Navigation Performance Research Articles

Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration

Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic measurement errors that characterize such devices. While different techniques are available to model and remove the deterministic errors, there has been considerable research over the past years with respect to modelling the stochastic errors which have complex structures. In order to do the latter, different replicates of these error signals are collected and a model is identified and estimated based on one of these replicates. While this procedure has allowed to improve navigation performance, it has not yet taken advantage of the information coming from all the other replicates collected on the same sensor. However, it has been observed that there is often a change of error behaviour between replicates which can also be explained by different (constant) external conditions under which each replicate was taken. Whatever the reason for the difference between replicates, it appears that the model structure remains the same between replicates but the parameter values vary. Assuming the model structure has been identified, in this work we therefore consider and study the properties of different approaches that allow to combine the information from all replicates considering this phenomenon, confirming their validity both in simulation settings and also when applied to real inertial sensor error signals. By taking into account parameter variation between replicates, this work highlights how these approaches can improve the average navigation precision as well as obtain reliable estimates of the uncertainty of the navigation solution.

Topology optimization design of dual-channel metasurface structure with controllable amplitude of retroreflection and mirror reflection

For oblique incident electromagnetic waves, the generation of double reflection channels including retroreflection channel is of great significance in improving target recognition performance and navigation performance. In double channel reflection, controlling the ratio of retroreflection to specular reflection is the key to realizing reflection power distribution. In order to realize the control of the proportion of the reflected power in each channel, a topology optimization method for designing the reflective metasurface microstructure with dual channel is proposed in this paper. The implementation mechanism is investigated, physical model of metasurface with dual reflection channel including retroreflection channel is established, and the topology optimization model of metasurface microstructure with specific power ratio of the retroreflection to specular reflection is established. As a numerical example, a dual channel metasurface reflector with a 1∶1 ratio of retroreflection power to specular reflection power is designed for a 10 GHz plane wave in the TE mode with a pitch angle of –30°. The designed metasurface exhibits strong directionality in the retroreflective direction, and the reflection amplitudes in both directions are similar. The retroreflective metasurface with the maximum retroreflection ratio is designed. The retroreflection ratio of the designed metasurface is 0.093. There is no specular reflection or other singular reflection, and the strong reflection on the metasurface is concentrated at –30°. The ratio of the main beam power to the total reflection power is 0.900. The simulated and experimental results verify the feasibility of the proposed method.

A Vision-Based Navigation System With Markerless Image Registration and Position-Sensing Localization for Oral and Maxillofacial Surgery

The quality of oral and maxillofacial surgery (OMS) significantly depends on the accuracy of surgical navigation. In this article, a vision-based markerless surgical navigation system is developed to overcome the shortcomings in the currently available technologies. Registration methods both for patient and surgical instrument tracking are improved to increase the navigation performance. For patient-image registration, we propose an efficient texture-less pose estimation method using only shape information. An innovative strategy is developed to effectively reject the outliers and improve the pose accuracy, which is the first attempt at introducing geometric matching information to guide PnP calculation. For surgical instrument tracking, a position-sensing marker is used to achieve robust and convenient instrument localization with high accuracy. Experiments were conducted on the 3-D-printed maxilla and mandible models to evaluate the navigation performance. Evaluation results validate the effectiveness of the proposed pose estimation method in improving the pose accuracy for texture-less teeth. Besides, it is revealed that the position-sensing marker can be localized with high accuracy even under nonideal visibility conditions, which expands the motion range of the instrument and decreases the size of the tool. The entire system has a sufficiently small target registration error (TRE). These experimental results have verified that the proposed surgical navigation system can provide practical guidance for OMS with satisfactory accuracy.

