Local Navigation Research Articles

A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting

In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and improving the accuracy of satellite navigation positioning can both benefit from the accurate modeling and forecasting of ionospheric TEC. The majority of current ionospheric TEC forecasting research only considers the temporal or spatial dimensions, ignoring the ionospheric TEC’s spatial and temporal autocorrelation. Therefore, we constructed a spatiotemporal network model with two modules: (i) global spatiotemporal characteristics extraction via forwarding spatiotemporal characteristics transfer and (ii) regional spatiotemporal characteristics correction via reverse spatiotemporal characteristics transfer. This model can realize the complementarity of TEC global spatiotemporal characteristics and regional spatiotemporal characteristics. It also ensures that the global spatiotemporal characteristics of the global ionospheric TEC are transferred to each other in both temporal and spatial domains at the same time. The spatiotemporal network model thus achieves a spatiotemporal prediction of global ionospheric TEC. The Huber loss function is also used to suppress the gross error and noise in the ionospheric TEC data to improve the forecasting accuracy of global ionospheric TEC. We compare the results of the spatiotemporal network model with the Center for Orbit Determination in Europe (CODE), the convolutional Long Short-Term Memory (convLSTM) model and the Predictive Recurrent Neural Network (PredRNN) model for one-day forecasts of global ionospheric TEC under different conditions of time and solar activity, respectively. With internal data validation, the average root mean square error (RMSE) of our proposed algorithm increased by 21.19, 15.75, and 9.67%, respectively, during the maximum solar activity period. During the minimum solar activity period, the RMSE improved by 38.69, 38.02, and 13.54%, respectively. This algorithm can effectively be applied to ionospheric delay error correction and can improve the accuracy of satellite navigation and positioning.

Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning

In this letter, we present an autonomous navigation system for goal-driven exploration of unknown environments through deep reinforcement learning (DRL). Points of interest (POI) for possible navigation directions are obtained from the environment and an optimal waypoint is selected, based on the available data. Following the waypoints, the robot is guided towards the global goal and the local optimum problem of reactive navigation is mitigated. Then, a motion policy for local navigation is learned through a DRL framework in a simulation. We develop a navigation system where this learned policy is integrated into a motion planning stack as the local navigation layer to move the robot between waypoints towards a global goal. The fully autonomous navigation is performed without any prior knowledge while a map is recorded as the robot moves through the environment. Experiments show that the proposed method has an advantage over similar exploration methods, without reliance on a map or prior information in complex static as well as dynamic environments.

B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving

We address the problem of ego-vehicle navigation in dense simulated traffic environments populated by road agents with varying driver behaviors. Navigation in such environments is challenging due to unpredictability in agents’ actions caused by their heterogeneous behaviors. We present a new simulation technique consisting of enriching existing traffic simulators with behavior-rich trajectories corresponding to varying levels of aggressiveness. We generate these trajectories with the help of a driver behavior modeling algorithm. We then use the enriched simulator to train a deep reinforcement learning (DRL) policy that consists of a set of high-level vehicle control commands and use this policy at test time to perform local navigation in dense traffic. Our policy implicitly models the interactions between traffic agents and computes safe trajectories for the ego-vehicle accounting for aggressive driver maneuvers such as overtaking, over-speeding, weaving, and sudden lane changes. Our enhanced behavior-rich simulator can be used for generating datasets that consist of trajectories corresponding to diverse driver behaviors and traffic densities, and our behavior-based navigation scheme can be combined with state-of-the-art navigation algorithms.

