Navigation Approach Research Articles

An operational concept flying GLS approaches using satellite-based augmentation systems

A new converter technology allows suitably equipped aircraft to use data provided by the satellite-based augmentation system in receivers originally designed for the ground-based augmentation landing system. For these aircraft, that system enables a lower decision altitude and, hence, improved access to airports. To make this technology usable, air crews require an operational concept and the flight crew has to be presented with the appropriate information in the form of approach charts. Two different possibilities for an operational concept were developed and the corresponding approach charts created. One option is a modified area navigation approach chart, to which the specific information is added. The other chart is an entirely separate procedure for the approach. These two options were tested with airline pilots in an Airbus A320 full-flight training simulator. During the simulator flights, aircraft performance data was recorded and the participants filled in questionnaires regarding workload and quality of the operational concept. The results show different behavior during the intercept of the final course, but all approaches remained within the required limits. The questionnaires revealed that the workload is higher during the area navigation variant and that all participants prefer the separate ground-based augmentation landing system variant.

Free Choice Navigation in the Real World: Giving Back Freedom to Wayfinders

In recent years, there has been collected evidence suggesting that increased usage of navigation assistance systems has a harmful effect on spatial cognition, including spatial knowledge acquisition. Previously, we proposed a potential remedy called Free Choice Navigation (simulation study). This novel navigation approach aims to provide the user with more freedom while navigating, and simultaneously give fewer navigation instructions. This approach also aims at increasing engagement with the environment and fostering spatial knowledge acquisition. We conducted a human-subject study with 48 participants comparing Free Choice Navigation with the widespread Turn-by-Turn approach on the outskirts of Vienna, Austria. The study showed the viability of our navigation system in real urban environments, providing fewer navigation instructions compared to the Turn-by-Turn approach (relative to the number of traversed junctions). Fewer instructions and forced engagement with the environment, however, did not result in differences concerning spatial knowledge acquisition, but interestingly, Free Choice Navigation users (without a map) could extract spatial configuration information similarly well as Turn-by-Turn users having a map. Moreover, we provide evidence that people are interested in learning more about their environments and are willing to walk longer routes to achieve it.

Performance of LiDAR-SLAM-based PNT with initial poses based on NDT scan matching algorithm

To achieve higher automation level of vehicles defined by the Society of Automotive Engineers, safety is a key requirement affecting navigation accuracy. We apply Light Detection and Ranging (LiDAR) as a main auxiliary sensor and propose LiDAR-based Simultaneously Localization and Mapping (SLAM) approach for Positioning, Navigation, and Timing. Furthermore, point cloud registration is handled with 3D Normal Distribution Transform (NDT) method. The initial guess of the LiDAR pose for LiDAR-based SLAM comes from two sources: one is the differential Global Navigation Satellite System (GNSS) solution; the other is Inertial Navigation System (INS) and GNSS integrated solution, generated with Extended Kalman Filter and motion constraints added, including Zero Velocity Update and Non-Holonomic Constraint. The experiment compares two initial guesses for scan matching in terms navigation accuracy. To emphasize the importance of a multi-sensor scheme in contrast to the conventional navigation method using the stand-alone system, the tests are conducted in both open sky area and GNSS signal block area, the latter might cause Multipath and Non-Line-Of-Sight effects. To enhance the navigation accuracy, the Fault Detection and Exclusion (FDE) mechanism is applied to correct the navigation outcome. The results show that the application of NDT and FDE for INS/GNSS integrated system can not only reach where-in-lane level navigation accuracy (0.5 m), but also enable constructing the dynamic map.

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.

Toward Safer and Energy Efficient Global Trajectory Planning of Self-Guided Vehicles for Material Handling System in Dynamic Environment

For a sustainable operation of multiple Self-Guided Vehicles (SGVs) in a dynamic manufacturing environment, it is essential to guarantee collision-free and efficient navigation to the autonomous mobile platforms and safety to the surrounding subjects. To prevent from navigation failures, an SGV must avoid conflicts that constrain itself to abruptly brake or stop to avoid collisions. These inefficient conflicts result from unexpected changes in the configuration space or due to nearby unforeseen obstacle. In this paper, a navigation approach is proposed to adapt the global trajectory in order to reduce conflict occurrence while limiting energy consumption of the mobile platform. To generate such trajectory, first the collision risks are characterized using an objective risk perception parameter, the Time-To-Collision TTC, that rely on the kinematics of the egoSGV and the neighboring obstacles. Next, weighted Kernel Density Estimation (wKDE) defines the spatial distribution of conflict severity in configuration space. The defined zones are incorporated as a conflict layer in the global map. Then, a global trajectory planner algorithm is used to weigh between the length cost and conflict cost. Finally, to test the proposed solution, a simulation is performed in a factory-like environment, then an experiment is conducted with a real SGV. In comparison with the state-of-the-art geometrical path planning method, the results show that the proposed approach reduces navigation failures by up to 52%, while reducing the trajectory execution time by around up to 10 %. Also, the smoothness of the executed motion allowed to reduce energy consumption by over 12%.

CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation.

Brain-computer interface (BCI) is a typical direction of integration of human intelligence and robot intelligence. Shared control is an essential form of combining human and robot agents in a common task, but still faces a lack of freedom for the human agent. This paper proposes a Centroidal Voronoi Tessellation (CVT)-based road segmentation approach for brain-controlled robot navigation by means of asynchronous BCI. An electromyogram-based asynchronous mechanism is introduced into the BCI system for self-paced control. A novel CVT-based road segmentation method is provided to generate optional navigation goals in the road area for arbitrary goal selection. An event-related potential of the BCI is designed for target selection to communicate with the robot. The robot has an autonomous navigation function to reach the human selected goals. A comparison experiment in the single-step control pattern is executed to verify the effectiveness of the CVT-based asynchronous (CVT-A) BCI system. Eight subjects participated in the experiment, and they were instructed to control the robot to navigate toward a destination with obstacle avoidance tasks. The results show that the CVT-A BCI system can shorten the task duration, decrease the command times, and optimize navigation path, compared with the single-step pattern. Moreover, this shared control mechanism of the CVT-A BCI system contributes to the promotion of human and robot agent integration control in unstructured environments.

Multilayer Model-Aided INS Based on Real-Time Corrected Vehicle Dynamics Model

Vehicle dynamics model-aided inertial navigation system (INS) is an effective approach for autonomous navigation when the Global Navigation Satellite System fails, However, the accuracy of the vehicle dynamics model (VDM) will decrease, or even diverge, in low-speed and high-dynamic situations, which leads to the performance degradation of the whole system. The traditional VDM-aided INS uses the velocity information from VDM to estimate and correct INS errors, but most previous research ignored that the acceleration and angular velocity from INS are more accurate than VDM. To reach the full potential of INS and VDM, this paper proposed a three-layer structure for the VDM-aided INS. In the proposed method, the navigation information is divided into the sensor layer, system layer I, and system layer II. In the sensor layer, VDM is corrected by INS through the strong tracking cubature Kalman filter, and the chi-square test is adopted to avoid redundant computation. In the system layer I, INS is aided by the corrected velocity information from VDM. In the system layer II, the position is updated with the velocity and attitude that are corrected in the system layer I. As the result, VDM and INS can be corrected in different layers simultaneously, and the accuracy and robustness of the whole system can be improved. The simulation experiment proved that, with the sensor layer filter, the velocity errors of VDM can be reduced by 35% in low-speed and high-dynamic situations, and reduced by 20% in the normal situation. Then, a 31-minute field test shows that the maximum horizontal position error of the proposed method is 241.8 m, while those of two traditional VDM-aided INS methods are more than 500 m.

Evaluation of UKF-Based Fusion Strategies for Autonomous Underwater Vehicles Multisensor Navigation

In the underwater domain, guaranteeing accurate navigation for an autonomous underwater vehicle (AUV) is a complex but fundamental task to be achieved. As a matter of fact, only by ensuring a correct AUV localization, it is possible to accomplish surveillance, monitoring, and inspection missions. Most of the navigation filters for AUVs are based on Bayesian estimators, such as the Kalman filter, the extended Kalman filter (EKF), or the unscented Kalman filter (UKF), and employ different instruments, including the Doppler velocity log to perform the localization task. Recently, the use of payload sensors, such as cameras or forward-looking SONARs, in navigation-aiding has arisen as an interesting research field in the attempt to reduce the localization error drift. Such sensors, if used simultaneously, can provide multiple observations, which can be combined in a Kalman filtering framework to increase navigation robustness against noise sources. Navigation techniques that employ multiple devices can provide a high improvement of the estimation quality, but they can also cause an increase in terms of computational load. Consequently, strategies that can represent a tradeoff between these two conflicting goals have to be investigated. In this contribution, two different frameworks have been implemented and compared: on the one hand, a centralized iterative UKF-based navigation approach and, on the other hand, a sensor fusion framework with parallel local UKFs. The sequential (or iterated) UKF, where the correction step is iteratively performed for each available measurement, belongs to the first class of filters. The federated and the consensus-based decentralized UKFs can be categorized as the second class and they differ in the employed fusion strategy. Experimental navigation data obtained during sea trials performed at Vulcano Island, Messina, Italy has been used for offline validation. The results analysis focuses on both the navigation quality and the filter robustness against the reduction of the available measurements.

