Navigation Points Research Articles

Homebody: Art Therapy and the Art of Possessions

This concept paper is proposing the inclusion of the home studio as an art making location for art therapy trainees who assemble personal belongings, within the context of their domestic lives. A collection of belongings can become an installation which brings together associations to identities in juxtaposition. The material culture of art therapy trainees, the objects which they live by, is a new contribution to art therapy pedagogy, as it designates the art of personal collections as a life archive (or accumulation) that is close at hand and already available. This collection of household artefacts teaches students how to honour the object collections of their clients, who are experts with lived experience of how to arrange legacies of gathering, consumption, inheritance, and gifted items. What follows is a proposal describing how home arrangements can signify personal associations to meaning and to an ethos of respecting what becomes us in terms of what we already own. The home studio is a key concept in this exploration of material culture as it relates to art therapy. This is a recognition of how a curation of possessions (where we place our belongings) is noteworthy as an act of agency and reflective practice within art therapy training and within the lives of those who participate in art therapy services. Recognising the significance of one’s lived-with objects, as navigation points of identity, values the culture of their curator who identifies the meaning of what they own as material assets.

Construction of maritime traffic network based on DBSCAN

Abstract Trajectory data is essentially a sequence of spatial points ordered by timestamps, usually with some descriptive information in addition to basic spatiotemporal information. This paper investigates how publicly available Automatic Identification System (AIS) data can be used to analyze maritime traffic and transform it into directed graphs for estimating potential destination points of trajectories. In the maritime field, analyzing and modeling maritime traffic is crucial for vessel safety and efficiency, creating pathways with waypoints and segments, and further forming traffic networks. Our approach incorporates a detailed analysis of the distribution characteristics of different types of navigational points, leading to the adoption of tailored clustering parameters and methods. This differentiation allows for a nuanced understanding of stationary points at docks and anchorages, as well as navigational changes along shipping routes. Using the DBSCAN algorithm, we successfully cluster similar waypoints, considering cluster density and shape without needing to predefine the cluster count.

Autonomous Navigation by Mobile Robot with Sensor Fusion Based on Deep Reinforcement Learning.

In the domain of mobile robot navigation, conventional path-planning algorithms typically rely on predefined rules and prior map information, which exhibit significant limitations when confronting unknown, intricate environments. With the rapid evolution of artificial intelligence technology, deep reinforcement learning (DRL) algorithms have demonstrated considerable effectiveness across various application scenarios. In this investigation, we introduce a self-exploration and navigation approach based on a deep reinforcement learning framework, aimed at resolving the navigation challenges of mobile robots in unfamiliar environments. Firstly, we fuse data from the robot's onboard lidar sensors and camera and integrate odometer readings with target coordinates to establish the instantaneous state of the decision environment. Subsequently, a deep neural network processes these composite inputs to generate motion control strategies, which are then integrated into the local planning component of the robot's navigation stack. Finally, we employ an innovative heuristic function capable of synthesizing map information and global objectives to select the optimal local navigation points, thereby guiding the robot progressively toward its global target point. In practical experiments, our methodology demonstrates superior performance compared to similar navigation methods in complex, unknown environments devoid of predefined map information.

Optimization of robotic path planning and navigation point configuration based on convolutional neural networks.

This study introduces a novel approach for enhancing robotic path planning and navigation by optimizing point configuration through convolutional neural networks (CNNs). Faced with the challenge of precise area coverage and the inefficiency of traditional traversal and intelligent algorithms (e.g., genetic algorithms, particle swarm optimization) in point layout, we proposed a CNN-based optimization model. This model not only tackles the issues of speed and accuracy in point configuration with Gaussian distribution characteristics but also significantly improves the robot's capability to efficiently navigate and cover designated areas with high precision. Our methodology begins with defining a coverage index, followed by an optimization model that integrates polygon image features with the variability of Gaussian distribution. The proposed CNN model is trained with datasets generated from systematic point configurations, which then predicts optimal layouts for enhanced navigation. Our method achieves an experimental result error of <8% on the test dataset. The results validate effectiveness of the proposed model in achieving efficient and accurate path planning for robotic systems.