Design of Dual-LiDAR High Precision Natural Navigation System

With the further development of online shopping and the impact of the COVID-19 pandemic, the logistics industry has further increased the demand for unmanned, automated warehousing and logistics handling. To realize intelligent warehousing and logistics handling, reliable positioning navigation technology is indispensable. Therefore, this paper designs a Dual-lidar high-precision natural navigation system based on the ROS (Robot Operating System) platform, which can fulfill the basic warehousing and logistics requirements. The natural navigation system uses the Lidar-SLAM method based on graph optimization to construct the 2D environment map, the PF (Particle Filter) algorithm in MRPT (Mobile Robot Programming Toolkit) is used for system positioning, and the real-time correction algorithm is used for motion control. On the built hardware platform, the navigation system completed the fixed-point cruise navigation task, and finally achieved a navigation accuracy of 4 cm and an average repeatable navigation accuracy of 6 mm. The designed navigation system has reference significance for multi-sensor fusion navigation. In reality, it can be applied to the transportation of warehousing and logistics, and it is expected to be mass-produced in the future.

The pre-process DV-UKF algorithm for high-precision attitude measurement with low-cost inertial sensor

PurposeThe purpose of this paper is to propose a high-precision attitude solution to solve the attitude drift problem caused by the dispersion of low-cost micro-electro-mechanical system devices in strap-down inertial navigation attitude solution of micro-quadrotor.Design/methodology/approachIn this study, a three-stage attitude estimation scheme that combines data preprocessing, gyro drifts prediction and enhanced unscented Kalman filtering (UKF) is proposed. By introducing a preprocessing model, the quaternion orientation is calculated as the composition of two algebraic quaternions, and the decoupling feature of the two quaternions makes the roll and pitch components independent of magnetic interference. A novel real-time based on differential value (DV) estimation algorithm is proposed for gyro drift. This novel solution prevents the impact of quartic characteristics and uses the iterative method to meet the requirement of real-time applications. A novel attitude determination algorithm, the pre-process DV-UKF algorithm, is proposed in combination with UKF based on the above solution and its characteristics.FindingsCompared to UKF, both simulation and experimental results demonstrate that the pre-process DV-UKF algorithm has higher reliability in attitude determination. The dynamic errors in the three directions of the attitude are below 2.0°, the static errors are all less than 0.2° and the absolute attitude errors tailored by average are about 47.98% compared to the UKF.Originality/valueThis paper fulfils an identified need to achieve high-precision attitude estimation when using low-cost inertial devices in micro-quadrotor. The accuracy of the pre-process DV-UKF algorithm is superior to other products in the market.

Hydrodynamic Characteristics at Intersection Areas of Ship and Bridge Pier with Skew Bridge

Ships sailing in the area of a bridge are vulnerable to the influence of complex water flow, due to the complex flow pattern around the bridge pier. Ships often crash into bridge piers, leading to serious economic losses and threating personal safety. Based on the common forms of piers of skew bridges, the hydrodynamic problems encountered during ship–bridge interactions in the area of a skew bridge were studied using particle image velocimetry-based flume testing, physical model testing, and numerical simulation. The influence of the flow angle of attack of a round-ended pier on the force and center of gravity of a ship moving on both sides of a pier is discussed under various ship–bridge transverse spacings. The results show that as a ship passes through the bridge area, the bow roll moment exhibits three peak values: ‘positive’, ‘negative’, and ‘positive’, and the curve of the center of gravity position forms the shape of a ‘straw hat’. With an increase in the flow angle of attack of the pier, the negative peak value and the second positive peak value of the bow roll moment of the ship passing through the back flow side of the pier become greater than those on the upstream side. Moreover, the ship’s navigation attitude is more unstable compared to that upstream, and the ship is at risk of colliding with the pier and sweeping. The width of the restricted water area, determined by the hydrodynamic action between the ship and bridge in the skew bridge area, is the same as that determined by the critical lateral velocity. For the ship class referred to in this study, the current code can also be used in channel design, to safeguard ship and personal safety with piers with a large flow angle of attack.

Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM.

