Consensus SE(3)-Constrained Extended Kalman Filter for Close Proximity Orbital Relative Pose Estimation

Precise relative GPS navigation is essential technology for rendezvous and formation flying of spacecraft in Low Earth Orbit. Key design issues of precise relative GPS navigation software are studied and a novel formulation is proposed for the mission which requires high accuracy when the separation distance is up to several km. The navigation filter estimates float navigation solutions by extended Kalman filter with elaborate dynamics models, and resolves ambiguities of integer carrier phase biases to achieve high accuracy fix navigation solutions. A relative navigation software with the proposed formulation is implemented and evaluated in two different ways. One is a test using spacebourne GPS receivers and a GPS signal simulator to evaluate the performance and sensitivity of the software against variation of parameters. The other is a test using actual telemetry data from Gravity Recovery and Climate Experiment (GRACE) to demonstrate the software performance. The design, implementation, and results of the evaluation are presented and discussed on this paper.

This chapter studies the spacecraft rendezvous problem and the spacecraft formation flying problem by using the TPF approaches. For the spacecraft rendezvous problem, the target spacecraft can be in arbitrary eccentric orbits, and for the spacecraft formation flying problem, the reference spacecraft is assumed to be in a circular orbit. By clarifying that the T-H equation and C-W equation are both ANCBC and NCE, the TPF approach for stabilization and consensus of linear systems with input and output delays built in the previous chapters are extended to solve the considered problems. Some simulations are carried out to validate the effectiveness of the proposed approaches.

Spacecraft relative navigation with an omnidirectional vision sensor

: This paper describes the first steps for the setting up and exploitation of the PLATFORM test bench. PLATFORM test bench is intended for the ground testing and validation of GNC technologies and sensors for scenarios including synchronized flight of two or more spacecraft (Formation Flying and Rendez-vous and Docking) and it can be eventually extended to cope with the requirements for robotic applications (f.i. cooperative robotic operations on the Mars surface). PLATFORM test bench includes the particularity, with respect to other ground test benches, of allowing the use of real sensor measurements (including most of the error sources present in a space scenario, as transmission delays) obtained through the recreation of a real dynamic profile of spacecraft mock-ups by using an accurate numerically controlled robotic arm. This project has been supported by the Spanish National Space Program.

The purpose of this paper is to propose the application of an extended Kalman filter (EKF) for the sensors fusion task within the problem of aerial refueling for unmanned aerial vehicles (UAVs). Specifically, the EKF is used to combine the position data from a global positioning system (GPS) and a machine vision (MV)-based system for providing a reliable estimation of the tanker-UAV relative position throughout the docking and the refueling phase. The performance of the scheme has been evaluated using a virtual environment specifically developed for the study of the UAV aerial refueling problem. Particularly, the EKF-based sensor fusion scheme integrates GPS data with MV-based estimates of the tanker-UAV position derived through a combination of feature extraction, feature classification, and pose estimation algorithms. The achieved results indicate that the accuracy of the relative position using GPS or MV estimates can be improved by at least one order of magnitude with the use of EKF in lieu of other sensor fusion techniques.

Robot localization is vital for the operation of an automated guided vehicle (AGV) but is susceptible to problems such as wheel slip. With more sensors fused together, the more environmental information can be collected by the AGV, which helps with the localization of AGV. Inertial measurement unit (IMU) and global positioning unit (GPS) are usually implemented to improve robot localization but are susceptible to noise and are effective outdoors. Indoors, however, are more suitable with light detection and ranging (lidar) device. This paper implements extended Kalman filter (EKF) and unscented Kalman filter (UKF) for robot localization on AGV. AGV localization was tested with EKF and UKF on three different test tracks with different turn conditions. The performance of the EKF and UKF was compared to each other. Different sensors were implemented along with sensor fusion. UKF generates better odometry estimation than EKF with 24.07% better accuracy. With the usage of lidar, wheel slip was compensated.KeywordsAutomated guided vehicleAGVRobot localizationSensor fusion

This paper presents a hardware-in-the-loop (HITL) simulation approach for multiple spacecraft formation flying. Considering a leader-follower formation flying configuration, a Fuzzy Logic controller is developed first to maintain the desired formation shape under external perturbations and the initial position offsets. Cold-gas on/off thrusters are developed to be introduced to the simulation loop, and the HITL simulations are conducted to validate the effectiveness of the proposed simulation configuration and Fuzzy Logic control.