RCAviz: Exploratory search in multi-relational datasets represented using relational concept analysis

The conceptual structures built with Formal Concept Analysis (FCA) and its extensions are appropriate constructs for supporting Exploratory Search (ES). FCA indeed classifies a set of objects described by Boolean attributes in a concept lattice which is prone to (intra-lattice) navigation. Relational Concept Analysis (RCA), for its part, classifies several sets of objects connected through multiple binary relationships by using logical operators (quantifiers) which can be approximate. The output is a set of interconnected concept lattices, thus adding inter-lattice navigation opportunities. In this paper, we describe the web platform RCAviz, which aims to support such intra- and inter-lattice navigation. The user can select a subset of objects and attributes as a starting point for navigation. Then RCAviz shows the associated concept and its close intra- and inter-lattice neighbors. The user can access to the objects and attributes introduced and inherited in a concept. They then can navigate, i.e. zoom and pan the current view, and move from one concept to another. Additional views show the previous and the next conceptual structures, as well as an history which allows the user to browse its navigation. A navigation example is shown on a real dataset to illustrate the potential of RCAviz for ES.

Mixed reality for spine surgery: a step into the future with a human cadaveric accuracy study.

Current application of mixed reality as a navigation aid in the field of spinal navigation points to the potential of this technology in spine surgery. Crucial factors for acceptance include intuitive workflow, system stability, reliability, and accuracy of the method. The authors therefore aimed to investigate the accuracy of the system in visualization of anatomical structures using mixed reality in the example of pedicles of the thoracic spine in a human cadaveric study. Potential difficulties and limitations are discussed. CT scans of a human cadaveric spinal column specimen were performed. After segmentation and import into the advanced HoloLens 2 software, the vertebrae were exposed. The vertebral arches were preserved on one side for a landmark-based surface registration, whereas pedicles were exposed on the other side in order to measure and evaluate deviation of the overlay holographs with regard to the exact anatomical structure. Accuracy was measured and statistically evaluated. In this work it was demonstrated that the overlay of the virtual 3D model pedicles with the real anatomical structures with anatomical landmark registration was within an acceptable surgical accuracy with the mean value of 2.1 mm (maximum 3.8 mm, minimum 1.2 mm). The highest accuracy was registered at the medial and lateral pedicle wall, and the measurement results were best in the region of the middle thoracic spine. The accuracy analysis for mixed reality (i.e., between the virtual and real anatomical situation of the thoracic spine) showed a very good agreement when focus was on the pedicles. This work is thus a rare proof of the precision of segmentation to the potential surgical area. The results encourage researchers to open up mixed reality technology in its development and application for spinal navigation.

Detection and Control Framework for Unpiloted Ground Support Equipment within the Aircraft Stand.

The rapid advancement in Unpiloted Robotic Vehicle technology has significantly influenced ground support operations at airports, marking a critical shift towards future development. This study presents a novel Unpiloted Ground Support Equipment (GSE) detection and control framework, comprising virtual channel delineation, boundary line detection, object detection, and navigation and docking control, to facilitate automated aircraft docking within the aircraft stand. Firstly, we developed a bespoke virtual channel layout for Unpiloted GSE, aligning with operational regulations and accommodating a wide spectrum of aircraft types. This layout employs turning induction markers to define essential navigation points, thereby streamlining GSE movement. Secondly, we integrated cameras and Lidar sensors to enable rapid and precise pose adjustments during docking. The introduction of a boundary line detection system, along with an optimized, lightweight YOLO algorithm, ensures swift and accurate identification of boundaries, obstacles, and docking sites. Finally, we formulated a unique control algorithm for effective obstacle avoidance and docking in varied apron conditions, guaranteeing meticulous management of vehicle pose and speed. Our experimental findings reveal an 89% detection accuracy for the virtual channel boundary line, a 95% accuracy for guiding markers, and an F1-Score of 0.845 for the YOLO object detection algorithm. The GSE achieved an average docking error of less than 3 cm and an angular deviation under 5 degrees, corroborating the efficacy and advanced nature of our proposed approach in Unpiloted GSE detection and aircraft docking.