The application of mobile robots is an important link in the development of intelligent greenhouses. In view of the complex environment of a greenhouse, achieving precise positioning and navigation by robots has become the primary problem to be solved. Simultaneous localization and mapping (SLAM) technology is a hot spot in solving the positioning and navigation in an unknown indoor environment in recent years. Among them, the SLAM based on a two-dimensional (2D) Lidar can only collect the environmental information at the level of Lidar, while the SLAM based on a 3D Lidar demands a high computation cost; hence, it has higher requirements for the industrial computers. In this study, the robot navigation control system initially filtered the information of a 3D greenhouse environment collected by a 3D Lidar and fused the information into 2D information, and then, based on the robot odometers and inertial measurement unit information, the system has achieved a timely positioning and construction of the greenhouse environment by a robot using a 2D Lidar SLAM algorithm in Cartographer. This method not only ensures the accuracy of a greenhouse environmental map but also reduces the performance requirements on the industrial computer. In terms of path planning, the Dijkstra algorithm was used to plan the global navigation path of the robot while the Dynamic Window Approach (DWA) algorithm was used to plan the local navigation path of the robot. Through the positioning test, the average position deviation of the robot from the target positioning point is less than 8 cm with a standard deviation (SD) of less than 3 cm; the average course deviation is less than 3° with an SD of less than 1° at the moving speed of 0.4 m/s. The robot moves at the speed of 0.2, 0.4, and 0.6 m/s, respectively; the average lateral deviation between the actual movement path and the target movement path is less than 10 cm, and the SD is less than 6 cm; the average course deviation is <3°, and the SD is <1.5°. Both the positioning accuracy and the navigation accuracy of the robot can meet the requirements of mobile navigation and positioning in the greenhouse environment.

Procedure of the Synthesis of Control Device by the Method of Analytical Design of the Optimal Regulator for the Problem of Aircraft Guidance Along a Hyperbola

The purpose of the article is to describe the methodology for obtaining the structure of the optimal controller, which provides the guidance of the aircraft to the target point on the plane, using the method of time difference of arrivals (TDoA) for two navigation positions. The main feature of the system is that one measurement of time difference of arrivals between the aircraft and two navigation positions does not allow to describe the position of the aircraft on the plane fully. It describes only the line of position (LOP) in the form of a hyperbola. The aiming problem is solved by choosing a hyperbola passing through the target point, bringing the aircraft to a given hyperbola, followed by its movement along this hyperbola. In this case, the choice of the target hyperbola is a trivial task, in contrast to the task of determining the structure of the control system implementing the right and effective guidance law. The regulator obtained on the basis of the method of analytical design of the optimal regulator, according to the results of the computer simulation, showed its operability.

Research on Improving Heading and Attitudes Accuracy by Online Calibration of Errors Based on Multi-RINSs Joint Rotation Modulation

The ship usually adopts multiple sets of rotational modulation inertial navigation system (RINS) to ensure the accuracy and reliability of long endurance navigation, but the rotational modulation will excite the gyro installation error and scale factor error, which will cause periodic oscillation of navigation attitude information, moreover, due to the influence of temperature, vibration and other environments, these errors will change in the results of pre-calibration, and it is impossible to accurately model them, resulting in the further aggravation of attitudes oscillation, which is not conducive to the transfer alignment between master and slave INS. However, the ship is always swinging or maneuvering, a single RINS can not complete self-calibration independently in dynamic condition. In order to solve the above problems, in this paper, an online calibration algorithm based on joint rotation modulation of multi-RINSs is proposed for redundant configuration of multi-RINSs without extra conditions, and the error parameters are tracked and estimated every 4 minutes, the dynamic simulation results show that the calibration strategy can effectively calibrate the gyro installation error, the nonorthogonal angle between the gimbals and the scale factor error of the z-gyro of each RINS, which proves the correctness of the calibration strategy. The results of 3h navigation experiment show that the amplitude of attitudes oscillation decreases about 80% after the compensation of joint calibration error parameters, the fluctuation amplitude of heading angle decreases by 70%, the effectiveness of the calibration strategy in practical application is further verified, the relationship of ship multi-RINSs changes from backup to assistance, which is of great significance to improve the heading and attitudes accuracy of RINSs and the transfer alignment performance between master and slave INS.

