Narrow Passages Research Articles

Morphy: A Compliant and Morphologically Aware Flying Robot

This study introduces a novel compliant and morphologically aware aerial robot called Morphy. The system is small (primary dimension 25.2 cm), lightweight (260 g), and agile (thrust‐to‐weight ratio of 3.3) while simultaneously integrating sensorized flexible joints in its arms. These elastic joints unlock the potential of experiencing flexible deformations, thus enabling the robot to resiliently withstand collisions at high speeds and squeeze through openings more narrow than its nominal dimensions. Extensive analysis of the flexible joints’ behavior under static loads and in free flight is conducted. Driven by the deformations that the robot can experience, adaptive control allocation exploiting real‐time angle deflection feedback from Hall‐effect sensors embedded in the joints is proposed and combined with fixed‐gain control. To demonstrate Morphy's performance, multiple experiments are conducted, including free flight trajectory tracking, impacts with the environment, and flying through narrow passages. As shown, the system can withstand collisions up to 7.6 m s−1 in drop tests and 3 m s−1 midflight while recovering back to hover. Finally, Morphy's capability to passively change its shape and squeeze through openings smaller than its nominal size is showcased both in the horizontal and in the vertical directions.

Development of a method for predicting hazardous ship trajectories under uncertainty of navigator actions

The object of the research is the automation processes in maritime navigation to ensure the safety of ship movement by predicting their trajectories in complex aquatic areas, such as narrow passages, straits, and ports. The research applied six key stages to create a comprehensive method for clustering and predicting ship trajectories based on ECDIS data. In the first stage, ship movement trajectories were constructed according to risk categories, using the LCSS and DTW algorithms to compare planned and actual trajectories. This allowed for the accurate identification of course deviations and the determination of potentially dangerous sections of the trajectory. The second stage implemented clustering using the DBSCAN and GMM algorithms. DBSCAN was used to identify the density of points in space, and GMM provided modeling of cluster probabilities, allowing for better risk zone determination. The third stage applied the Douglas-Peucker compression algorithm to reduce the number of points in the trajectories, which preserved key characteristics and optimized data processing. In the fourth stage, ship movement stability was assessed using the Fourier transform, which allowed the detection of high-frequency oscillations that may indicate movement instability caused by changes in course or speed. The fifth stage included fuzzy clustering of trajectories using the Gaussian Mixture Model (GMM), which allowed modeling the probabilities of dangerous trajectories, considering the uncertainty of navigational parameters. At the final stage, a multilayer neural network (MLP) was used to predict future points of ship trajectories. The model accurately predicted the ship's coordinates, enabling timely trajectory adjustments. Experimental results showed that the developed method increased the accuracy of ship trajectory prediction to 72–81 % and also significantly reduced the final error, ensuring effective risk management during complex navigation.

Wilson’s bottleneck

Planetary sustainability is in trouble, heading towards what pioneer of evolutionary biology, E.O Wilson, twenty-two years ago called a “bottleneck”. Created through the actions of humanity this is an increasingly narrow passage through which only some species can pass, and on which humans depend to provide the sources of re-radiation. What is lost is hard to impossible to restore. Keeping this passage as wide as possible is crucial, but the trends are not yet promising. At a time when those trends appear to be converging to a human and ecological crisis of planetary but finite duration, changed priorities are required whilst at the same time providing opportunity. In particular, strategies, such as experimental governance devised to act in the face of unknowns and uncertain knowledge provide a basis for action to hold open and successfully pass through the bottleneck, a goal which is of the highest importance for humans as we seek to achieve a sustainable future.

Geomorphic processes within the La Gomera-Tenerife Channel (Canary Islands): Decoding the interaction of bottom currents with seabed topography

The La Gomera-Tenerife Channel is a narrow passage between La Gomera and Tenerife Islands, i.e., two volcanic edifices of the Canary Archipelago (Atlantic Ocean). A geophysical study was conducted to identify the main geomorphic processes affecting the seabed and their interplay. In particular, submetric resolution bathymetric and side scan sonar backscatter data were collected in the southern sector of the Channel, down to 1200 m water depth. Their integrated analysis revealed a complex seabed morphology and a variety of morpho-sedimentary features, resulting from three main geomorphic processes: submarine volcanic activity, mass wasting (e.g., turbidity currents, small landslides and exotic blocks emplaced by a massive landslide event), and bottom currents activity. Bottom currents strongly reshaped the seabed into bedforms, confined drifts and moats. Although the flanks of volcanic islands are typically dominated by mass wasting and volcanic features, our results indicate that bottom current activity can be predominant in confined settings and around topographic features due to modification of flow patterns and enhancement of current flows.This study is the first to document volcanic, mass wasting and bottom current features within the La Gomera-Tenerife Channel. Furthermore, it provides insights on: i) morpho-sedimentary reconstructions of narrow passages between volcanic islands; ii) interplay among different geomorphic processes; iii) oceanographic reconstructions. The variety of geomorphic processes shaping the La Gomera-Tenerife Channel makes this area significant for high-resolution studies. Moreover, it provides new insights on poorly known processes, such as: the interaction of bottom currents with complex topography and bottom current morpho-dynamic in curved moats.