Strategies for enrolment management in private universities in Ghana during the COVID-19 pandemic

COVID-19 has negatively affected the educational systems of most countries, most especially, private universities in Ghana. The study assessed the strategies adopted by private universities in Ghana for effective enrolment management practices during the COVID-19 pandemic. The descriptive qualitative research method was used to solicit primary data through interviews, document/system reviews, and observations from private universities. The study established that the private universities were able to deploy some strategies which had positive impact on enrolment management at the universities during the COVID-19 pandemic. Some of the strategies adopted include transition to the digital learning experience (using digital learning experience ZOOM, LOOM, Microsoft Team, and Google Meet); establishing an enrolment command center to coordinate and support the decision-making activities regarding students' access, recruitment, admission, persistence, retention, and completion during the COVID-19 pandemic and beyond; and the deployment of virtual tour services by improving tour services virtually and making navigation points on the university websites. Furthermore, the use of social media (Facebook, Twitter, YouTube, and WhatsApp) and financial aid served as strategies for effective enrollment management. The findings of the study provide empirical and practical support and information for university administrators, academic advisors, educators, and researchers on the strategies that they can deploy for effective enrolment management practices in private universities during epidemics or pandemics and beyond.

基于PID-模糊控制的单目视觉农田机器人视觉导航系统设计

This study proposes a monocular vision navigation control system based on PID-fuzzy control, which travels along the edge of the path. It collects path image information through monocular vision, identifies the path edge through image processing to determine the preview point, and uses a combination of PID and fuzzy control to design a controller to track the preview point for path navigation. Firstly, coordinate calibration and conversion were performed on the monocular camera, achieving coordinate conversion from the image coordinate system to the world coordinate system. The accuracy of the calibration results was verified through experiments. According to the navigation strategy of driving along the edge of the path, the world coordinate equation of the path edge is obtained through image processing technology, and the preview point tracked by the navigation system is determined. The navigation parameters are determined based on the position of the preview point. The PID fuzzy controller system designed in this study can switch different control methods based on the position of the preview point. Finally, an experimental verification was conducted on the monocular visual navigation system of the control system. The verification results showed that the average error of the navigation control system in tracking the path when driving in a straight line was 0.039 m, the average error when turning left was 0.079 m, and the average error when turning right was 0.121 m. The error range can meet the basic requirements of agricultural robot farmland operations. Research has shown that the navigation strategy based on PID-fuzzy joint controller to track the preview point along the path edge has a good effect on the visual navigation control system of agricultural robots. This study provides important reference value for the research and development of monocular visual navigation systems of agricultural robots.

Neural Networks for Navigation: From Connections to Computations

Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and reciprocally connected to motor control. This review summarizes recent progress in understanding these networks, focusing on studies in arthropods. One factor driving recent progress is the availability of the Drosophila connectome; however, it is increasingly clear that navigation depends on ongoing synaptic plasticity in these networks. Functional synapses appear to be continually reselected from the set of anatomical potential synapses based on the interaction of Hebbian learning rules, sensory feedback, attractor dynamics, and neuromodulation. This can explain how the brain's maps of space are rapidly updated; it may also explain how the brain can initialize goals as stable fixed points for navigation.

Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance the exploration capability and search accuracy of the algorithm by improving the velocity update method and inertia weight. Secondly, a particle initialization strategy is used to increase population diversity, and an addressing local optimum strategy is used to make the algorithm overcome the local optimum. Thirdly, a method of selecting navigation points is proposed to guide local path planning. The robot selects the appropriate navigation points as the target points for local path planning based on the position of the robot and the risk of collision with dynamic obstacles. Finally, an improved dynamic window approach (IDWA) is proposed by combining the velocity obstacle (VO) with the DWA, and the evaluation function of the DWA is improved to enhance trajectory tracking and dynamic obstacle avoidance capabilities. The simulation and experimental results show that IPSO has greater exploration capability and search accuracy; IDWA is more effective in trajectory tracking and dynamic obstacle avoidance; and the hybrid algorithm enables the robot to efficiently complete path planning in dynamic environments.

Analysing the benefits of trajectory deviations for planar trajectory optimisation

Aircraft traverse airspace sectors under the supervision of Air Traffic Controllers by following navigation points. The trajectories can deviate from these points under the supervision of controllers. However, the controllers have to consider various factors for managing air traffic which increases their workload. We aim to offline analyse the potential benefits of trajectory deviations, by tactically shifting navigation points for planar trajectory optimisation of multiple aircraft at an enroute sector level. Historic deviations of trajectories are considered for this analysis to implement them at practical level. Within nominal speed and turning rate of aircraft trajectories, 5.47% reduction in time cost and an annual fuel saving worth US$ 29.32 million has been estimated using shifts for an airspace sector.