Rapidly Exploring Random Trees for Autonomous Navigation in Observable and Uncertain Environments

This paper proposes the use of a small differential robot with two DC motors, controlled by an ESP32 microcon-troller, that implements the Rapidly Exploring Random Trees algorithm to navigate from an origin point to a destination point in an unknown but observable environment. The motivation behind this research is to explore the use of a low-cost, versatile and efficient robotic platform for autonomous navigation in complex environments. This work presents a practical and cost-effective solution that can be easily replicated and implemented in various scenarios such as search and rescue, surveillance, and industrial automation. The proposed robotic platform is equipped with a set of sensors and actuators that allow it to observe the environment, estimate its position, and move through it. The Rapidly Exploring Random Trees algorithm is implemented to generate a path from an origin to a destination point, avoiding obstacles and adjusting the robot’s motion accordingly. The implementation of this algorithm enables the robot to navigate through complex environments with high efficiency and reliability, making it a suitable solution for a wide range of applications. The results obtained through simulations and experiments show that the proposed robotic platform and algorithm achieve high performance and accuracy in autonomous navigation, even in complex environments.

The Impact of Applying Deming's Model on Institutional Performance in Air Navigation: A Study on Quality and Customer Satisfaction

This study investigates the influence of applying Deming's model on the institutional performance of air navigation, with a specific focus on the concept of quality and its alignment with the present and future needs and expectations of customers. The research was conducted within the Department of Air Traffic in Kuwait, specifically within the Air Navigation Services Company, utilizing a sample of 206 employees. To achieve its objectives, the researcher developed a questionnaire as the primary research tool, designed to assess the relationship between the application of the Deming model and institutional performance. The study reveals several significant findings. Firstly, there exists a statistically significant relationship at a significance level of α≤0.05 between the practices of the Deming model and the promotion of administrative innovation within the Air Navigation Company in Kuwait. Additionally, the research demonstrates a statistically significant effect at the α≤0.05 level of Deming model practices on internal institutional innovation. Moreover, a similar statistically significant effect at α≤0.05 was observed for Deming Model Management practices on creating value within the Air Navigation Company in Kuwait. Among the key recommendations arising from this study is the importance of assessing the quality of air navigation services from the perspective of customers, particularly in light of the application of the Deming model. Understanding customer perceptions and expectations can play a pivotal role in further enhancing the quality and effectiveness of air navigation services. This research contributes valuable insights into the application of Deming's model in the context of air navigation, shedding light on its potential benefits in terms of innovation, quality, and value creation.

Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning.

Vision-and-language navigation (VLN) asks an agent to follow a given language instruction to navigate through a real 3D environment. Despite significant advances, conventional VLN agents are trained typically under disturbance-free environments and may easily fail in real-world navigation scenarios, since they are unaware of how to deal with various possible disturbances, such as sudden obstacles or human interruptions, which widely exist and may usually cause an unexpected route deviation. In this paper, we present a model-agnostic training paradigm, called Progressive Perturbation-aware Contrastive Learning (PROPER) to enhance the generalization ability of existing VLN agents to the real world, by requiring them to learn towards deviation-robust navigation. Specifically, a simple yet effective path perturbation scheme is introduced to implement the route deviation, with which the agent is required to still navigate successfully following the original instruction. Since directly enforcing the agent to learn perturbed trajectories may lead to insufficient and inefficient training, a progressively perturbed trajectory augmentation strategy is designed, where the agent can self-adaptively learn to navigate under perturbation with the improvement of its navigation performance for each specific trajectory. For encouraging the agent to well capture the difference brought by perturbation and adapt to both perturbation-free and perturbation-based environments, a perturbation-aware contrastive learning mechanism is further developed by contrasting perturbation-free trajectory encodings and perturbation-based counterparts. Extensive experiments on the standard Room-to-Room (R2R) benchmark show that PROPER can benefit multiple state-of-the-art VLN baselines in perturbation-free scenarios. We further collect the perturbed path data to construct an introspection subset based on the R2R, called Path-Perturbed R2R (PP-R2R). The results on PP-R2R show unsatisfying robustness of popular VLN agents and the capability of PROPER in improving the navigation robustness under deviation.

