latent variables. By utilizing latent variables to model subregions, the estimation accuracy of scene layout is implicitly improved. The proposed method consists of three sub-tasks: feature extraction, subregion classification and geometric layout classification. Firstly, the location features are extracted to roughly estimate the basic indoor structure. The influence of illumination, rich color, and foreground occlusion can be eliminated. Secondly, N-slack SSVM is applied to efficiently classify the location features extracted in the previous step. Finally, the bag-of-words model is combined with cosine similarity and information divergence filtering to improve the fault tolerance of the geometric layout classification task. The classification accuracy can reach 0.982, which well demonstrate the effectiveness of the proposed approach.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126664836","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 : 2019-10-01DOI: 10.1109/ICUS48101.2019.8995997
Z. Yi, Fang Guowei, Yang Xiu-xia, Cao Weiyi, Yan Xuan
In order to improve the penetration and strike capability of unmanned aerial vehicles (UAVs), a cooperative guidance law is presented for multiple UAVs attacks on the same maneuvering target. Firstly, based on the finite-time consistency convergence theory, a multiple UAVs time cooperative guidance law is designed, the guidance law is not bound by the end time, which enhances the anti-interference ability during the guidance period. Secondly, based on the sliding mode variable structure control theory, the guidance law of multiple UAVs attack angle constraint is designed; the guidance law is optimized according to the quasi- sliding mode control, which weakens the chattering of the sliding mode. Finally, the simulation verifies the effectiveness of the designed guidance law.
{"title":"Time-Cooperative Guidance Law for Multiple UAVs with Angle Constraints","authors":"Z. Yi, Fang Guowei, Yang Xiu-xia, Cao Weiyi, Yan Xuan","doi":"10.1109/ICUS48101.2019.8995997","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8995997","url":null,"abstract":"In order to improve the penetration and strike capability of unmanned aerial vehicles (UAVs), a cooperative guidance law is presented for multiple UAVs attacks on the same maneuvering target. Firstly, based on the finite-time consistency convergence theory, a multiple UAVs time cooperative guidance law is designed, the guidance law is not bound by the end time, which enhances the anti-interference ability during the guidance period. Secondly, based on the sliding mode variable structure control theory, the guidance law of multiple UAVs attack angle constraint is designed; the guidance law is optimized according to the quasi- sliding mode control, which weakens the chattering of the sliding mode. Finally, the simulation verifies the effectiveness of the designed guidance law.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129179922","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 : 2019-10-01DOI: 10.1109/ICUS48101.2019.8996060
Jiaxin Zhao, Weiping Zhang, Chenyang Wang, Zou Yang, Jiahao Wang
The insect-inspired Flapping Wing Micro Air Vehicles(FWMAV) has always been a one of the research focuses and difficulties in the field of bionic unmanned micro-systems. Because the FWMAV’s small size, high concealment, flexibility, which has a broad application prospect. In this paper, the development process and critical technologies of insect-inspired FWMAV were introduced and summarized according to the driving methods that commonly used in insect-inspired FWMAV: piezoelectric drive, motor drive, electromagnetic drive, and others.
{"title":"Current Status of insect-inspired Flapping Wing Micro Air Vehicles","authors":"Jiaxin Zhao, Weiping Zhang, Chenyang Wang, Zou Yang, Jiahao Wang","doi":"10.1109/ICUS48101.2019.8996060","DOIUrl":"https://doi.org/10.1109/ICUS48101.2019.8996060","url":null,"abstract":"The insect-inspired Flapping Wing Micro Air Vehicles(FWMAV) has always been a one of the research focuses and difficulties in the field of bionic unmanned micro-systems. Because the FWMAV’s small size, high concealment, flexibility, which has a broad application prospect. In this paper, the development process and critical technologies of insect-inspired FWMAV were introduced and summarized according to the driving methods that commonly used in insect-inspired FWMAV: piezoelectric drive, motor drive, electromagnetic drive, and others.","PeriodicalId":344181,"journal":{"name":"2019 IEEE International Conference on Unmanned Systems (ICUS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124134036","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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