Pub Date : 2013-05-06DOI: 10.1109/ICRA.2013.6630670
Hyunsoo Yang, Dongjun Lee
We present a novel cooperative grasping control framework for multiple kinematic nonholonomic mobile manipulators, which enables them to drive the grasped object with velocity commands, while rigidly maintaining the grasping shape with no dedicated grasp-enforcing fixtures and also avoiding obstacles either via their whole formation maneuver or internal formation reconfiguration. For this, nonholonomic passive decomposition [1], [2] is utilized to split the robots' motion into the three aspects (i.e., grasping shape; grasped object maneuver; internal motions) so that we can control these aspects simultaneously and separately. Peculiar dynamics of the internal motions is exploited to achieve obstacle avoidance via the formation reconfiguration. Simulations are performed to support the theory.
{"title":"Cooperative grasping control of multiple mobile manipulators with obstacle avoidance","authors":"Hyunsoo Yang, Dongjun Lee","doi":"10.1109/ICRA.2013.6630670","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6630670","url":null,"abstract":"We present a novel cooperative grasping control framework for multiple kinematic nonholonomic mobile manipulators, which enables them to drive the grasped object with velocity commands, while rigidly maintaining the grasping shape with no dedicated grasp-enforcing fixtures and also avoiding obstacles either via their whole formation maneuver or internal formation reconfiguration. For this, nonholonomic passive decomposition [1], [2] is utilized to split the robots' motion into the three aspects (i.e., grasping shape; grasped object maneuver; internal motions) so that we can control these aspects simultaneously and separately. Peculiar dynamics of the internal motions is exploited to achieve obstacle avoidance via the formation reconfiguration. Simulations are performed to support the theory.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124402384","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 : 2013-05-06DOI: 10.1109/ICRA.2013.6631212
Ning Sun, Yongchun Fang
The present paper exploits a partially saturated nonlinear control law for underactuated crane systems, which is achieved by converting the crane model into an objective (or equivalently, desired closed-loop) system. The proposed method guarantees “soft” trolley start by incorporating a smooth saturated function into the control law. More specifically, we first establish an objective system with guaranteed signal convergence and stability performance; then based on the structure of the objective dynamics, a partially saturated control law is derived straightforwardly by solving one partial differential equation, without performing any partial feedback linearization operations on the original crane model. The convergence and stability performance of the objective (i.e., closed-loop) system is guaranteed with Lyapunov techniques and LaSalle's invariance theorem. To validate the practical performance of the proposed method, we implement hardware experiments to illustrate that the new method achieves superior performance with reduced control efforts.
{"title":"A partially saturated nonlinear controller for overhead cranes with experimental implementation","authors":"Ning Sun, Yongchun Fang","doi":"10.1109/ICRA.2013.6631212","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6631212","url":null,"abstract":"The present paper exploits a partially saturated nonlinear control law for underactuated crane systems, which is achieved by converting the crane model into an objective (or equivalently, desired closed-loop) system. The proposed method guarantees “soft” trolley start by incorporating a smooth saturated function into the control law. More specifically, we first establish an objective system with guaranteed signal convergence and stability performance; then based on the structure of the objective dynamics, a partially saturated control law is derived straightforwardly by solving one partial differential equation, without performing any partial feedback linearization operations on the original crane model. The convergence and stability performance of the objective (i.e., closed-loop) system is guaranteed with Lyapunov techniques and LaSalle's invariance theorem. To validate the practical performance of the proposed method, we implement hardware experiments to illustrate that the new method achieves superior performance with reduced control efforts.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"23 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124576797","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 : 2013-05-06DOI: 10.1109/ICRA.2013.6631380
Geoffrey A. Hollinger, A. Pereira, G. Sukhatme
We discuss the problem of learning uncertainty models of ocean processes to assist in the operation of Autonomous Underwater Vehicles (AUVs) in the ocean. We focus on the prediction of ocean currents, which have significant effect on the navigation of AUVs. Available models provide accurate prediction of ocean currents, but they typically do not provide confidence estimates of these predictions. We propose augmenting existing prediction methods with variance measures based on Gaussian Process (GP) regression. We show that commonly used measures of variance in GPs do not accurately reflect errors in ocean current prediction, and we propose an alternative uncertainty measure based on interpolation variance. We integrate these measures of uncertainty into a probabilistic planner running on an AUV during a field deployment in the Southern California Bight. Our experiments demonstrate that the proposed uncertainty measures improve the safety and reliability of AUVs operating in the coastal ocean.
