Autonomous navigation system of near-Earth spacecraft

Initiated and financed by the Canadian Space Agency, Micro-Rover Platform with Tooling Arm (MRPTA) project has targeted the development of a robotic system with remote control and autonomous navigation capabilities for testing a large variety of analogous planetary missions. Although developed for planetary exploration, the navigation system is highly suitable to such areas of application as construction and in mining. Autonomous navigation system is capable of moving the platform to predefined position(s)/orientation(s) while continuously mapping the terrain, assessing its traversability and choosing most appropriate path to follow. Designed according to the JAUS (Joint Architecture for Unmanned Systems) framework, the system consists of several interconnected components. Following components are of particular importance: Pose Estimator (PE), Terrain Evaluator (TE), Map Manager (MP), and Path Planner (PP). The PE uses a minimal sensor configuration consisting of an azimuth gyroscope, inclinometer and wheel odometry. The PE provides reliable pose estimates in spite of pronounced slippage by employing extensive use of fused data. The TE uses a nodding laser scanner which continuously sweeps the area in front of the platform and constructs a “traversability grid” (TG) of the surrounding area. Multiple TGs are constantly constructed along the platform motion path are combined into a terrain map maintained by the MP. The PP uses this map to compute a motion path Obtained path is executed by the motion controller and remains current until it is contradicted by the most recent map – at which point it is re-planned. This paper addresses integration issues, lessons learned and also outlines the possible applications for construction and mining.

With the rapid development of UAV industry, autonomous UAV obstacle avoidance navigation has become a core problem in the field of UAV control. Based on recurrent neural networks, this paper proposes an LSTM-enhanced Layered-RSAC algorithm to construct a collaborative task execution and autonomous navigation system for UAV swarms. By constructing the autonomous navigation system of UAV, its accuracy is tested and the UAV operation situation index is examined. Through model training, the standard deviation of the a priori strategy with the best success rate of autonomous navigation is explored. The a priori strategy σ = 0.45 is taken as the initial value to verify the performance improvement of the Layered-RSAC algorithm. The results show that the Layered-RSAC algorithm reaches 90% navigation success rate at 50 training steps for the first time and stabilizes at 90% to 100% success rate after 100 training steps, which is significantly ahead of Prior-Policy, DDPG and SAC algorithms.

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments.

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and positioning. In order to reduce the computational complexity and exclude the risk caused by Global Positioning System (GPS) outage, an autonomous navigation system based on distributed filtering is designed and realized. When GPS is available by monitoring the GPS integrity, sensor information from Strapdown Inertial Navigation System (SINS) and GPS is fused using a 7-state Conventional Kalman Filter (CKF) to estimate the full UAV state (position, velocity and attitude); when GPS is unavailable, sensor information from gyroscopes, accelerometers and magnetometer is fused using a 4-state Extended Kalman Filter (EKF) to estimate the attitude and heading of the UAV, and sensor information from SINS and vision positioning system is fused using a 7-state Incoordinate Interval Kalman Filter (IIKF) to estimate the position and velocity of the UAV. In addition, the second-order vertical channel damping loop is adopted to fuse measurements from a barometer with those of SINS, which suppresses the divergence of the vertical channel error and makes the altitude information calculated by SINS trustable. Both ground and flight experiments of the autonomous navigation system have been carried out. The test results show that the system can provide stabilized attitude information in long durations, and can realize the automatic flight control of UAV.

This paper presents new algorithmic methods for accuracy improvement of autonomous inertial navigation systems of aircrafts. Firstly, an inertial navigation system platform and its nonlinear error model are considered, and the correction schemes are presented for autonomous inertial navigation systems using internal information. Next, a correction algorithm is proposed based on signals from precession angle sensors. A vector of reduced measurements for the estimation algorithm is formulated using the information about the angles of precession. Finally, the accuracy of the developed correction algorithms for autonomous inertial navigation systems of aircrafts is studied. Numerical solutions for the correction algorithm are presented by the adaptive Kalman filter for the measurement data from the sensors. Real data of navigation system Ts-060K are obtained in laboratory experiments, which validates the proposed algorithms.

