Autonomous satellite navigation using starlight refraction angle measurements

Implicit UKF and its observability analysis of satellite stellar refraction navigation system

The stellar refraction navigation is a type of autonomous celestial navigation method for satellites that uses high-accuracy star sensors to obtain the stellar refraction. The apparent height and the stellar refraction angle are two kinds of measurements for the satellite stellar refraction navigation system. A better navigation performance can be achieved by the stellar refraction angle rather than the refraction apparent height. However, the measurement model of the stellar refraction angle is an implicit function, which needs an implicit filter method. A new implicit unscented Kalman filter (UKF) method is proposed and applied to the satellite stellar refraction navigation system in this paper. The main difference between the classical UKF and the implicit UKF lies in the measurement update. First, since the implicit measurement model can be rearranged into an equation whose one side is zero, the vector of zeros is regarded as the equivalent measurement, and the difference between the predicted measurement and its true value is utilized for updating the predicted state and its covariance matrix. Second, the measurement noise and its covariance matrix are transformed based on the unscented transformation principle. In addition, the implicit UKF is compared with the implicit extended Kalman filter (IEKF), the iterative IEKF, and the UKF. Simulations show that the proposed implicit UKF method can obtain about 19.0% and 17.5% improvements in three-dimensional root mean square position estimation compared to the IEKF and the iterative IEKF, respectively. Although the navigation performance of the UKF is close to the implicit UKFs, the computational time of the implicit UKF is much shorter than the UKFs. In addition, the sensitivities of the four different Kalman filters to the star sensor's faults and the atmosphere perturbation are compared, respectively.

Autonomous navigation is one of the key technologies to ensure the successful implementation of deep space exploration mission. Aiming at the requirement of celestial autonomous navigation ability of asteroid probe in practical engineering tasks, the integrated autonomous navigation method combined celestial spectral velocity measurement with celestial image angle measurement is proposed to realize continuous autonomous, real-time and high-precision navigation of asteroid probe. Considering the limitation of traditional radio navigation on the ground and the high requirement for autonomous survivability of asteroid probe, the task of asteroid explorer has an urgent need for real-time and autonomous navigation ability. The current autonomous navigation method mainly extracts the angle measurement information of the probe relative to the target celestial planet from the optical image information of the target celestial planet, and then calculates and determines the navigation state of the probe. However the accuracy of velocity estimation by this navigation method is limited, which still cannot fully meet the current demand of asteroid probe. In view of the shortcomings of the main existing autonomous navigation methods, this paper proposes a direct velocity measurement method based on astronomical spectrum, and combines it with the astronomical angle navigation method to form the integrated autonomous navigation method. Based on the integrated autonomous navigation method, the navigation system model and filtering algorithm are derived, and the observability and navigation accuracy performance of the integrated navigation system are analyzed. Taking the Ceres exploration mission as an engineering background, the simulation results show that the stellar velocity information measured by astronomical spectroscopy can greatly improve the integrated navigation performance. And the accuracy of position and velocity estimation results of the probe can restrain the influence of errors more effectively and enhance the reliability of navigation system. Compared with the traditional terrestrial radio navigation or angle measurement navigation methods, the integrated navigation results have higher accuracy and better real-time performance, which can provide accurate navigation information for asteroid probe orbit modification. The integrated navigation method derived in this paper is effective, reliable and easy to implement. It provides a reference for the implementation of the asteroid exploration project and the subsequent major deep space missions in China.

A starlight refraction scheme with single star sensor used in autonomous satellite navigation system

In order to solve the problem of autonomous localization and navigation caused by the loss of the position information of the mobile robot in a complex environment, a method for autonomous navigation of a mobile robot based on ROS is proposed in this paper. The ROS platform is used to avoid the problems of code redundancy, poor portability and so on. In this method, firstly, monte carlo algorithm is used to realize the autonomous localization of the robot, raster map is constructed with lidar data, and the concept of raster occupancy is introduced to reduce the influence of sensor noise. Then an improved ant colony algorithm is proposed for global path planning to realize autonomous navigation of the robot. At the end of this paper, the robot control system is combined with ROS navigation system for map construction and autonomous navigation experiments. The experimental results show that the improved ant colony algorithm can effectively shorten the number of iterations, reduce the number of iterations by 38%, improve the adaptability to the environment, facilitate the fast finding of the shortest path, and accurately realize the autonomous navigation task of the robot.