The prescribed performance robust control method for the leader/follower (L/F) formation is proposed in this paper to solve the problem of spacecraft formation flying (SFF) full-process control (FPC). The objective of FPC is to establish an ultra-close formation with the constraint of collision avoidance between two spacecraft, and then to maintain the formation configuration with high-precision accuracy in a period of time. The main contribution of this paper lies in the following three aspects. Firstly, the six-degree-of-freedom (DOF) error dynamics model of SFF is developed to describe the synchronization motion of the L/F system. Secondly, the prescribed performance bound that comprehensively considers transience and transient performance is designed, which is key for the realization of collision avoidance and high-precision accuracy requirements. Finally, combing prescribed performance control and robust control theories, based on the backstepping method, the predefined performance robust controller is designed, and the tracking errors are proven to converge to the predefined performance bounds in the presence of external disturbances by using the predefined performance robust controller. Illustrative simulations are performed to verify the proposed theoretical results.

Combined Kalman and sliding innovation filtering: An adaptive estimation strategy

We present several results of the leader-following paradigm in the formation flying of multiple spacecraft in free space. In this direction, ideas from elementary graph theory and linear matrix inequalities are combined with logic-based switching to shed light on the various control designs which are feasible using the leader-following mechanism for various formation flying scenarios.

As a fundamental prerequisite for a variety of location-based services, indoor location information has received increasing attention in recent years. Under the line-of-sight condition, the positioning accuracy of the indoor positioning technology based on ultrawideband (UWB) is acceptable for many applications, but under the non-line-of-sight condition, it degrades dramatically. The positioning accuracy can be significantly improved by the fusion of inertial measurement units and UWB sensors based on the extended Kalman filter (EKF) algorithm. However, when UWB measurements are affected by large non-Gaussian noise, the assumption of the EKF algorithm that observations are subject to Gaussian distribution for noise is invalid. Although the non-Gaussian noise can be handled by the robust EKF algorithm, this algorithm only uses the prior information to judge the reliability of the observations, and the positioning result is not stable when the number of beacons is small. To solve this problem, a method for successive updating of the covariance and posterior state of the observations in iterations based on an iterated extended Kalman filter (IEKF) is proposed. The marginal distribution of the posterior distribution is constructed and iteratively optimized, inhibiting the effect of non-Gaussian noise on UWB under a complex environment. The positioning results of the proposed method, the standard EKF algorithm, and the robust EKF algorithm, using different numbers of beacons, are compared. The results show that the positioning accuracy of the proposed algorithm is the highest under all scenarios. The proposed algorithm shows the smallest decrease in accuracy and presents the most stable positioning when the number of beacons is small, which is a common situation in practical applications.

Spacecraft formation flying is a fast improving technology for space missions, showcasing reduced cost, increased flexibility and efficiency and design redundancy over similar single satellite missions. Furthermore, it provides a viable solution for missions requiring high spacial and temporal resolutions. While a lot of work has been conducted on spacecraft formation flying control, very few of these algorithms have been validated on flight hardware or through hardware experiments. The L 1 adaptive control algorithm is a promising algorithm that can deal with various model uncertainties in real time. It's capability has been demonstrated through numerous aerospace simulations and also through real world experience on board NASA's AirSTAR aircraft. As such, this work focuses on the design, simulation, implementation and validation of the L 1 adaptive controller for spacecraft formation flying. More specifically, this thesis first introduces the L 1 adaptive control architecture using examples for simple linear systems, to gain an understanding of the controller implementation and behaviour. The desired controller design decisions are discussed I would like to begin by thanking my thesis supervisor, Steve Ulrich, for his support, guidance and motivation. His enthusiasm and mentorship always motivated me to put my best foot forward and I cannot imagine doing this with a better supervisor and mentor.

In this study, Adaptive Network-Based Fuzzy Inference System (ANFIS) is presented with sensor data fusion approach to estimate satellite attitude. The active sensors are sun and earth sensors. Satellite attitude dynamic, including attitude quaternion and angular velocities are estimated simultaneously utilizing the measured values by the sensors. The Extended Kalman Filter (EKF) is employed to verify and evaluate the efficiency of the presented method. Additionally, the neural networks with Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) are also designed to prove the superiority of the proposed ANFIS network among the smart methods of sensor data fusion for satellite attitude estimation. Root Mean Square Error (RMSE) as a numerical criterion and graphical analysis of residues are utilized to evaluate the simulation results. The simulations confirm that the obtained estimations from ANFIS network have more accuracy in modeling of nonlinear complex systems compared to EKF, MLP and RBF networks. In general, using intelligent data fusion, especially ANFIS, reduces attitude estimation error and time in comparison to the classical EKF method.

Online state estimation in water distribution systems via Extended Kalman Filtering

Robust adaptive smooth variable structure Kalman filter for spacecraft attitude estimation