Simulation Experiment and Analysis of GNSS/INS/LEO/5G Integrated Navigation Based on Federated Filtering Algorithm.

This article examines the positioning effect of integrated navigation after adding an LEO constellation signal source and a 5G ranging signal source in the context of China’s new infrastructure construction. The tightly coupled Kalman federal filters are used as the algorithm framework. Each signal source required for integrated navigation is simulated in this article. At the same time, by limiting the range of the azimuth angle and visible height angle, different experimental scenes are simulated to verify the contribution of the new signal source to the traditional satellite navigation, and the positioning results are analyzed. Finally, the article compares the distribution of different federal filtering information factors and reveals the method of assigning information factors when combining navigation with sensors with different precision. The experimental results show that the addition of LEO constellation and 5G ranging signals improves the positioning accuracy of the original INS/GNSS by an order of magnitude and ensures a high degree of positioning continuity. Moreover, the experiment shows that the federated filtering algorithm can adapt to the combined navigation mode in different scenarios by combining different precision sensors for navigation positioning.

Method for determining of the distance to the object in the hyperbolic navigation system via doppler frequency offsets

In this paper, a method for estimating the distance to the object guided along a hyperbola to a target using a bistatic hyperbolic navigation system on a plane is given. At the same time, to solve the guidance problem, the number of required navigation positions is reduced by one in comparison with the classical method of hyperbolic navigation. However, in the guidance algorithms, it is still required to estimate the distance of the targeted object from the center of the base, the methods of obtaining which are considered in the work.

Stability and Reliability of Enhanced External-Internal Motion Correlation via Dynamic Phase-Shift Corrections Over 30-min Timeframe for Respiratory-Gated Radiotherapy.

To assess the stability of patient-specific phase shifts between external- and internal-respiratory motion waveforms, the reliability of enhanced external–internal correlation with phase-shift correction, and the feasibility of guiding respiratory-gated radiotherapy (RGRT) over 30 min. In this clinical feasibility investigation, external bellows and internal-navigator waveforms were simultaneously and prospectively acquired along with two four-dimensional magnetic resonance imaging (4DMRI) scans (6–15 m each) with 15–20 m intervals in 10 volunteers. A bellows was placed 5 cm inferior to the xiphoid to monitor abdominal motion, and an MR navigator was used to track the diaphragmatic motion. The mean phase-domain (MPD) method was applied, which combines three individual phase-calculating methods: phase-space oval fitting, principal component analysis, and analytic signal analysis, weighted by the reciprocal of their residual errors (RE) excluding outliers (RE >2σ). The time-domain cross-correlation (TCC) analysis was applied for comparison. Dynamic phase-shift correction was performed based on the phase shift detected on the fly within two 10 s moving datasets. Simulating bellows-triggered gating, the median and 95% confidence interval for the navigator's position at beam-on/beam-off and %harm (percentage of beam-on time outside the safety margin) were calculated. Averaged across all subjects, the mean phase shifts are found indistinguishable (p > .05) between scan 1 (55˚ ± 9˚) and scan 2 (59˚ ± 11˚). Using the MPD method the averaged correlation increases from 0.56 ± 0.22 to 0.85 ± 0.11 for scan 1 and from 0.47 ± 0.30 to 0.84 ± 0.08 for scan 2. The TCC correction results in similar results. After phase-shift correction, the number of cases that were suitable for amplitude gating (with <10%harm) increased from 2 to 17 out of 20 cases. A patient-specific, stable phase-shift between the external and internal motions was observed and corrected using the MPD and TCC methods, producing long-lasting enhanced motion correlation over 30m. Phase-shift correction offers a feasible strategy for improving the accuracy of tumor-motion prediction during RGRT.