Satellite navigation by stellar refraction

An autonomous relative navigation method based on stereo vision measurements is proposed for noncooperative targets without any prior information. Firstly, a target identification model using only stereo vision measurements is established. Based on the relative position vector computed from the stereo image sequence, the initial state vector of the noncooperative target and navigation filter covariance matrix are initialized by taken spacecraft attitude and camera installation information into account. Then, an extended Kalman filter considering state noise compensation as well as observation variance matrix and state noise matrix adaptive correction is formulated to solve the noncooperative relative navigation problem. Finally, a low Earth orbit, noncooperative target approaching navigation scenario is considered. The simulation results demonstrate the effectiveness of the proposed method for noncooperative autonomous relative navigation.

A snake robot is a type of highly redundant mobile robot that significantly differs from a tracked robot, wheeled robot and legged robot. To address the issue of a snake robot performing self-localization in the application environment without assistant orientation, an autonomous navigation method is proposed based on the snake robot’s motion characteristic constraints. The method realized the autonomous navigation of the snake robot with non-nodes and an external assistant using its own Micro-Electromechanical-Systems (MEMS) Inertial-Measurement-Unit (IMU). First, it studies the snake robot’s motion characteristics, builds the kinematics model, and then analyses the motion constraint characteristics and motion error propagation properties. Second, it explores the snake robot’s navigation layout, proposes a constraint criterion and the fixed relationship, and makes zero-state constraints based on the motion features and control modes of a snake robot. Finally, it realizes autonomous navigation positioning based on the Extended-Kalman-Filter (EKF) position estimation method under the constraints of its motion characteristics. With the self-developed snake robot, the test verifies the proposed method, and the position error is less than 5% of Total-Traveled-Distance (TDD). In a short-distance environment, this method is able to meet the requirements of a snake robot in order to perform autonomous navigation and positioning in traditional applications and can be extended to other familiar multi-link robots.

With the development of space programs, autonomous navigation of spacecraft is required in case of emergencies. Celestial navigation is a kind of fully autonomous navigation method, which has widely been used in many space missions. Because of the measurement noise in the celestial navigation system, the geometric relations between the spacecraft and various celestial bodies have a great impact on navigation accuracy. The positions of the sun, Earth, and other planets are fixed at a certain time, but stars are distributed all over the sky. The position of the measured star is mainly determined by the pointing direction of the star sensor. To explicitly demonstrate the effect of different pointing directions, an improved observability analysis method based on the hybrid condition number of the observability matrix for a nonlinear system is presented. The impact of the pointing direction of the star sensor on the navigation accuracy is analyzed using this analysis method, with the star-Earth angle used as a measurement for a near-Earth satellite. Simulations show that the value of the hybrid condition number is in good agreement with the position estimation error. This analysis method and the corresponding analysis results are useful in the design, construction, and analysis of celestial navigation systems for spacecraft.

The PPP is a technique being actively researched and developed for estimating the precise position of a stationary point on the surface of the earth. Its particular features are that unlike the relative positioning method, the PPP does not require reference points and it provides the centimeter-level positioning accuracy in real-time using the precise orbit and clock information of the navigation satellites such as the GPS satellites. Lately, the application of the PPP technique to the onboard navigation of the low earth orbit (LEO) satellites is attracting attentions from aerospace researchers and engineers. Such a navigation technique is called the PPP in Space or the Space PPP. As for the existing approaches, the PPP in Space methods using precise LEO satellite dynamics [1] and kinematic PPP which does not use such dynamics [2] [3] were proposed and these results were impressive: the decimeter-level or even the centimeter-level satellite positioning accuracy was confirmed by the offline experiments using actual European LEO satellites dataset. Although these navigation methods are obviously state-of-the-art, this paper takes an another approach that focuses on improving the accuracy of satellite onboard navigation (NAV) using the PPP technique. The methodology presented in this paper is similar to the work [4] that used a reference orbit trajectory for the PPP in Space, that is, the errors of the reference trajectory were estimated using the PPP technique. However, the calculation of their reference trajectories requires the computational time and it can not be performed onboard a satellite. Therefore, we designed our method to work in realtime by using the onboard NAV as the reference trajectory. The performance of the proposed PPP NAV method was evaluated using the actual data of a Japanese earth observation satellite ALOS2 (Advanced Land Observing Satellite) [5].

