This paper investigates the six degree-of-freedom(6DOF)relative kinodynamic motion planning problem for spacecraft close approach operations,wherein a controlled chaser spacecraft is required to approach a noncooperat...This paper investigates the six degree-of-freedom(6DOF)relative kinodynamic motion planning problem for spacecraft close approach operations,wherein a controlled chaser spacecraft is required to approach a noncooperative space target at a close range under both dynamic constraints and motion constraints.An enhanced version of the bidirectional rapidlyexploring random tree^(*)(BiRRT^(*))algorithm based on flight zoning(FZ-BiRRT^(*))is proposed to generate safe,feasible,and nearoptimal relative motion trajectories.In the proposed algorithm,the space surrounding the space target is zoned in a spherical coordinate system based on the collision probability so that specific designs can be made for different phases of the approaching.Subsequently,based on the flight zone,dynamic constraints,and experiential knowledge,a series of modifications are made to the classic BiRRT^(*)algorithm,and a postprocessing step is designed to accelerate convergence and promote search efficiency.Furthermore,a general regression neural network is introduced to fit a smooth and applicable final motion trajectory.Finally,the feasibility of the generated motion trajectory and the superiority of the proposed algorithm is demonstrated by means of numerical simulations.展开更多
We investigate the close-range relative motion and control of a spacecraft approaching a tumbling target. Unlike the traditional rigid-body dynamics with translation and rotation about the center of mass（CM）, the ki...We investigate the close-range relative motion and control of a spacecraft approaching a tumbling target. Unlike the traditional rigid-body dynamics with translation and rotation about the center of mass（CM）, the kinematic coupling between translation and rotation is taken into consideration to directly describe the motion of the spacecraft＇s sensors or devices which are not coincident with the CM. Thus, a kinematically coupled 6 degrees-of-freedom（DOF） relative motion model for the instrument（feature point） is set up. To make the chaser spacecraft＇s feature point track the target＇s, an optimal tracking problem is defined and a control law with a feedback-feedforward structure is designed. With quasi-linearization of the nonlinear dynamical system, the feedforward term is computed from a specified constraint about the dynamical system and the reference model, and the feedback action is derived starting from the state-dependent Ricca equation（SDRE）. The proposed controller is compared with an existing suboptimal tracking controller, and numerical simulations are presented to illustrate the effectiveness and superiority of the proposed method.展开更多
The Unmanned Surface Vehicle(USV)navigation system needs an accurate,firm,and reliable performance to avoid obstacles,as well as carry out automatic movements during missions.The Global Positioning System(GPS)is often...The Unmanned Surface Vehicle(USV)navigation system needs an accurate,firm,and reliable performance to avoid obstacles,as well as carry out automatic movements during missions.The Global Positioning System(GPS)is often used in these systems to provide absolute position information.However,the GPS measurements are affected by external conditions such as atmospheric bias and multipath effects.This leads to the inability of the stand-alone GPS to provide accurate positioning for the USV systems.One of the solutions to correct the errors of this sensor is by conducting GPS and Inertial Measurement Unit(IMU)fusion.The IMU sensor is complementary to the GPS and not affected by external conditions.However,it accumulates noise as time elapses.Therefore,this study aims to determine the fusion of the GPS and IMU sensors for the i-Boat navigation system,which is a USV developed by Institut Teknologi Sepuluh Nopember(ITS)Surabaya.Using the Unscented Kalman filter(UKF),sensor fusion was carried out based on the state equation defined by the dynamic and kinematic mathematical model of ship motion in 6 degrees of freedom.Then the performance of this model was tested through several simulations using different combinations of attitude measurement data.Two scenarios were conducted in the simulations:attitude measurement inclusion and exclusion(Scenarios I and II,respectively).The results showed that the position estimation in Scenario II was better than in Scenario I,with the Root Mean Square Error(RMSE)value of 0.062 m.Further simulations showed that the presence of attitude measurement data caused a decrease in the fusion accuracy.The UKF simulation with eight measurement parameters(Scenarios A,B and C)and seven measurement parameters(Scenarios D,E and F),as well as analytical attitude movement,indicated that yaw data had the largest noise accumulation compared to roll and pitch.展开更多
基金supported by the Science Center Program of National Natural Science Foundation of China(62188101)the National Key Research and Development Program of China(2024YFF0504702)the SiYuan Collaborative Innovation Alliance of Artificial Intelligence Science(HTKJ2023SY502003).
