Spacecraft Autonomous Navigation Using the Doppler Velocity Differences of Different Points on the Solar Disk

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.

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

A distribution map of the large-scale chromospheric Doppler velocities on the solar disk for 5 June 2002 is presented. The map was obtained using a 2D-spectroscopy technique within the He i 10830 A line. The spatial resolution of the map is about 30 arc sec. The map demonstrates a downflow in the chromosphere over active regions, especially significant around the spots and inside the plages. Positive Doppler velocities correspond to strong magnetic field areas, regardless of the field sign. Three major chromospheric outflow zones are observed: an equatorial and two polar ones. Each area of substantial negative Doppler velocities matches a zone of weak intensity of inner corona observed within the Fe ix-x 171 A line by the SOHO spacecraft. A Doppler velocity histogram and the dependence of the Doppler velocities on the cosine of the heliocentric angle for the solar disk are calculated. The total mass outflow from the upper chromosphere is estimated as 2×1013 g s−1. Four percent of this amount is sufficient to produce the fast solar wind.

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.

Autonomous satellite navigation using starlight refraction angle measurements

<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>

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

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.

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.

The paper reviews existing methods of spacecraft navigation and attitude control on near-earth orbits, including the methods utilizing earth-based external trajectory measurement stations, global navigation satellite systems, inertial on-board navigation systems and star trackers. The proposed method is based on the obtaining and processing of a set of sky images on-board of a spacecraft that is moving along a near-earth circular orbit. Image processing results are used to find the coordinates, velocity and attitude of a spacecraft in inertial space.

In an urban environment, one of the key challenges remains to be the reliable estimation of the other traffic participants' motion state. Due to the highly nonlinear motions in city traffic, an instant and precise estimation of heading direction, velocity, and, particularly, yaw rate is required. Radar sensors are well suited for this task due to their robustness to environmental influences and direct measurement of the radial (Doppler) velocity. High-resolution radars receive multiple reflections from an extended object. In comparison to state-of-the-art approaches, not only is the Doppler velocity of a single reference point taken into account, but also is the distribution of the Doppler velocity across the vehicle analyzed. The velocity profile is derived with characteristic features and a corresponding sample covariance. These are fused into an unscented Kalman filter, resulting in a significant accuracy improvement and a reduction in the latency of the filter to almost zero during a change in motion or initialization. This yields a great improvement in determining the trajectories of potential critical objects, increasing the time to avoid collisions. Furthermore, the approach enables simultaneous identification of the rotation center of the object, which is essential for the tracking of highly dynamic maneuvers. All approaches were implemented and evaluated on a large experimental data set using highly precise reference systems as ground truth. The results show an impressive improvement in the accuracy of the yaw rate estimation of a factor of 3–4 compared with state-of-the-art approaches in a dynamic scenario.

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.

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).

This paper addresses the issue of autonomous navigation method based on MEMS sensor optimization for mobile robot in Global Urban System (GUS). A new GPS/INS (Inertial Navigation System) navigation scheme is proposed, based on MEMS gyro north-finding approach. Existing research in the area of GUS focuses on the issues related to SLAM (Simultaneous Localization and Mapping) and planned trajectory tracking, while our work will specially consider about the robot's effectiveness and adaptability based on MEMS gyroscope technology and application in prescribed urban area. And we apply the urban map and GPS information into the outdoor experiment to observe the mobile robot's trajectory. Compared with usual integrated navigation, the system's structure is simplified; targeting precision and navigation performance are improved. From the simulation and experimental results, the MEMS gyro north finding accuracy, effectiveness and usefulness of the proposed autonomous navigation scheme are confirmed.

Currently, international interest has revived for the scientific exploration of the Moon. In this paper, autonomous orbit navigation of spacecraft for lunar probe missions is implemented completely independent of a ground tracking system. In order to achieve this aim, the Doppler measurement duo to the relative motion from a probe to the Moon is used. Other measurements are the directional data and distance data from the ultraviolet sensor. The observability of the system with the available measurements is investigated using a novel observability analysis, which can provide an assessment of the degree of observability. Autonomous orbit navigation is obtained by extended Kalman filtering. The resultings of simulation show that adding radial velocity can improve the estimate accuracy. It is also proved from an observability index point of view.