Collision-free Navigation with Unknown and Heterogeneous Social Preferences

In this study, we address cooperative navigation using multiple agents with social preferences such as egoism and altruism. Previous studies have proposed collision-free navigation with social preferences under the assumption that the social preferences of other agents are available. However, if some agents cannot express their social preferences to others, this method is unavailable. In this study, we consider the cooperative navigation problem where agents do not know the social preference of the others. Our social preference estimation scheme can realize provably collision-free navigation. Agents estimate social preferences based on their trajectories. Then, they modify existing Voronoi cells by using their positions and the estimated social preferences of other agents. Moving within the modified Voronoi cells enables all agents to avoid collision with others, even if the actual social preferences are unknown. Through a simulation study for various distributions of social preferences, we show that the proposed method can avoid collision and achieve navigation performance close to that of the existing method using the actual social preferences.

Navigation of Autonomous Vehicles using Reinforcement Learning with Generalized Advantage Estimation

This study proposes a reinforcement learning ap-proach using Generalized Advantage Estimation (GAE) for autonomous vehicle navigation in complex environments. The method is based on the actor-critic framework, where the actor network predicts actions and the critic network estimates state values. GAE is used to compute the advantage of each action, which is then used to update the actor and critic networks. The approach was evaluated in a simulation of an autonomous vehicle navigating through challenging environments and it was found to effectively learn and improve navigation performance over time. The results suggest GAE as a promising direction for further research in autonomous vehicle navigation in complex environments.

Orientation behavior in men and women: The relationship between gender stereotype, growth mindset, and spatial self-efficacy

People's beliefs about their navigation ability could be important for their navigation performance. This correlational study aimed to investigate how growth mindset, gender stereotype, and spatial self-efficacy are related to orientation behaviors (use of GPS and exploration tendency). Moreover, we investigated gender differences in beliefs and orientation behaviors. A sample of 609 participants (329 women) completed self-reported measures assessing their mindset, gender stereotype, spatial self-efficacy, sense of direction, and orientation behaviors. The structural equation models show that a growth mindset and gender stereotype are related to exploration tendency and GPS use through the mediation of spatial self-efficacy in both men and women. Concerning gender stereotype a different pattern of correlation emerged in men and women. Moreover, significant differences in favor of men emerged in self-efficacy, sense of direction, and orientation behaviors. Overall, these findings support the conviction that men and women's beliefs about their spatial abilities are related to the ways they behave in the environment.

Application of shaken lattice interferometry based sensors to space navigation

High–sensitivity shaken lattice interferometry (SLI) based sensors have the potential to provide deep space missions with the ability to precisely measure non–gravitational perturbing forces. This work considers the simulation of the OSIRIS-REx mission navigation in the vicinity of Bennu with the addition of measurements from onboard SLI–based accelerometers. The simulation is performed in the Jet Propulsion Laboratory’s (JPL) Mission Analysis, Operations and Navigation Toolkit (MONTE) and incorporates OSIRIS-REx reconstructed trajectory and attitude data from the Navigation and Ancillary Information Facility (NAIF) database. The use of the reconstructed data from NAIF provides realistic true dynamical errors and JPL’s MONTE software allows for a high–fidelity simulation of an integrated trajectory for the filter. The navigation performance and reduction of tracking and complex modelling enabled by the onboard SLI–based sensor are presented for two orbital phases of the OSIRIS–REx mission. Overall, the results show that the addition of SLI–based accelerometer measurements improves navigation performance, when compared to a radiometric tracking only configuration. In addition, results demonstrate that highly–precise accelerometer measurements can effectively replace at least one day of DSN passes over a three–day period, thereby reducing tracking requirements. Furthermore, it is shown that lower–fidelity surface force modeling and parameter estimation is required when using onboard SLI–based accelerometers.

Research on SLAM Path Planning of ROS Robot based on Lidar

In order to improve the autonomous navigation performance of robot, an automatic mapping and path planning method based on ROS robot is proposed, which uses two-dimensional laser radar to collect the surrounding environment. Gmapping algorithm is used for automatic optimization and mapping, Class A algorithm is used for global path planning, and DWA method is used to realize the local obstacle avoidance function of the robot. Experiments show that this method can realize the automatic generation and automatic navigation of the robot.