Ensuring situational awareness of the moving objects group using local navigation

Ensuring situational awareness of the moving objects group using local navigation

Development of an Intraoperative Cancer Localization Navigation System to Support Complete Resection of Prostate Cancer

Development of an Intraoperative Cancer Localization Navigation System to Support Complete Resection of Prostate Cancer

A node selection algorithm to graph-based multi-waypoint optimization navigation and mapping

Autonomous robot multi-waypoint navigation and mapping have been demanded in many real-world applications found in search and rescue (SAR), environmental exploration, and disaster response. Many solutions to this issue have been discovered via graph-based methods in need of switching the robotos trajectory between the nodes and edges within the graph to create a trajectory for waypoint-to-waypoint navigation. However, studies of how waypoints are locally bridged to nodes or edges on the graphs have not been adequately undertaken. In this paper, an adjacent node selection (ANS) algorithm is developed to implement such a protocol to build up regional path from waypoints to nearest nodes or edges on the graph. We propose this node selection algorithm along with the generalized Voronoi diagram (GVD) and Improved Particle Swarm Optimization (IPSO) algorithm as well as a local navigator to solve the safety-aware concurrent graph-based multi-waypoint navigation and mapping problem. Firstly, GVD is used to form a Voronoi diagram in an obstacle populated environment to construct safety-aware routes. Secondly, the sequence of multiple waypoints is created by the IPSO algorithm to minimize the total travelling cost. Thirdly, while the robot attempts to visit multiple waypoints, it traverses along the edges of the GVD to plan a collision-free trajectory. The regional path from waypoints to the nearest nodes or edges needs to be created to join the trajectory by the proposed ANS algorithm. Finally, a sensor-based histogram local reactive navigator is adopted for moving obstacle avoidance while local maps are constructed as the robot moves. An improved -spline curve-based smooth scheme is adopted that further refines the trajectory and enables the robot to be navigated smoothly. Simulation and comparison studies validate the effectiveness and robustness of the proposed model.

Transport safety: GNSS spoofing detection using the single-antenna receiver and the speedometer of a vehicle

Article presented the security issues of the means of transport which apply global navigation satellite systems, with a particular emphasis on transport safety and the risks resulting from the use of those systems. Presented method of detecting spoofing attacks of the global satellite navigation system involves collecting navigational position readings and comparing them with the current vehicle speed read from the vehicle's speedometer (SoV) and sequential testing of GNSS and SoV readings. Proposed a way automatic speedometer calibration to specified threshold value (D). A physical experiment was carried out to confirm the assumptions of the above-mentioned method, the results of which are that when the car appears in range of spoofer, its speed, as stated by the NMEA RMC readings, was equal to the spoofer speed (0 km/h).

New Methodology of Designation the Precise Aircraft Position Based on the RTK GPS Solution.

The paper presents the results of research on improving the accuracy of aircraft positioning using RTK-OTF (Real Time Kinematic–On The Fly) technique in air navigation. The paper shows a new solution of aircraft positioning for the application of the differential RTK-OTF technique in air navigation. In particular, a new mathematical model is presented which makes it possible to determine the resultant position of an aircraft based on the solution for the method of least squares in a stochastic process. The developed method combines in the process of alignment of GPS (Global Positioning System) observations, three independent solutions of the aircraft position in OTF mode for geocentric coordinates XYZ of the aircraft. Measurement weights as a function of the vector length and the mean vector length error, respectively, were used in the calculations. The applied calculation method makes it possible to determine the resultant position of the aircraft with high accuracy: better than 0.039 m with using the measurement weight as a function of the vector length and better than 0.009 m with the measurement weight as a function of the mean error of the vector length, respectively. In relation to the classical RTK-OTF solution as a model of the arithmetic mean, the proposed method makes it possible to increase the accuracy of determination of the aircraft position by 45–46% using the measurement weight as a function of the vector length, and 86–88% using the measurement weight as a function of the mean error of the vector length, respectively. The obtained test results show that the developed method improves to significantly improve the accuracy of the RTK-OTF solution as a method for determining the reference position in air navigation.