<abstract> <p>This paper presents an autonomous navigation method for an agricultural mobile robot "AgriEco Robot", with four-wheel-drive and embedded perception sensors. The proposed method allows an accurate guidance between strawberry crop rows while automatically spraying pesticides, as well as detecting the end and switching to the next rows. The main control system was developed using Robot Operating System (ROS) based on a 2D LIDAR sensor. The acquired 2D point clouds data is processed to estimate the robot's heading and lateral offset relative to crop rows. A motion controller is incorporated to ensure the developed autonomous navigation method. Performance in terms of accuracy of the autonomous navigation has been evaluated in real-world conditions within strawberry greenhouses, proving its usefulness for automatic pesticide spraying.</p> </abstract>

Countless applications today are using mobile robots, including autonomous navigation, security patrolling, housework, search-and-rescue operations, material handling, manufacturing, and automated transportation systems. Regardless of the application, a mobile robot must use a robust autonomous navigation system. Autonomous navigation remains one of the primary challenges in the mobile-robot industry; many control algorithms and techniques have been recently developed that aim to overcome this challenge. Among autonomous navigation methods, vision-based systems have been growing in recent years due to rapid gains in computational power and the reliability of visual sensors. The primary focus of research into vision-based navigation is to allow a mobile robot to navigate in an unstructured environment without collision. In recent years, several researchers have looked at methods for setting up autonomous mobile robots for navigational tasks. Among these methods, stereovision-based navigation is a promising approach for reliable and efficient navigation. In this article, we create and develop a novel mapping system for a robust autonomous navigation system. The main contribution of this article is the fuse of the multi-baseline stereovision (narrow and wide baselines) and laser-range reading data to enhance the accuracy of the point cloud, to reduce the ambiguity of correspondence matching, and to extend the field of view of the proposed mapping system to 180°. Another contribution is the pruning the region of interest of the three-dimensional point clouds to reduce the computational burden involved in the stereo process. Therefore, we called the proposed system multi-sensors multi-baseline mapping system. The experimental results illustrate the robustness and accuracy of the proposed system.

In this paper, we present localization and autonomous navigation method using GPU(Graphics Processing Unit)-based SIFT(Scale-Invariant Feature Transform) algorithm and virtual force method for mobile robots. To do this, at first, we propose the localization method to recognize the landmark using the GPU-based SIFT algorithm and to update the position using extended Kalman filter. And then, we propose the A-star algorithm for path planning and the virtual force method for autonomous navigation of the mobile robot. Finally, we demonstrate the effectiveness and applicability of the proposed method through some experiments using the mobile robot with OPRoS(Open Platform for Robotic Services).

Before developing an integrated navigation system, it is an efficient method to build an integrated navigation simulator to reduce cost and shorten period of development. Building a distributed simulator with multiple computers will be more close to the actual system. A distributed integrated simulator for spacecraft autonomous navigation, with the characteristics of modularization and universalisation, was developed with computers and data bus. Based on the previously research, autonomous navigation sensors consists of Strap-down Inertial Navigation System (SINS), Global Positioning System (GPS) and Star Sensor (SS). To realize integrated navigation function, the configuration of the distributed simulator was designed to include Trajectory Generator subsystem, Inertial Measurement Unit subsystem, SINS subsystem, GPS subsystem, SS subsystem and Integrated Navigation subsystem. Hardware design and software development of the simulator are discussed. The federated/centralized Kalman filters are used as simulator information fusion algorithm. The distributed simulator has been applied successfully to provide useful advice for designers.

The angle of the polarization (AOP) and the degree of polarization (DOP) of the scattered skylight are symmetrically distributed concerning the solar meridian. Based on the symmetry of the skylight polarization distribution pattern, this paper proposes a novel skylight orientation sensor consisting of a camera, an S-waveplate, and a linear polarizer. The skylight orientation sensor is using the image polarization encoding capability of the S-waveplate and the linear polarizer to convert the skylight polarization information into the image’s symmetry axis extraction, which has the advantages of simple structure, better real-time performance, no resolution loss and instantaneous field of view error. The symmetry axis in the image is consistent with the solar meridian. Therefore, the angle between the solar meridian and the skylight orientation sensor reference axis can be obtained without calculating the polarization information, which shows why the sensor has better real-time performance. The angle measurement accuracy and uncertainty of the skylight orientation sensor are verified by numerical simulation and outdoor experiments. The results demonstrate that the skylight orientation sensor has good application potential in autonomous navigation.