Innovation-Based Fault Detection and Exclusion Applied to Ultra-WideBand Augmented Urban GNSS Navigation

Due to their ability to provide a worldwide absolute outdoor positioning, Global Navigation Satellite Systems (GNSS) have become a reference technology in terms of navigation technologies. Transportation-related sectors make use of this technology in order to obtain a position, velocity, and time solution for different outdoor tasks and applications. However, the performance of GNSS-based navigation is degraded when employed in urban areas in which satellite visibility is not good enough or nonexistent, as the ranging signals become obstructed or reflected by any of the numerous surrounding objects. For these situations, Ultra-Wideband (UWB) technology is a perfect candidate to complement GNSS as a navigation solution, as its anchor trilateration-based radiofrequency positioning resembles GNSS’s principle. Nevertheless, this fusion is vulnerable to interferences affecting both systems, since multiple signal-degrading error sources can be found in urban environments. Moreover, an inadequate location of the augmenting UWB transmitters can introduce additional errors to the system due to its vulnerability to the multipath effect. Therefore, the misbehavior of an augmentation system could lead to unexpected and critical faults instead of improving the performance of the standalone GNSS. Accordingly, this research work presents the performance improvement caused by the application of Fault Detection and Exclusion methods when applied to a UWB-augmented low-cost GNSS system in urban environments.

On line-of-sight navigation for deep-space applications: A performance analysis

Line-of-Sight (LoS) navigation is an optical navigation technique that exploits the direction to visible celestial bodies, obtained from an onboard imaging system, to estimate the position and velocity of a spacecraft. The directions are fed to an estimation filter, where they are matched with the actual position of the observed bodies, retrieved from onboard stored ephemerides. As LoS navigation represents a really promising option for the next-generation deep-space spacecraft, the objective of this work is to provide new insights into the performance. First, the information matrix is analyzed to show the influence of the geometry between the spacecraft and the observed planet(s). Then, a Monte Carlo approach is used to investigate the influence of measurement error (ranging from 0.1 to 100 arcsec), and tracking frequency (ranging from four observations per day to one observation every two days). The effect on navigation performance is quantified by two indicators. The first is the 3D position and velocity Root-Mean-Square-Errors, computed once the estimation is considered to be steady-state. The second is the convergence time, which quantifies the required time for the estimation to reach the steady-state behaviour. The simulation is based on a set of four planets, which do not follow the common heliocentric dynamics but rotate around the Sun with the same (distance-independent) angular velocity of the spacecraft. This approach allows the separation of scenario-dependent behaviours from navigation intrinsic properties, as the same relative geometry between observer and observed objects is maintained during the whole simulation. The results provide a useful guide for the next-generation autonomous navigation system, for both the definition of hardware requirements and the design of an appropriate navigation strategy. Considerations are then applied to Near-Earth Asteroid fly-by mission scenarios for the definition of the navigation strategy and hardware requirements. It is shown the importance of relative angles between the spacecraft and the planets. In the single-planet observation scenario, when the angle between the position vectors of the spacecraft and planet approaches a null value, the estimation error decreases. In the double-planets observation scenario, when the separation angle between the two LoS directions gets close to 90°, the estimation error decreases. The main influence on the performance is driven by the measurement error, which with current technologies is shown to be able to provide a position error in the order of a few hundred kilometers, while with a lower measurement error (0.1 arcsec) it would be possible to have a position error below 100 km. Finally, it is demonstrated that tracking frequency plays a secondary role in the performance, and only influences tangibly the convergence time.

