The rotational motion of a tumbling target brings great challenges to space robot on successfully capturing the tumbling target.Therefore,it is necessary to reduce the target's rotation to a rate at which capture ...The rotational motion of a tumbling target brings great challenges to space robot on successfully capturing the tumbling target.Therefore,it is necessary to reduce the target's rotation to a rate at which capture can be accomplished by the space robot.In this paper,a detumbling strategy based on friction control of dual-arm space robot for capturing tumbling target is proposed.This strategy can reduce the target's rotational velocity while maintaining base attitude stability through the establishment of the rotation attenuation controller and base attitude adjustment controller.The rotation attenuation controller adopts the multi-space hybrid impedance control method to control the friction precisely.The base attitude adjustment controller applies the dual-arm extended Jacobian matrix to stabilize the base attitude.The main contributions of this paper are as follows:(1)The compliant control method is adopted to achieve a precise friction control,which can reduce the target angular velocity steadily;(2)The dual-arm extended Jacobian matrix is applied to stabilize the base attitude without affecting the target capture task;(3)The detumbling strategy of dualarm space robot is designed considering base attitude stabilization,realizing coordinated planning of the base attitude and the arms.The strategy is verified by a dual-arm space robot with two 7-DOF(degrees of freedom)arms.Simulation results show that,target with a rotation velocity of 20(°)/s can be effectively controlled to stop within 30 s,and the final deflection of the base attitude is less than 0.15°without affecting the target capture task,verifying the correctness and effectiveness of the strategy.Except to the tumbling target capture task,the control strategy can also be applied to other typical on-orbit operation tasks such as space debris removal and spacecraft maintenance.展开更多
Addressing the challenges in detecting surface floating litter in artificial lakes,including complex environments,uneven illumination,and susceptibility to noise andweather,this paper proposes an efficient and lightwe...Addressing the challenges in detecting surface floating litter in artificial lakes,including complex environments,uneven illumination,and susceptibility to noise andweather,this paper proposes an efficient and lightweight Ghost-YOLO(You Only Look Once)v8 algorithm.The algorithmintegrates advanced attention mechanisms and a smalltarget detection head to significantly enhance detection performance and efficiency.Firstly,an SE(Squeeze-and-Excitation)mechanism is incorporated into the backbone network to fortify the extraction of resilient features and precise target localization.This mechanism models feature channel dependencies,enabling adaptive adjustment of channel importance,thereby improving recognition of floating litter targets.Secondly,a 160×160 small-target detection layer is designed in the feature fusion neck to mitigate semantic information loss due to varying target scales.This design enhances the fusion of deep and shallow semantic information,improving small target feature representation and enabling better capture and identification of tiny floating litter.Thirdly,to balance performance and efficiency,the GhostConv module replaces part of the conventional convolutions in the feature fusion neck.Additionally,a novel C2fGhost(CSPDarknet53 to 2-Stage Feature Pyramid Networks Ghost)module is introduced to further reduce network parameters.Lastly,to address the challenge of occlusion,a newloss function,WIoU(Wise Intersection over Union)v3 incorporating a flexible and non-monotonic concentration approach,is adopted to improve detection rates for surface floating litter.The outcomes of the experiments demonstrate that the Ghost-YOLO v8 model proposed in this paper performs well in the dataset Marine,significantly enhances precision and recall by 3.3 and 7.6 percentage points,respectively,in contrast with the base model,mAP@0.5 and mAP 0.5:0.95 improve by 5.3 and 4.4 percentage points and reduces the computational volume by 1.88MB,the FPS value hardly decreases,and the efficient real-time identification of 展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.61403038 and 61573066)the Open Research Fund of Key Laboratory of Space Utilization,Chinese Academy of Sciences(Nos.LSU-2016-05-2 and LSUKJTS-2017-02)。
文摘The rotational motion of a tumbling target brings great challenges to space robot on successfully capturing the tumbling target.Therefore,it is necessary to reduce the target's rotation to a rate at which capture can be accomplished by the space robot.In this paper,a detumbling strategy based on friction control of dual-arm space robot for capturing tumbling target is proposed.This strategy can reduce the target's rotational velocity while maintaining base attitude stability through the establishment of the rotation attenuation controller and base attitude adjustment controller.The rotation attenuation controller adopts the multi-space hybrid impedance control method to control the friction precisely.The base attitude adjustment controller applies the dual-arm extended Jacobian matrix to stabilize the base attitude.The main contributions of this paper are as follows:(1)The compliant control method is adopted to achieve a precise friction control,which can reduce the target angular velocity steadily;(2)The dual-arm extended Jacobian matrix is applied to stabilize the base attitude without affecting the target capture task;(3)The detumbling strategy of dualarm space robot is designed considering base attitude stabilization,realizing coordinated planning of the base attitude and the arms.The strategy is verified by a dual-arm space robot with two 7-DOF(degrees of freedom)arms.Simulation results show that,target with a rotation velocity of 20(°)/s can be effectively controlled to stop within 30 s,and the final deflection of the base attitude is less than 0.15°without affecting the target capture task,verifying the correctness and effectiveness of the strategy.Except to the tumbling target capture task,the control strategy can also be applied to other typical on-orbit operation tasks such as space debris removal and spacecraft maintenance.
基金Supported by the fund of the Henan Province Science and Technology Research Project(No.242102210213).
文摘Addressing the challenges in detecting surface floating litter in artificial lakes,including complex environments,uneven illumination,and susceptibility to noise andweather,this paper proposes an efficient and lightweight Ghost-YOLO(You Only Look Once)v8 algorithm.The algorithmintegrates advanced attention mechanisms and a smalltarget detection head to significantly enhance detection performance and efficiency.Firstly,an SE(Squeeze-and-Excitation)mechanism is incorporated into the backbone network to fortify the extraction of resilient features and precise target localization.This mechanism models feature channel dependencies,enabling adaptive adjustment of channel importance,thereby improving recognition of floating litter targets.Secondly,a 160×160 small-target detection layer is designed in the feature fusion neck to mitigate semantic information loss due to varying target scales.This design enhances the fusion of deep and shallow semantic information,improving small target feature representation and enabling better capture and identification of tiny floating litter.Thirdly,to balance performance and efficiency,the GhostConv module replaces part of the conventional convolutions in the feature fusion neck.Additionally,a novel C2fGhost(CSPDarknet53 to 2-Stage Feature Pyramid Networks Ghost)module is introduced to further reduce network parameters.Lastly,to address the challenge of occlusion,a newloss function,WIoU(Wise Intersection over Union)v3 incorporating a flexible and non-monotonic concentration approach,is adopted to improve detection rates for surface floating litter.The outcomes of the experiments demonstrate that the Ghost-YOLO v8 model proposed in this paper performs well in the dataset Marine,significantly enhances precision and recall by 3.3 and 7.6 percentage points,respectively,in contrast with the base model,mAP@0.5 and mAP 0.5:0.95 improve by 5.3 and 4.4 percentage points and reduces the computational volume by 1.88MB,the FPS value hardly decreases,and the efficient real-time identification of