{"title":"Learning uncertainty models for reliable operation of Autonomous Underwater Vehicles","authors":"Geoffrey A. Hollinger, A. Pereira, G. Sukhatme","doi":"10.1109/ICRA.2013.6631380","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6631380","url":null,"abstract":"We discuss the problem of learning uncertainty models of ocean processes to assist in the operation of Autonomous Underwater Vehicles (AUVs) in the ocean. We focus on the prediction of ocean currents, which have significant effect on the navigation of AUVs. Available models provide accurate prediction of ocean currents, but they typically do not provide confidence estimates of these predictions. We propose augmenting existing prediction methods with variance measures based on Gaussian Process (GP) regression. We show that commonly used measures of variance in GPs do not accurately reflect errors in ocean current prediction, and we propose an alternative uncertainty measure based on interpolation variance. We integrate these measures of uncertainty into a probabilistic planner running on an AUV during a field deployment in the Southern California Bight. Our experiments demonstrate that the proposed uncertainty measures improve the safety and reliability of AUVs operating in the coastal ocean.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114403105","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 : 2013-05-06DOI: 10.1109/ICRA.2013.6630841
S. Klare, A. Peer
Dynamically rendering the shape of an object offers a lot of new opportunities in the fields of virtual reality, design, and prototyping as bare hand interaction represents a very intutive way to explore objects. We propose a 3D-shape interface formed by a parallel kinematics connecting multiple nodes and present an inverse kinematics based on a retargeting algorithm to control these nodes. The shape is formed by simultaneously taking into account loop constraints, joint limits and user interaction points. Two alternative control modes for stiff and compliant objects are introduced and compared with each other in simulations.
{"title":"Inverse kinematics for shape rendering interfaces","authors":"S. Klare, A. Peer","doi":"10.1109/ICRA.2013.6630841","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6630841","url":null,"abstract":"Dynamically rendering the shape of an object offers a lot of new opportunities in the fields of virtual reality, design, and prototyping as bare hand interaction represents a very intutive way to explore objects. We propose a 3D-shape interface formed by a parallel kinematics connecting multiple nodes and present an inverse kinematics based on a retargeting algorithm to control these nodes. The shape is formed by simultaneously taking into account loop constraints, joint limits and user interaction points. Two alternative control modes for stiff and compliant objects are introduced and compared with each other in simulations.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114773723","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 : 2013-05-06DOI: 10.1109/ICRA.2013.6631225
Dylan Hadfield-Menell, L. Kaelbling, Tomas Lozano-Perez
For robots to effectively interact with the real world, they will need to perform complex tasks over long time horizons. This is a daunting challenge, but recent advances using hierarchical planning [1] have been able to provide leverage on this problem. Unfortunately, this approach makes no effort to account for the execution cost of an abstract plan and often arrives at poor quality plans. This paper outlines a method for dynamically improving a hierarchical plan during execution. We frame the underlying question as one of evaluating the resource needs of an abstract operator and propose a general way to approach estimating them. We ran experiments in challenging domains and observed up to 30% reduction in execution cost when compared with a standard hierarchical planner.
{"title":"Optimization in the now: Dynamic peephole optimization for hierarchical planning","authors":"Dylan Hadfield-Menell, L. Kaelbling, Tomas Lozano-Perez","doi":"10.1109/ICRA.2013.6631225","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6631225","url":null,"abstract":"For robots to effectively interact with the real world, they will need to perform complex tasks over long time horizons. This is a daunting challenge, but recent advances using hierarchical planning [1] have been able to provide leverage on this problem. Unfortunately, this approach makes no effort to account for the execution cost of an abstract plan and often arrives at poor quality plans. This paper outlines a method for dynamically improving a hierarchical plan during execution. We frame the underlying question as one of evaluating the resource needs of an abstract operator and propose a general way to approach estimating them. We ran experiments in challenging domains and observed up to 30% reduction in execution cost when compared with a standard hierarchical planner.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114874794","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 : 2013-05-06DOI: 10.1109/ICRA.2013.6630974
A. Orthey, Marc Toussaint, Nikolay Jetchev
Solving complex robot manipulation tasks requires to combine motion generation on the geometric level with planning on a symbolic level. On both levels robotics research has developed a variety of mature methodologies, including geometric motion planning and motion primitive learning on the motor level as well as logic reasoning and relational Reinforcement Learning methods on the symbolic level. However, their robust integration remains a great challenge. In this paper we approach one aspect of this integration by optimizing the motion primitives on the geometric level to be as consistent as possible with their symbolic predictions. The so optimized motion primitives increase the probability of a “successful” motion-meaning that the symbolic prediction was indeed achieved. Conversely, using these optimized motion primitives to collect new data about the effects of actions the learnt symbolic rules becomes more predictive and deterministic.