Robot indoor navigation point cloud map generation algorithm based on visual sensing

Abstract At present, low-cost Red Green Blue Depth (RGB-D) sensors are mainly used in indoor robot environment perception, but the depth information obtained by RGB-D cameras has problems such as poor accuracy and high noise, and the generated 3D color point cloud map has low accuracy. In order to solve these problems, this article proposes a vision sensor-based point cloud map generation algorithm for robot indoor navigation. The aim is to obtain a more accurate point cloud map through visual SLAM and Kalman filtering visual-inertial navigation attitude fusion algorithm. The results show that in the positioning speed test data of the fusion algorithm in this study, the average time-consuming of camera tracking is 23.4 ms, which can meet the processing speed requirement of 42 frames per second. The yaw angle error of the fusion algorithm is the smallest, and the ATE test values of the algorithm are smaller than those of the Inertial measurement unit and Simultaneous-Localization-and-Mapping algorithms. This research algorithm can make the mapping process more stable and robust. It can use visual sensors to make more accurate route planning, and this algorithm improves the indoor positioning accuracy of the robot. In addition, the research algorithm can also obtain a dense point cloud map in real time, which provides a more comprehensive idea for the research of robot indoor navigation point cloud map generation.

A Scalable Tree Based Path Planning for a Service Robot

Path planning plays a vital role in a mobile robot navigation system. It essentially generates a shortest traversable path between two given points. There are many path planning algorithms proposed by researchers all over the world, however, there are very little work focussing on path planning for a service environment. The general assumption are either the environment is fully known or unknown. Both the cases will not be suitable for a service environment. Afully known environment will restrict further expansion in terms of number of navigation points and unknown environment would give an inefficient path. Unlike other environments, service environments have certain factors to be considered, like user friendliness, repeatability, scalability and portability which are very essential for a service robot. In this paper a simple, efficient, robust and environment independent path planning algorithm for an indoor mobile servicerobot is presented. Initially the robot is trained to navigate to all the possible destinations sequentially with minimal user interface which will ensure the robot knows partial paths in the environment. With the trained data the path planning algorithm maps all the logical paths between all the destinations, which helps in autonomous navigation. The algorithm is implemented and tested using 2D simulator Player/Stage. The proposed system is tested with two differentservice environment layouts and proved to have features like scalability, trainability, accuracy and repeatability. The algorithm is compared with various classical path planning algorithms and the results shows that proposed path planning algorithm is in par with the other algorithms in terms of accuracy and efficient path generation.

Prototyp kaskadowego autopilota okrętowego zaimplementowany w środowisku CPDev

Ship autopilots can be divided into conventional, only capable of maintaining a given heading, and advanced, which can additionally follow a „track” connecting the given navigation points along the ship’s route. The article presents the structure of the prototype autopilot of the ship implemented in the CPDev environment and the formulas allowing to determine the settings of the course controller (PID) and track controller (PI) in the cascade control. For each of them, individual design parameters were adopted to define the dynamics of the closed control loop. These rules were applied in the software of the autopilot prototype, created in cooperation with a Dutch company designing control and visualization systems for ships.

Flexible neuroendoscopy for endoscopic third ventriculostomy and fourth ventricular arachnoid cyst fenestration in an infant.

Arachnoid cysts of the fourth ventricle are rarely reported. Management options include CSF diversion, cyst fenestration, or cyst excision. Fenestration can be done via open microsurgical technique or endoscopically with or without simultaneous third ventriculostomy; and both rigid and flexible endoscopy have been used successfully. However, application of this treatment modality in pediatric patients is not well described. Therefore, to their knowledge, the authors report the first successful treatment of a fourth ventricular arachnoid cyst with a single frontal burr hole entry point for third ventriculostomy and fourth ventricular arachnoid cyst fenestration performed using flexible neuroendoscopy. The patient was a 13-month-old boy presenting with progressive macrocephaly. The authors review their technique, discuss special considerations when using this approach, and include an annotated intraoperative video for demonstration to help instruct and guide management. The authors demonstrate with an example that a single frontal burr hole entry point for flexible endoscopic third ventriculostomy and navigation through a dilated cerebral aqueduct for fourth ventricular arachnoid cyst fenestration is a viable treatment for symptomatic fourth ventricular arachnoid cysts in children.