The Influences of Different Sensory Modalities and Cognitive Loads on Walking Navigation: A Preliminary Study

External cognitive burden has long been considered an important factor causing pedestrian navigation safety problems, as pedestrians in navigation inevitably acquire external information through their senses. Therefore, the influences of different types of sensory modalities and cognitive loads on walking navigation are worthy of in-depth investigation as the foundation for improving pedestrians’ safety in navigation. This study investigated users’ performance in visual, auditory, and tactile navigation under different cognitive loads by experimental simulation. Thirty-six participants were recruited for the experiment. A computer program simulating walking navigation was used, and three different cognitive task groups were set up. Participants’ reaction times and performances were recorded during the experiment, and a post-test questionnaire was administered for evaluation purposes. According to the tests, the following points are summarized. First, visual navigation performed the best in load-free conditions, which was significantly faster than auditory navigation and tactile navigation, but the difference between the latter two was not significant. There was a significant interaction between navigation types and cognitive load types. Specifically, in the condition without load, reaction time in auditory navigation was significantly slower than those in visual navigation and tactile navigation. In the condition with auditory load, reaction time in visual navigation was significantly faster than those in auditory navigation and tactile navigation. In the condition with visual load, there were no significant differences among the three navigations.

Unilateral hippocampal lesions and the navigational performance of homing pigeons as revealed by GPS-tracking

The left and right hippocampal formation (HF) of the avian brain have been reported to control some different aspects of homing in pigeons. In the current study, we employed GPS-tracking technology and unilateral HF lesions to further explore what if any aspects of a pigeon’s homing flight might be under dominant control by either the left or right HF. Pigeons were released from three locations prior to any experimental manipulation and released repeatedly from the same three sites as sham-lesioned control, right HF-lesioned and left HF-lesioned treatment groups. Analyses of homing performance and virtual vanishing bearings revealed no effect of either lesion treatment. A more in-depth analysis of path efficiency during the initial decision-making, en route and near home phases of a homing flight also revealed no effect of either lesion treatment. A last analysis on the learning and memory for positions along a previously flown route, a proxy for investigating the development of route fidelity, also revealed no effect of either unilateral lesion. However, independent of treatment group, some statistically significant effects were observed with respect to changes in performance across training and the different release sites. The current study revealed no detectable difference between the left and right HF-lesioned pigeons with respect to several navigational parameters of a homing flight. Although in need of supporting experimentation, the results are consistent with the hypothesis that both the left and right HF are similarly able to support several aspects of homing pigeon navigation.

THE EFFECT OF MOTION FORMATION ON COOPERATIVE NAVIGATION

The effect of formation movement on the performance of cooperative navigation is investigated in this paper. First, the inertial navigation system of each agent with a certain accuracy is modeled and simulated. Initial results showed that the navigation error of each agent increased individually over time, and this problem is more severe for agents equipped with a weaker system. Cooperative navigation is implemented for the agents to resolve this problem. It is shown that the total navigation errors are improved by observing and participating the relative distance between the agents. Various simulations and experimental tests using two real agents supported this assertation. The performance of cooperative navigation can be improved further through appropriate formation. Proper formations are investigated and evaluated through simulations. The collective covariance matrix is employed to form an objective function using an extended Kalman filter (EKF). This function has been minimized using Newton’s method, which could be the solution for the formation. The simulation results show that better accuracy can be achieved by applying the optimal formation trajectory.

Empowering episodic memory through a model-based egocentric navigational training.

Recent works have proposed that spatial mechanisms in the hippocampal-entorhinal system might have originally developed to represent distances and positions in the physical space and successively evolved to represent experience and memory in the mental space (Bellmund et al. 2018; Bottini and Doeller 2020). Within this phylogenetic continuity hypothesis (Buzsáki and Moser 2013), mechanisms supporting episodic and semantic memory would have evolved from egocentric and allocentric spatial navigation mechanisms, respectively. Recent studies have described a specific relationship between human performance in egocentric navigation and episodic memory (Committeri et al. 2020; Fragueiro et al. 2021), representing the first behavioral support to this hypothesis. Here, we tested the causal relationship among egocentric navigation and both episodic and semantic components of declarative memory. We conducted two experiments on healthy young adults: in the first experiment, participants were submitted to a navigational training based on path integration, while in the second experiment, participants completed a control training based on visual-perceptual learning. Performance in a set of memory tasks assessing episodic, semantic and short-term memory was compared among the pre- vs. post-training sessions. The results indicated a significant improvement of the episodic memory but not of the semantic or the short-term memory performance following the navigational training. In addition, no modulations of performance across the three memory tasks were observed following the control perceptual training. Our findings provide brand-new evidence of a potential causal association between mechanisms of egocentric navigation and episodic memory, thereby further supporting the phylogenetic continuity hypothesis between navigation and memory mechanisms as well as offering new insights about possible clinical applications of navigational trainings for memory functions/dysfunctions.