{"title":"Optimizing motion primitives to make symbolic models more predictive","authors":"A. Orthey, Marc Toussaint, Nikolay Jetchev","doi":"10.1109/ICRA.2013.6630974","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6630974","url":null,"abstract":"Solving complex robot manipulation tasks requires to combine motion generation on the geometric level with planning on a symbolic level. On both levels robotics research has developed a variety of mature methodologies, including geometric motion planning and motion primitive learning on the motor level as well as logic reasoning and relational Reinforcement Learning methods on the symbolic level. However, their robust integration remains a great challenge. In this paper we approach one aspect of this integration by optimizing the motion primitives on the geometric level to be as consistent as possible with their symbolic predictions. The so optimized motion primitives increase the probability of a “successful” motion-meaning that the symbolic prediction was indeed achieved. Conversely, using these optimized motion primitives to collect new data about the effects of actions the learnt symbolic rules becomes more predictive and deterministic.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114519434","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 : 2013-05-06DOI: 10.1109/ICRA.2013.6631287
R. Saegusa, G. Metta, G. Sandini, L. Natale
The paper describes a developmental framework of action-driven perception in anthropomorphic robots. The key idea of the framework is that action develops the agent's perception of the own body and its action. In this framework, a robot voluntarily generates movements, and then develops the ability to perceive its own body and the effects of action primitives. The robot, moreover, demonstrates manipulative actions composed of the learned primitives, and characterizes the actions based on their sensory effects. After learning, the robot can predictively recognize humans' manipulative actions with cross-modal recovery of unavailable sensory information and reproduce the recognized actions. We evaluated the proposed framework in experiments with a real robot. In the experiments, we achieved developmental recognition of human actions as well as their reproduction.
{"title":"Developmental action perception for manipulative interaction","authors":"R. Saegusa, G. Metta, G. Sandini, L. Natale","doi":"10.1109/ICRA.2013.6631287","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6631287","url":null,"abstract":"The paper describes a developmental framework of action-driven perception in anthropomorphic robots. The key idea of the framework is that action develops the agent's perception of the own body and its action. In this framework, a robot voluntarily generates movements, and then develops the ability to perceive its own body and the effects of action primitives. The robot, moreover, demonstrates manipulative actions composed of the learned primitives, and characterizes the actions based on their sensory effects. After learning, the robot can predictively recognize humans' manipulative actions with cross-modal recovery of unavailable sensory information and reproduce the recognized actions. We evaluated the proposed framework in experiments with a real robot. In the experiments, we achieved developmental recognition of human actions as well as their reproduction.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115082579","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 : 2013-05-06DOI: 10.1109/ICRA.2013.6631375
M. Tanner, J. Burdick, I. Nesnas
Several potentially important science targets have been observed in extreme terrains (steep or vertical slopes, possibly covered in loose soil or granular media) on other planets. Robots which can access these extreme terrains will likely use tethers to provide climbing and stabilizing force. To prevent tether entanglement during descent and subsequent ascent through such terrain, a motion planning procedure is needed. Abad-Manterola, Nesnas, and Burdick [1] previously presented such a motion planner for the case in which the geometry of the terrain is known a priori with high precision. Their algorithm finds ascent/descent paths of fixed homotopy, which minimizes the likelihood of tether entanglement. This paper presents an extension of the algorithm to the case where the terrain is poorly known prior to the start of the descent. In particular, we develop new results for how the discovery of previously unknown obstacles modifies the homotopy classes underlying the motion planning problem. We also present a planning algorithm which takes the modified homotopy into account. An example illustrates the methodology.
