Accurate motion prediction of free-tumbling satellites is crucial for the success of capture operations.This paper proposes a two-step method to estimate the motion states and parameters of such satellites,thereby ena...Accurate motion prediction of free-tumbling satellites is crucial for the success of capture operations.This paper proposes a two-step method to estimate the motion states and parameters of such satellites,thereby enabling precise long-term motion prediction.This paper begins with a measurement of the system's degree of observability,quantified through the Empirical Observability Gramian(EOG).Based on this measurement,a batch processing algorithm is first employed to estimate the satellite's constant parameters offline.Subsequently,an online filtering algorithm,utilizing a minimal state set,fine-tunes these parameters and estimates the motion states in real time.This integrated approach significantly enhances both convergence properties and estimation accuracy,particularly for systems with poor observability.Utilizing the predicted long-term motion of the satellite,a composite evaluation metric is formulated to identify the optimal capture point and moment.The base pose of the space robot is then adjusted to ensure that the optimal capture point lies within the manipulator's dexterous workspace,which is determined through a pre-constructed capability map.The effectiveness of the proposed method is demonstrated through both simulation and experimental results.展开更多
Mapping grasps from human to anthropomorphic robotic hands is an open issue in research,because the master hand and the slave hand have dissimilar kinematics.This paper proposes a hybrid mapping method to solve this p...Mapping grasps from human to anthropomorphic robotic hands is an open issue in research,because the master hand and the slave hand have dissimilar kinematics.This paper proposes a hybrid mapping method to solve this problem.In the proposed method,fingers in the master and the slave hands are divided into vital and synergic fingers according to their contribution to the grasping task.The tip of the vital finger of the master hand is first mapped to that of the slave hand while ensuring that both are in simultaneous contact with the object to be grasped.Following postural synergy theory,joints of the other synergic fingers of the slave hand are then used to generate an anthropomorphic grasping configuration according to the shape of the object to be grasped.Following this,a human-guided impedance controller is used to reduce the pre-grasping error and realize compliant interaction with the environment.The proposed hybrid mapping method can not only generate the posture of the humanoid envelope but can also carry out impedance-adaptive matching.It was evaluated using simulations and an experiment involving an anthropomorphic robotic slave hand.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62403171,T2388101)。
文摘Accurate motion prediction of free-tumbling satellites is crucial for the success of capture operations.This paper proposes a two-step method to estimate the motion states and parameters of such satellites,thereby enabling precise long-term motion prediction.This paper begins with a measurement of the system's degree of observability,quantified through the Empirical Observability Gramian(EOG).Based on this measurement,a batch processing algorithm is first employed to estimate the satellite's constant parameters offline.Subsequently,an online filtering algorithm,utilizing a minimal state set,fine-tunes these parameters and estimates the motion states in real time.This integrated approach significantly enhances both convergence properties and estimation accuracy,particularly for systems with poor observability.Utilizing the predicted long-term motion of the satellite,a composite evaluation metric is formulated to identify the optimal capture point and moment.The base pose of the space robot is then adjusted to ensure that the optimal capture point lies within the manipulator's dexterous workspace,which is determined through a pre-constructed capability map.The effectiveness of the proposed method is demonstrated through both simulation and experimental results.
基金supported in part by the China National Key Research and Development Program under Grant no.2020YFC2007801in part by the National Natural Science Foundation of China under Grant no.U1813209.
文摘Mapping grasps from human to anthropomorphic robotic hands is an open issue in research,because the master hand and the slave hand have dissimilar kinematics.This paper proposes a hybrid mapping method to solve this problem.In the proposed method,fingers in the master and the slave hands are divided into vital and synergic fingers according to their contribution to the grasping task.The tip of the vital finger of the master hand is first mapped to that of the slave hand while ensuring that both are in simultaneous contact with the object to be grasped.Following postural synergy theory,joints of the other synergic fingers of the slave hand are then used to generate an anthropomorphic grasping configuration according to the shape of the object to be grasped.Following this,a human-guided impedance controller is used to reduce the pre-grasping error and realize compliant interaction with the environment.The proposed hybrid mapping method can not only generate the posture of the humanoid envelope but can also carry out impedance-adaptive matching.It was evaluated using simulations and an experiment involving an anthropomorphic robotic slave hand.