Image-Guided Precision Treatments.

Chemotherapy, radiotherapy, and surgery are traditional cancer treatments, which usually produce unpredictable side effects and potential risks to normal healthy organs/tissues. Thus, safe and reliable treatment strategies are urgently required for maximized therapeutic efficiency to lesions and minimized risks to healthy regions. To this end, molecular imaging is responsible to undertake a specific targeting therapy. Besides that, the image guidance as a precision visualized approach for real-time in situ evaluations as well as an intraoperational navigation approach has earned attractive attention in the past decade. Along with the rapid development of multifunctional micro-/nanobiomaterials, versatile cutting-edge and advanced therapy strategies (e.g., thermal therapy, dynamic therapy, gas therapy, etc.) have been achieved and greatly contributed to the image-guided precision treatments in every aspect. Therefore, this chapter aims to discuss about both traditional and advanced cancer treatments and especially to elucidate the important roles that visualized medicine has been playing in the image-guided precision treatments.

An ORB-SLAM3-based Approach for Surgical Navigation in Ureteroscopy

ABSTRACT In ureteroscopy a flexible ureteroscope is used to inspect the kidneys and the ureters. In this context, we investigate robotic solutions that leverage recent developments from Simultaneous Localization and Mapping (SLAM) to provide a 3D map of the organ to be explored and the ureteroscope’s pose. With this aid, the surgeoncan navigate through the organ more precisely. Additionally, for the final organ inspection, the risk of missing certain regions of the organ is minimized. In this paper, we propose a visual, monocular SLAM system based on ORB-SLAM3 that is able to estimate the pose of the ureteroscope’s tip. In order to fulfill this task, we introduce two pre-processing steps. The first one aims at increasing the contrast of the image and the second one helps to avoid detecting features in non-desired regions (e.g. reflections). Additionally, we extend ORB-SLAM3 with A-KAZE and SuperPoint features and compare their performances to ORB features. The proposed method is evaluated in two experiments. The first experiment shows that we are able to estimate the trajectory of the ureteroscope and the map of a synthetic kidney with low errors. The second experiment shows that the method also gives promising results in cystoscopy.

Autonomous vehicle path planning for smart logistics mobile applications based on modified heuristic algorithm

In this study, a heuristic algorithm is used to find an optimal route for smart logistics loading and unloading applications. Various environments, such as traditional building blocks, satellite images, terrain environments, and Google map environments are developed by converting into a binary occupancy grid and used to optimize the viable path in the smart mobile logistics application. The proposed autonomous vehicle (AV) route planning navigation approach is to forecast the AV’s path until it detects an imminent obstacle, at which point it should turn to the safest area before continuing on its route. To demonstrate the path navigation results of proposed algorithms, a navigational model is developed in the MATLAB/Simulink 2D virtual environment. The particle swarm optimization (PSO) method, the Bat search algorithm, and its proposed variants are used to identify a smooth and violation-free path for a given application environment. The proposed variants improve the algorithm’s effectiveness in finding a violation-free path while requiring less time complexity by using cubic spline curve interpolation and its improved constriction factor. Extensive simulation and benchmark validation results show the proposed standard PSO has a significantly shorter violation-free path, quick convergence rate and takes less time to compute the distance between loading and unloading environment locations than the cooperative coevolving PSO, Bat algorithm, or modified frequency Bat algorithms.

Differential effects in young and aged rats' navigational accuracy following instantaneous rotation of environmental cues.