{"title":"Online motion planning for tethered robots in extreme terrain","authors":"M. Tanner, J. Burdick, I. Nesnas","doi":"10.1109/ICRA.2013.6631375","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6631375","url":null,"abstract":"Several potentially important science targets have been observed in extreme terrains (steep or vertical slopes, possibly covered in loose soil or granular media) on other planets. Robots which can access these extreme terrains will likely use tethers to provide climbing and stabilizing force. To prevent tether entanglement during descent and subsequent ascent through such terrain, a motion planning procedure is needed. Abad-Manterola, Nesnas, and Burdick [1] previously presented such a motion planner for the case in which the geometry of the terrain is known a priori with high precision. Their algorithm finds ascent/descent paths of fixed homotopy, which minimizes the likelihood of tether entanglement. This paper presents an extension of the algorithm to the case where the terrain is poorly known prior to the start of the descent. In particular, we develop new results for how the discovery of previously unknown obstacles modifies the homotopy classes underlying the motion planning problem. We also present a planning algorithm which takes the modified homotopy into account. An example illustrates the methodology.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116892894","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 : 2013-05-06DOI: 10.1109/ICRA.2013.6630770
K. Nakajima, H. Hauser, Rongjie Kang, E. Guglielmino, D. Caldwell, R. Pfeifer
Octopus arms, as well as elephant trunks, squid tentacles, and vertebrate tongues are termed muscular-hydrostats. In such structures, the volume of the organ remains constant during their motions, enabling diverse, complex, and highly controlled movements without the support of a skeleton. Such flexible structures show major advantages over articulated arms that have a rigid skeleton and joints. These advantages have been attracting roboticists aiming to apply these material properties to soft robot controls. In this paper, we show that the muscular-hydrostat system itself has the computational capacity to achieve a complex nonlinear computation. By using a 3D dynamic simulator of the system inspired by the octopus, we actually demonstrate that the system is capable of emulating complex nonlinear dynamical systems by exploiting its elastic body dynamics as a computational resource. In addition, we systematically analyze its computational power in terms of memory capacity, and show that the system has an intrinsic and characteristic short term memory profile. Finally, the implications for soft robot control and future application scenarios are discussed.
{"title":"Computing with a muscular-hydrostat system","authors":"K. Nakajima, H. Hauser, Rongjie Kang, E. Guglielmino, D. Caldwell, R. Pfeifer","doi":"10.1109/ICRA.2013.6630770","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6630770","url":null,"abstract":"Octopus arms, as well as elephant trunks, squid tentacles, and vertebrate tongues are termed muscular-hydrostats. In such structures, the volume of the organ remains constant during their motions, enabling diverse, complex, and highly controlled movements without the support of a skeleton. Such flexible structures show major advantages over articulated arms that have a rigid skeleton and joints. These advantages have been attracting roboticists aiming to apply these material properties to soft robot controls. In this paper, we show that the muscular-hydrostat system itself has the computational capacity to achieve a complex nonlinear computation. By using a 3D dynamic simulator of the system inspired by the octopus, we actually demonstrate that the system is capable of emulating complex nonlinear dynamical systems by exploiting its elastic body dynamics as a computational resource. In addition, we systematically analyze its computational power in terms of memory capacity, and show that the system has an intrinsic and characteristic short term memory profile. Finally, the implications for soft robot control and future application scenarios are discussed.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117000332","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 : 2013-05-06DOI: 10.1109/ICRA.2013.6631237
Nicolás Rojas, F. Thomas
A double banana is defined as the bar-and-joint assembly of two bipyramids joined by their apexes. Clearly, the bar lengths of this kind of assembly are not independent as we cannot assign arbitrary values to them. This dependency can be algebraically expressed as a closure condition fully expressed in terms of bar lengths. This paper is devoted to its derivation and to show how its use simplifies the position analysis of many well-known serial and parallel robots thus providing a unifying treatment to apparently disparate problems. This approach permits deriving the univariate polynomials, needed for the closed-form solution of these position analysis problems, without relying on trigonometric substitutions or difficult variable eliminations.
{"title":"The closure condition of the double banana and its application to robot position analysis","authors":"Nicolás Rojas, F. Thomas","doi":"10.1109/ICRA.2013.6631237","DOIUrl":"https://doi.org/10.1109/ICRA.2013.6631237","url":null,"abstract":"A double banana is defined as the bar-and-joint assembly of two bipyramids joined by their apexes. Clearly, the bar lengths of this kind of assembly are not independent as we cannot assign arbitrary values to them. This dependency can be algebraically expressed as a closure condition fully expressed in terms of bar lengths. This paper is devoted to its derivation and to show how its use simplifies the position analysis of many well-known serial and parallel robots thus providing a unifying treatment to apparently disparate problems. This approach permits deriving the univariate polynomials, needed for the closed-form solution of these position analysis problems, without relying on trigonometric substitutions or difficult variable eliminations.","PeriodicalId":259746,"journal":{"name":"2013 IEEE International Conference on Robotics and Automation","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117230205","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：