Forecasting the impact of an 1859-calibre superstorm on satellite resources

GNSS ile konum belirleme yöntemlerinden mutlak konum belirleme tekniği olan Hassas Nokta Konumlama (PPP) yöntemi tek bir Küresel Konum Belirleme Sistemi (GNSS) alıcısıyla konum bilgisi sağlamaktadır. Bu durum maliyet ve zaman tasarrufu, yüksek konum doğruluğu, kolay ve pratik çözümler sunması son yıllarda geleneksel-PPP ve Hassas Nokta Konumlama-Belirsizlik Çözümü (PPP-AR) yöntemlerine olan ilgiyi arttırmış ve bu sayede çok sayıda yazılım üretilmiştir. Bu çalışmada son yıllarda geliştirilmiş ve kullanıcının hizmetine sunulmuş olan Net_Diff ve PRIDE yazılımları kullanılarak PPP-AR yönteminin konum belirleme performansı incelenmiştir. Bu amaçla ISTA ve MERS Uluslararası GNSS Hizmeti (IGS) istasyonlarının yirmi günlük (29.10.2022-17.11.2022) gözlem verileri dikkate alınmıştır. Değerlendirmelerde GPS, GLONASS, GALILEO ve BeiDou uydu sistemleri kullanılmıştır. Yazılımlardan elde edilen sonuçlar istasyonların referans (IGS günlük çözüm) koordinat değerleriyle karşılaştırılmıştır. Çalışma sonucunda yatay ve düşey yönlerde elde edilen karesel ortalama hata değerlerinden PRIDE yazılımının Net_Diff yazılımına göre daha yüksek nokta konumlandırma doğruluğuna ve daha iyi performansa sahip olduğu tespit edilmiştir.

Autonomous navigation of Satellite and constellation has series of benefits, such as to reduce operation cost and ground station workload, to avoid the event of crises of war and natural disaster, to increase spacecraft autonomy, and so on. Autonomous navigation satellite is independent of ground station support. Many systems are developed for autonomous navigation of satellite in the past 20 years. Along them American MANS (Microcosm Autonomous Navigation System) [1] of Microcosm Inc. and ERADS [2]，[3] (Earth Reference Attitude Determination System) of Honeywell Inc. are well known. The systems anticipate a series of good features of autonomous navigation and aim low cost, integrated structure, low power consumption and compact layout. The ERADS is an integrated small 3-axis attitude sensor system with low cost and small volume. It has the Earth center measurement accuracy higher than the common IR sensor because the detected ultraviolet radiation zone of the atmosphere has a brightness gradient larger than that of the IR zone. But the ERADS is still a complex system because it has to eliminate many problems such as making of the sapphire sphere lens, birefringence effect of sapphire, high precision image transfer optical fiber flattener, ultraviolet intensifier noise, and so on. The marginal sphere FOV of the sphere lens of the ERADS is used to star imaging that may be bring some disadvantages., i.e. , the image energy and attitude measurements accuracy may be reduced due to the tilt image acceptance end of the fiber flattener in the FOV. Besides Japan, Germany and Russia developed visible earth sensor for GEO [4]、[5]. Do we have a way to develop a cheaper/easier and more accurate autonomous navigation system that can be used to all LEO spacecraft, especially, to LEO small and micro satellites? To return this problem we provide a new type of the system—CANS (Compact Autonomous Navigation System) [6].

This paper presents the usage of LQR-based intelligent path tracking methods to control the autonomous navigation system of an Unmanned Surface Robot (USR) in sea-clutter environmental fields. Since considerable interest in the Autonomous Navigation System (ANS) of USR in sea warfare has increased, the stability of the Autonomous Navigation System (ANS) control becomes of the utmost importance. When the USR moves in sea environments, the control stability of autonomous navigation systems becomes a major concern. The Path Tracking Controller (PTC) of USRs is one of the high potential subsystems that can be further improved to achieve more accurate, robust, and comfortable tracking performance. The stability of the PTC should be taken into consideration while performing the path tracking task in autonomous navigation. To solve the automatic waypoint tracking stability of independently-developed PTCs, a waypoint tracking controller is designed based on the Linear Quadratic Regulator (LQR) theory with the kinematic model of USRs. Then, a simulation analysis of the designed waypoint tracking controller is performed, and the results are compared at two different speeds. Additionally, complex waypoint tracking tests under realistic sea conditions are performed based on the designed waypoint tracking controller. Results show good tracking performance, which indicates that the designed waypoint tracking controller can adapt to high-speed USRs.

This study continues the series of papers devoted to the problems of autonomous operation of spacecraft in a geostationary orbit. The solution of the problem considered here assumes the formation of a set of algorithms for control processes in a closed autonomous spacecraft control and navigation system in a geostationary orbit. The paper is aimed at the formalization and solution of the new technical task of autonomous control during the spacecraft's ascent to the given orbital position and remaining in this position. An important requirement is to provide the safe separation of several spacecraft in one orbital position. The control problem is solved using the combined optimization method developed by us; in this method, the control vector is divided into the synthesized and the programmed components taking into account the principle of the separation of the navigation and control problem in the stochastic approach. The motion's models proposed in the previous paper are used to develop the control algorithms for a spacecraft's ascent to the working position in a geostationary orbit and remaining in this position. The results of the algorithms simulating the ascent and maintaining for the exactly known state vector taking into account the random spread of the initial conditions and thrust are presented.