Successful navigation depends critically upon two broad categories of spatial navigation strategies that include allocentric and egocentric reference frames, relying on external or internal spatial information, respectively. As with older adults, aged rats show robust impairments on a number of different spatial navigation tasks. There is some evidence that these navigation impairments are accompanied by a bias toward relying on egocentric over allocentric navigation strategies. To test the degree to which young and aged animals utilize these two navigation approaches, a novel behavioral arena was used in which rats are trained to traverse a circular track and to stop at a learned goal location that is fixed with respect to a panorama of visual cues projected onto the surrounding walls. By instantaneously rotating the cues, allocentric and egocentric reference frames were put in direct and immediate conflict and goal navigation performance was assessed with respect to how accurately young and aged animals were able to utilize the rotated cues. Behavioral data collected from nine young and eight aged animals revealed that both age groups were able to update their navigation performance following cue rotation. Contrary to what was expected, however, aged animals showed more accurate overall goal navigation performance, stronger allocentric strategy use, and more evident changes in behavior in response to cue rotation compared to younger animals. The young rats appeared to mix egocentric and allocentric strategies for ICR task solution. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Review on Path Planning and Obstacle Avoidance Algorithms for Autonomous Mobile Robots

Mobile robots have been widely used in various sectors in the last decade. A mobile robot could autonomously navigate in any environment, both static and dynamic. As a result, researchers in the robotics field have offered a variety of techniques. This paper reviews the mobile robot navigation approaches and obstacle avoidance used so far in various environmental conditions to recognize the improvement of path planning strategists. Taking into consideration commonly used classical approaches such as Dijkstra algorithm (DA), artificial potential field (APF), probabilistic road map (PRM), cell decomposition (CD), and meta-heuristic techniques such as fuzzy logic (FL), neutral network (NN), particle swarm optimization (PSO), genetic algorithm (GA), cuckoo search algorithm (CSO), and artificial bee colony (ABC). Classical approaches have limitations of trapping in local minima, failure to handle uncertainty, and many more. On the other hand, it is observed that heuristic approaches can solve most real-world problems and perform well after some modification and hybridization with classical techniques. As a result, many methods have been established worldwide for the path planning strategy for mobile robots. The most often utilized approaches, on the other hand, are offered below for further study.

LiDAR-Only Crop Navigation for Symmetrical Robot.

This paper presents a navigation approach for autonomous agricultural robots based on LiDAR data. This navigation approach is divided into two parts: a line finding algorithm and a control algorithm. The paper proposes several line finding algorithms (based on PEARL/Ruby approach) that extract lines from a LiDAR data set. Once the lines have been processed from the data set, a control algorithm filters these lines and, using a fuzzy controller, generates the wheel speed commands to move the robot among the crop rows. This navigation approach was tested using a simulator built on ROS middle-ware and Gazebo (the source codes of the simulation are available on GitHub). The results of the simulated experiments show that the proposed approach performs well for a large range of crop configurations (with or without considering weeds, with or without holes in the crop rows…).

Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model.

Mobile robot navigation has been studied for a long time, and it is nowadays widely used in multiple applications. However, it is traditionally focused on two-dimensional geometric characteristics of the environments. There are situations in which robots need to share space with people, so additional aspects, such as social distancing, need to be considered. In this work, an approach for social navigation is presented. A multi-layer model of the environment containing geometric and topological characteristics is built based on the fusion of multiple sensor information. This is later used for navigating the environment considering social distancing from individuals and groups of people. The main novelty is combining fast marching square for path planning and navigation with Gaussian models to represent people. This combination allows to create a continuous representation of the environment from which smooth paths can be extracted and modified according to dynamically captured data. Results prove the practical application of the method on an assistive robot for navigating indoor scenarios, including a behavior for crossing narrow passages. People are efficiently detected and modeled to assure their comfort when robots are around.

PACNav: a collective navigation approach for UAV swarms deprived of communication and external localization

This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving the decentralized collective navigation of unmanned aerial vehicle (UAV) swarms. The technique is based on the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. As global and concurrent information of all swarm members is not available in natural swarms, these systems use local observations to achieve the desired behavior. Similarly, PACNav relies only on local observations of the relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems. We introduce the novel concepts of path persistence and path similarity that allow each swarm member to analyze the motion of other members in order to determine its own future motion. PACNav is based on two main principles: (a) UAVs with little variation in motion direction have high path persistence, and are considered by other UAVs to be reliable leaders; (b) groups of UAVs that move in a similar direction have high path similarity, and such groups are assumed to contain a reliable leader. The proposed approach also embeds a reactive collision avoidance mechanism to avoid collisions with swarm members and environmental obstacles. This collision avoidance ensures safety while reducing deviations from the assigned path. Along with several simulated experiments, we present a real-world experiment in a natural forest, showcasing the validity and effectiveness of the proposed collective navigation approach in challenging environments. The source code is released as open-source, making it possible to replicate the obtained results and facilitate the continuation of research by the community.