System noise variance matrix adaptive Kalman filter method for AUV INS/DVL navigation system

The Inertial Navigation System (INS)/Doppler Velocity Log (DVL) navigation system is capable of locating the Autonomous Underwater Vehicle (AUV) in real-time. However, inherent errors of sensors, especially the inertial measurement unit (IMU) measurement noise changes during navigation, make the system noise variance matrix unknown and time-dependent, resulting in inaccurate Kalman filter estimates of velocity and reducing the accuracy of the navigation system. To solve this problem, this paper proposes an adaptive system noise variance matrix Kalman Filter method for AUV INS/DVL navigation system. In this method, the statistical characteristics of the IMU measurement noise are estimated by distinguishing the AUV motion information obtained by the IMU measurement and the IMU measurement noise by the frequency domain analysis method. The Kalman filter velocity estimation accuracy is improved by adaptively adjusting the system noise variance matrix based on the IMU measurement noise. Six different sets of trajectory experiments were used to validate the effectiveness and applicability of the algorithm proposed in this paper. The experimental results show that the improved KF algorithm improves the positioning accuracy by an average of 2.29‰ navigation distance compared with the traditional KF algorithm, which proves that the proposed method can improve navigation performance.

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents.

Several maze shapes are used to test spatial navigation performance and behavioral phenotypes. Traditionally, each experiment requires a unique maze shape, thus requiring several separate mazes in different configurations. The maze geometry cannot be reconfigured in a single environment to accommodate scalability and reproducibility. The reconfigurable maze is a unique approach to address the limitations, allowing quick and flexible configurations of maze pathways in a repeatable manner. It consists of interlocking pathways and includes feeders, treadmills, movable walls, and shut-off sensors. The current protocol describes how the reconfigurable maze can replicate existing mazes, including the T-shaped, plus-shaped, W-shaped, and figure-eight mazes. Initially, the T-shaped maze was constructed inside a single experimental room, followed by modifications. The rapid and scalable protocol outlined herein demonstrates the flexibility of the reconfigurable maze, achieved through the addition of components and behavioral training phases in a stepwise manner. The reconfigurable maze systematically and precisely assesses the performance of multiple aspects of spatial navigation behavior.

Asteroid Approaching Orbit Optimization Considering Optical Navigation Observability

An increasing demand for investigating the solar system has envisioned many asteroid exploration missions. Optical navigation is the principal technology to determine the explorer’s orbit relative to an asteroid when approaching it in an exploration mission. The approaching orbit determines the observing conditions and consequently affects the optical navigation accuracy. Aiming at improving the optical navigation performance, this article proposes the approach orbit optimization method considering optical navigation observability. The defect of the optical navigation along the line-of-sight (LOS) direction when the explorer moves along the conventional approaching orbit is revealed. The quantitative index of the navigation performance is designed by deriving the Fisher information matrix (FIM). The orbit optimization problem is constructed by involving the elements of FIM and fuel consumption together into the optimization index, and setting up the necessary engineering constraints. Numerical simulations show that compared with the conventional fuel-optimal cases, the navigation along the LOS direction is greatly improved. The navigation accuracy is improved from 86 to 99% and an additional 1.62 kg of fuel is consumed. The proposed method can effectively improve the navigation performance and has promising applications in asteroid approach trajectory design in the future asteroid exploration missions.