文摘This paper investigates the six degree-of-freedom(6DOF)relative kinodynamic motion planning problem for spacecraft close approach operations,wherein a controlled chaser spacecraft is required to approach a noncooperative space target at a close range under both dynamic constraints and motion constraints.An enhanced version of the bidirectional rapidlyexploring random tree^(*)(BiRRT^(*))algorithm based on flight zoning(FZ-BiRRT^(*))is proposed to generate safe,feasible,and nearoptimal relative motion trajectories.In the proposed algorithm,the space surrounding the space target is zoned in a spherical coordinate system based on the collision probability so that specific designs can be made for different phases of the approaching.Subsequently,based on the flight zone,dynamic constraints,and experiential knowledge,a series of modifications are made to the classic BiRRT^(*)algorithm,and a postprocessing step is designed to accelerate convergence and promote search efficiency.Furthermore,a general regression neural network is introduced to fit a smooth and applicable final motion trajectory.Finally,the feasibility of the generated motion trajectory and the superiority of the proposed algorithm is demonstrated by means of numerical simulations.
基金Project supported by the Major Program of the National Natural Science Foundation of China(Grant Nos.61690210 and 61690213)
文摘We investigate the close-range relative motion and control of a spacecraft approaching a tumbling target. Unlike the traditional rigid-body dynamics with translation and rotation about the center of mass（CM）, the kinematic coupling between translation and rotation is taken into consideration to directly describe the motion of the spacecraft＇s sensors or devices which are not coincident with the CM. Thus, a kinematically coupled 6 degrees-of-freedom（DOF） relative motion model for the instrument（feature point） is set up. To make the chaser spacecraft＇s feature point track the target＇s, an optimal tracking problem is defined and a control law with a feedback-feedforward structure is designed. With quasi-linearization of the nonlinear dynamical system, the feedforward term is computed from a specified constraint about the dynamical system and the reference model, and the feedback action is derived starting from the state-dependent Ricca equation（SDRE）. The proposed controller is compared with an existing suboptimal tracking controller, and numerical simulations are presented to illustrate the effectiveness and superiority of the proposed method.
基金the i-Boat ITS TeamDRPM ITS IndonesiaWorld-Class Professor Program (Ministry of Higher Education, Research, and Technology, Indonesia) for the data and financial support of this study
文摘The Unmanned Surface Vehicle(USV)navigation system needs an accurate,firm,and reliable performance to avoid obstacles,as well as carry out automatic movements during missions.The Global Positioning System(GPS)is often used in these systems to provide absolute position information.However,the GPS measurements are affected by external conditions such as atmospheric bias and multipath effects.This leads to the inability of the stand-alone GPS to provide accurate positioning for the USV systems.One of the solutions to correct the errors of this sensor is by conducting GPS and Inertial Measurement Unit(IMU)fusion.The IMU sensor is complementary to the GPS and not affected by external conditions.However,it accumulates noise as time elapses.Therefore,this study aims to determine the fusion of the GPS and IMU sensors for the i-Boat navigation system,which is a USV developed by Institut Teknologi Sepuluh Nopember(ITS)Surabaya.Using the Unscented Kalman filter(UKF),sensor fusion was carried out based on the state equation defined by the dynamic and kinematic mathematical model of ship motion in 6 degrees of freedom.Then the performance of this model was tested through several simulations using different combinations of attitude measurement data.Two scenarios were conducted in the simulations:attitude measurement inclusion and exclusion(Scenarios I and II,respectively).The results showed that the position estimation in Scenario II was better than in Scenario I,with the Root Mean Square Error(RMSE)value of 0.062 m.Further simulations showed that the presence of attitude measurement data caused a decrease in the fusion accuracy.The UKF simulation with eight measurement parameters(Scenarios A,B and C)and seven measurement parameters(Scenarios D,E and F),as well as analytical attitude movement,indicated that yaw data had the largest noise accumulation compared to roll and pitch.