Knowledge points navigation based on three-way concept lattice for autonomous learning

In view of the ability of the three-way concept lattices to describe the correlations and hierarchical relationships among the knowledge points, this paper proposes a knowledge point navigation approach based on the three-way concept lattices for autonomous learning. First, this paper constructs the formal context of the exercises-knowledge points according to the corresponding relationship between the exercises and knowledge points; then, the three-way concept lattice generation algorithms are used to obtain the AE-concept lattice and OE-concept lattice; finally, this paper provides students with an effective autonomous learning path and knowledge points navigation Hasse diagram to achieve efficient guidance of autonomous learning.

Wheeled Mobile Robots: State of the Art Overview and Kinematic Comparison Among Three Omnidirectional Locomotion Strategies

In the last decades, mobile robotics has become a very interesting research topic in the field of robotics, mainly because of population ageing and the recent pandemic emergency caused by Covid-19. Against this context, the paper presents an overview on wheeled mobile robot (WMR), which have a central role in nowadays scenario. In particular, the paper describes the most commonly adopted locomotion strategies, perception systems, control architectures and navigation approaches. After having analyzed the state of the art, this paper focuses on the kinematics of three omnidirectional platforms: a four mecanum wheels robot (4WD), a three omni wheel platform (3WD) and a two swerve-drive system (2SWD). Through a dimensionless approach, these three platforms are compared to understand how their mobility is affected by the wheel speed limitations that are present in every practical application. This original comparison has not been already presented by the literature and it can be used to improve our understanding of the kinematics of these mobile robots and to guide the selection of the most appropriate locomotion system according to the specific application.

Bayesian fault-tolerant protection level for multi-constellation navigation from integrity perspective

The multi-constellation is anticipated to enhance navigation performance by providing abundant satellites in view. However, with this comes the necessity of considering simultaneous fault events in integrity risk assessment when applying the receiver autonomous integrity monitoring. Meanwhile, the integrity monitoring efficacy remains vulnerable to the uncertainty of prior fault probability in multi-constellation navigation. In this regard, this paper proposes a Bayesian approach for the multi-constellation navigation at the user segment to strengthen the evaluation robustness of integrity risk. We bound the Bayesian posterior probability of fault hypotheses that comprise multiple concurrent faults (satellite or constellation faults) without assuming an optimistic prior fault distribution. This posterior probability bound leads to an associated Bayesian integrity risk. Following that, a fault-tolerant estimate accounting for multiple faults and nominal biases is derived to minimize the Bayesian integrity risk. Finally, we establish a Bayesian fault-tolerant protection level to bound the position estimate error and assess the integrity availability, resisting the uncertainty in the prior fault probability. Results validate the robustness improvement the proposed Bayesian approach fulfills compared with the advanced receiver autonomous integrity monitoring in four cases. In addition, the Bayesian approach can lower the protection level at the cost of an offset from the least-square estimate while maintaining the same position accuracy.

Influence of experience on dental implant placement: an in vitro comparison of freehand, static guided and dynamic navigation approaches

PurposeThis study aimed to investigate the performance of novice versus experienced practitioners for placing dental implant using freehand, static guided and dynamic navigation approaches.MethodsA total of 72 implants were placed in 36 simulation models. Three experienced and three novice practitioners were recruited for performing the osteotomy and implant insertion with freehand, surgical guide (pilot-drill guidance) and navigation (X-Guide, X-Nav technologies) approaches. Each practitioner inserted 4 implants per approach randomly with a 1-week gap to avoid memory bias (4 insertion sites × 3 approaches × 6 practitioners = 72 implants). The performance of practitioners was assessed by comparing actual implant deviation to the planned position, time required for implant placement and questionnaire-based self-confidence evaluation of practitioners on a scale of 1–30.ResultsThe navigation approach significantly improved angular deviation compared with freehand (P < 0.001) and surgical guide (P < 0.001) irrespective of the experience. Surgical time with navigation was significantly longer compared to the freehand approach (P < 0.001), where experienced practitioners performed significantly faster compared to novice practitioners (P < 0.001). Overall, self-confidence was higher in favor of novice practitioners with both guided approaches. In addition, the confidence of novice practitioners (median score = 26) was comparable to that of experienced practitioners (median score = 27) for placing implants with the navigation approach.ConclusionsDynamic navigation system could act as a viable tool for dental implant placement. Unlike freehand and static-guided approaches, novice practitioners showed comparable accuracy and self-confidence to that of experienced practitioners with the navigation approach.Graphical