Optimization of a heterogeneous computing system architecture based on its coordination with a sensors group of a mobile unmanned platform

This article analyses the existing variety of sensors used in robotics and related fields and also proposes the architecture of a heterogeneous computing system designed to analyze data obtained from sensors of a mobile unmanned platform (MUP). A feature of unmanned platforms is the presence of tasks that require a significantly different level of performance of the on-board computing system for processing data from sensors of the corresponding type. Therefore, the adaptation of existing universal computing systems seems to be impractical, compared to the development of a specialized computing system with a heterogeneous architecture. The computing system is designed to solve problems of local navigation, stabilize the position of the MUP and control its movement, as well as control special equipment installed on the MUP. Often, if the goal is to ensure maximum efficiency, expressed in speed, accuracy and reliability, it is necessary to develop specialized devices. The article provides information on sensors of the main types used in robotics and indicates the requirements for the performance of a computing system necessary for processing data from sensors of this type. This, in turn, made it possible to propose a heterogeneous architecture containing processor subsystems focused on processing data from sensors requiring low, medium and high performance according to the considered classification.

GIL: a tightly coupled GNSS PPP/INS/LiDAR method for precise vehicle navigation

Accurate positioning and navigation play a vital role in vehicle-related applications, such as autonomous driving and precision agriculture. With the rapid development of Global Navigation Satellite Systems (GNSS), Precise Point Positioning (PPP) technique, as a global positioning solution, has been widely applied due to its convenient operation. Nevertheless, the performance of PPP is severely affected by signal interference, especially in GNSS-challenged environments. Inertial Navigation System (INS) aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments, but suffers from degradation during GNSS outages. LiDAR (Laser Imaging, Detection, and Ranging)-Inertial Odometry (LIO), which has performed well in local navigation, can restrain the divergence of Inertial Measurement Units (IMU). However, in long-range navigation, error accumulation is inevitable if no external aids are applied. To improve vehicle navigation performance, we proposed a tightly coupled GNSS PPP/INS/LiDAR (GIL) integration method, which tightly integrates the raw measurements from multi-GNSS PPP, Micro-Electro-Mechanical System (MEMS)-IMU, and LiDAR to achieve high-accuracy and reliable navigation in urban environments. Several experiments were conducted to evaluate this method. The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors (RMSEs) of the proposed GIL method have the improvements of 63.0%, 51.3%, and 62.2% in east, north, and vertical components, respectively. The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment (i.e., the environment with GNSS signals partly-blocked) and meter-level positioning accuracy in GNSS difficult environment (i.e., the environment with GNSS hardly used). Besides, the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.

Mounting a smartphone on a steering-wheel to facilitate ease of visibility of the navigation screen: A systematic product design approach.

Both professional and personal car drivers use smartphones as In-Vehicle Infotainment System (IVIS) and generally mount it wherever they feel convenient. Inappropriate or sub-optimal positioning of navigation devices increases off-road eye-glance duration and fixation frequency. The current research aimed to develop a smartphone holding device to facilitate the mobile-phone's easy mounting on the steering wheel's hub, ensuring the screen's visibility at a comfortable viewing angle in a vertical upright position irrespective of the steering wheel's rotation. A systematic product design methodology was adopted to develop the final product. The morphological chart was adopted for generating the different concepts of the smartphone mounting device. A Pugh chart was used for screening the various concepts generated in the previous step. Finally, a prototype of the selected best concept was made. User acceptance was assessed by taking feedback from users, and System Usability Scale (SUS) was used for usability evaluation. The developed innovative mounting device was light-weight and easy to use (SUS score 83.5). The final prototype was very effective in changing the angle of the smartphone to facilitate easy visibility at a comfortable viewing angle through the use of a ball and socket mechanism at the base. A ball bearing system was used in the mobile-phone holder for maintaining vertical stationary position during steering wheel rotation. As the device is useful for mounting the smartphone on the steering wheel's hub, it might reduce driver distraction.