Safety assurance remains paramount for the realization of autonomous vessels. A robust assurance methodology that can provide traceability throughout the design and verification process is necessary to demonstrate safety equivalence to that of conventional ships. However, there are few references that propose a holistic safety assurance scheme for autonomous ships, using actual engineering processes of ships as examples. This study proposes a design and development method for an autonomous navigation system that integrates Model-Based Systems Engineering (MBSE), System Theoretic Process Analysis (STPA), Failure Modes and Effects Analysis (FMEA), and several verification methods including simulation-based tests to develop a comprehensive safety assurance scheme. This safety assurance scheme is being conducted as a case study for a newly constructed autonomous vessel. First, STPA can be performed on the conceptual design established by MBSE to extract additional safety requirements from the identified loss scenarios. Focusing on the process model in the loss scenario description leads to a deeper understanding of the system behavior. FMEA in addition identify potential component failure modes and their impact on system safety. The multi-level requirements that emerge from these activities are validated in test scenarios. Simulators are used to evaluate whether the autonomous navigation system’s safety can meet predefined pass criteria in some of the scenarios. These activities ensure traceability from safety goals to design decisions, allowing design changes and their impact on safety to be evaluated throughout the development lifecycle and allowing for more systematic ongoing monitoring during operations.

Abstract. Yield mapping is the first step for site-specific crop management. Many yield mapping systems have been developed for grain crops. However, it remains a difficult task for tree crops. In this study an autonomous yield mapping system for citrus crops was developed. The system was designed to detect fruit and create yield maps at early stages so that farmers could manage the grove site specifically based on the maps. It consisted of two major sub-systems, an autonomous navigation system and an imaging system. Robot Operating System (ROS) was used for developing the autonomous navigation system on top of an unmanned ground vehicle. An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable navigation solutions. In the imaging system, a high-resolution visible camera was carried by the vehicle for image acquisition. All the video frames were associated with latitude and longitude coordinates automatically. Detection of fruit from the video frames utilized a VGG16 model, which was trained with Faster-RCNN. Fruit detection was evaluated and an accuracy of 77% was achieved. The Lucas-Kanade optical flow method was used for tracking each detected fruit and counting the total number of fruit. The complete system was tested in a citrus grove in Florida.

Technical solutions for the creation of fundamentally new universal multifunctional autonomous navigation system of radar operating principle are proposed. Currently, navigation systems of unmanned aerial vehicles (UAV) are based on global navigation satellite systems (GNSS) receivers, combined with a block of inertial sensors of spatial orientation. GNSS are based on the use of coordinated by motion and radiation signals of Earth artificial navigation satellites network. GNSS provide continuous and almost immediate determination of the position (location) and speed of the UAV almost over the globe. Such a system provides a fairly accurate determination of the UAV position and parameters of its movement subject to a good GNSS signal. At the same time, the GNSS is a radio-technical navigation system and, like any such system, is subject to the influence of external radio interference, both natural and artificial. One of the ways to solve the problem of performing UAV flights in conditions of natural and artificial interference, when information from external sources is not available to estimate the position of the UAV, is the use of path reckoning method. The most important component of the autonomous navigation system implementing the course-Doppler mode of path calculation is the Doppler velocity sensor and hover drift. The R&D of Doppler velocity sensor & hover drift for the UAV (small size and weight) is a complex scientific, technical and design task, the implementation of which became possible due to the use of modern technical (design, schematic, technological, etc.) solutions.

An autonomous and secure navigation system is needed for the spacecraft in future deep space missions. X-ray pulsar offers the possibility of autonomous navigation anywhere in the solar system. This article introduces an analysis of the spacecraft autonomous navigation system accuracy, utilizing signals emitted from the X-ray pulsars. Based on the principle of the X-ray navigation, visibility of X-ray pulsar on L1 sun-observation orbit was simulated. The influence of the geometry distribution and the pulsar angle position error was analyzed. Error sources of the navigation system were listed, including the measurement error of the X-ray detector, transformation error of time-of-arrival (TOA), and the navigation calculation error, and the simulations of their parts were shown.

Over the last few years, the increasing number of autonomous systems has required stronger, smarter, and more context-aware frameworks that can work effectively in uncertain and dynamic settings. This research proposes a multisensor fusion architecture based on deep learning for autonomous control and navigation systems. The method combines complementary sensor inputs like LiDAR, radar, and vision data using a layered pipeline, including data acquisition, preprocessing, fusion, and neural decision-making. The combination strategy improves environmental perception, and real-time object detection, path planning, and decision output are accomplished using convolutional and recurrent neural networks. Experiment results indicate significant system accuracy, response time, and adaptability improvements compared with the conventional modular control structures. The proposed approach also demonstrates enhanced capabilities in more complex environments that require adequate obstacle perception and real-time path search. Despite the computational difficulties due to readout characteristics and issues of interpretability, the results show encouraging evidence towards the scalable and safe deployment of autonomous systems. Future development and the development of lighter AI models, explainable decision mechanisms, and approaches for domain adaptation are described in the conclusion of this work.