A Rural Community Hospital’s Perioperative Surgical Home Model Compared to a Traditional Surgical System

Background: The Perioperative Surgical Home (PSH) was designed by the American Society of Anesthesiologists (ASA) to improve perioperative care through more coordinated and communicative decision-making. PSH has proven success in large urban health centers by reducing surgery cancellation, operating room time, length of stay (LOS), and readmission rates. Yet, only limited studies have assessed the impact of PSH on surgical outcomes in rural areas. Thus, this study compared surgical outcomes – LOS, discharge disposition, and 90-day readmission – at a rural hospital from cohorts before and after the implementation of PSH. Method: An initial model of a PSH system was initiated in a local rural hospital. This newly created clinical team and outpatient clinic retrospectively assessed total joint surgeries occurring from November 2017 to April 2018 (before PSH, n = 324) as well as from November 2018 to April 2019 (after PSH, n = 326). General linear models were used to understand the impact of PSH on rural surgical outcomes. Results: Implementing PSH had a positive impact on LOS (P-value &lt; 0.01), discharge disposition (P-value &lt; 0.01), and readmission (P-value = 0.03).Conclusion: Implementation of the PSH system in a rural, community hospital reduced LOS, 90-day readmission, and increased direct to home discharge. Increased clinical management standardization across internal and external stakeholders was achieved because the PSH clinic enabled a centralized navigation point for clinicians, patients, and their families to better communicate and coordinate care.

PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field

Path-keeping requires unmanned surface vehicles (USVs) to follow a planned path in autonomous navigation. It is essential for USVs to carry out autonomous tasks such as collecting various data of water quality and surrounding terrain for exploration and protection of water environments. However, due to obstacle avoidance and other factors such as wind, water waves, and dynamics of USVs, USVs usually deviate from the original planned path during autonomous navigation. This paper proposes a novel path-keeping algorithm based on the artificial potential field method (PK-APF) for USVs. To minimize the deviation between the actual path and the original planned path, the vertical distance and the virtual foot points of the current position of USVs to the original path (a line connecting the previous navigation point and the next navigation point) are calculated. When the vertical distance is larger than a threshold, we regard the vertical foot point as a virtual goal point to guide the USVs to navigate the original path in real-time to achieve high-precision path-keeping. Obstacle avoidance is simulated in the MATLAB and Virtual RobotX (VRX) simulators, and the influence of wind and water waves is considered in VRX. Experiments are conducted in typical scenarios and results show that PK-APF outperforms the traditional APF by at least 22%. The work provides an important basis for real-life environments. Substantial further work is planned for applying the method on a physical USV.

Preventing Tragedies Through Shared Learning

In December 1995, an American Airlines (AA) flight en route from Miami, Florida, to Cali, Colombia, crashed into the side of a mountain, killing 151 of 155 passengers on board. While, like most catastrophic incidents, a number of factors led to the crash, a fault in the flight management navigation system was key to the disaster. To save time, the cockpit crew had decided to take a different route into the Cali airport and correspondingly reprogrammed the flight management system. They entered “R” into the system, intent on heading for navigational point “ROZO.” However, the “R” also stood for ROMEO, a navigational point near Bogota, 170 miles from Cali.

Crowd navigation in an unknown and complex environment based on deep reinforcement learning

AbstractWe propose a virtual crowd navigation approach based on deep reinforcement learning to improve the adaptability of virtual crowds in an unknown and complex environment. To address the problem of local optimum or slow iteration or even failure to converge due to sparse rewards in complex environments, we integrate the curiosity‐driven mechanism, the key navigation points acquisition and the failure path penalty method in addition to combining long short‐term memory networks, dynamic obstacle collision prediction with proximal policy optimization algorithms, which realizes the crowd navigation in a complex environment. The experimental results show that the proposed approach can simulate the motions of virtual crowds in various dynamic and complex scenarios without the environment modeling. The use of continuous action space also ensures that the movement trajectories of the virtual crowds are more realistic and natural. Furthermore, our approach can provide analysis and demonstration tools for a variety of independent collaborations such as competition and cooperation of group intelligence in an open and dynamic environment.