Comparison of Treatment Methods for Carpal Tunnel Syndrome: Surgical vs. Non-Surgical

Introduction Carpal tunnel syndrome (CTS) is a common condition that results from the compression of the median nerve within the carpal tunnel in the wrist. Both surgical and non-surgical treatments are used to manage CTS. While surgery is necessary in some cases, many patients can be treated effectively with conservative methods. This review compares the efficacy of surgical treatment for CTS against various conservative approaches. Aim The study aims to evaluate and compare the outcomes of surgical versus conservative treatments for CTS, focusing on symptom relief, patient outcomes, and long-term prognosis. State of Knowledge The carpal tunnel is a narrow passage in the wrist formed by carpal bones and the flexor retinaculum. Compression of the median nerve within this tunnel leads to CTS, which can be caused by anatomical variations or systemic conditions like diabetes. Symptoms include pain, numbness, and tingling in the hand, which may worsen at night or with repetitive movements. Summary (Conclusions) Surgical treatment, especially decompression, offers superior symptom relief and functional improvement but carries risks of complications. Conservative treatments like wrist immobilization and anti-inflammatory medications are effective for many patients, supporting their use as initial treatment. Further research is needed to optimize CTS management.

A path planner enabling second-order continuity across multiple motion modes of the generalized bicycle model

In mobile robotics, the generalized bicycle model is notable for its agility and diverse motion capabilities. However, current path planning methods often overlook its potential for multiple motion modes, leading to impractical paths and collision risks in complex environments. To address this, our study introduces a novel path planning algorithm that utilizes the generalized bicycle model and ensures second-order continuity, crucial for smooth and safe navigation. We establish rigorous second-order continuity conditions for seamless motion mode transitions and derive criteria for each mode. Our algorithm integrates these conditions with the rapidly exploring random tree concept to generate optimal paths that accommodate the model’s mode-changing capabilities. Through tests in challenging environments, our method demonstrates improved performance, especially in narrow passages, enhancing path planning safety and efficiency for mobile robots. This advancement significantly contributes to the field of robotic path planning by offering a more dynamic and reliable approach to autonomous navigation.

Nasogastric Tube-guided Nasotracheal Intubation for Narrow Nasal Passage in Orthognathic Surgery.

Nasogastric Tube-guided Nasotracheal Intubation for Narrow Nasal Passage in Orthognathic Surgery.

The Constraints of Narrow Channels: Implications for Multimodal Logistics

Narrow passages, whether man made, place limitations, on the efficiency of multimodal logistics activities by affecting factors like ship size, cargo capacity, speed of transit and operational effectiveness. This paper delves into the impact of these constraints on logistics and supply chain management emphasizing the importance of developing approaches to overcome these obstacles. Key areas of study include how ships behave in waterways, scheduling and simulating traffic flow establishing passing protocols and reducing risks related to grounding incidents. The study also explores how technological advancements like ships and sophisticated navigation systems can improve safety and operational efficiency. Furthermore, it examines the benefits of integrating water routes into the broader supply chain network to enhance logistics performance and sustainability. Ultimately this research aims to offer insights for optimizing logistics operations in channels with a focus, on enhancing strategies and reducing transit times.

GVP-RRT: a grid based variable probability Rapidly-exploring Random Tree algorithm for AGV path planning

In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5–69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length.

Oceanographic Characteristics in the Three International Indonesian Archipelago Sea Lanes (IASLs) Region: Implications for Underwater Acoustics System

Graphical Abstract Highlight Research The IASL-1 entry portal in the southern and northern regions shows the emergent SOFAR channels. The shadow zone and the existence of a SOFAR deep sound channel in the IASL-2 and IASL-3 routes can be triggered by the emergent “saddle” SVP pattern. The variability seasonally and interannually due to variations in seawater properties stratification plays an important role in SOFAR channel appearances in IASLs. The diverse oceanographic characteristics of IASLs necessitate the implementation of sustainable marine geospatial data. Abstract The Indonesian Maritime Continent (IMC) provides three international sea lanes, known as the Indonesian Archipelago Sea Lanes (IASLs), allowing ships to navigate across territorial waters between the Pacific and Indian Oceans and vice versa. Gaining knowledge about the distinct oceanographic characteristics of the three IASLs can offer valuable insight into maritime safety and sustainable marine resource management. This study aims to review oceanographic characteristics in IASL regions from available past studies to provide a comprehensive insight into the processes and dynamical oceanography in the IASL region and its implications for underwater acoustic patterns. The results of research found that the IASL-1 route is characterized by a shallow shelf passage with homogeneous sound velocity profile (SVP) but a deep and narrow entry portal in the southern and northern SOFAR channels. Seasonal reversal monsoonal wind-driven current dominates the circulation. The IASL-2 and IASL-3 routes convey a deep and narrow passage with complexity of sea-air interactions; they vary on seasonal and interannual time scales. These IASLs emerge with the “saddle” SVP, which can trigger the shadow zone and the existence of a SOFAR deep sound channel that varies seasonally and interannually due to variations in seawater properties stratification. The diverse oceanographic characteristics discussed above significantly influence the underwater object detection equipment, the planning time, and the strategies for underwater defense systems. Due to these implications, it is necessary to utilize marine geospatial database. Subsequently, these data may be utilized to facilitate policy-making and provide approximations for marine activities and management along the IASLs.

Properties and Model of Pore-Scale Methane Displacing Water in Hydrate-Bearing Sediments

The flow characteristics of methane and water in sedimentary layers are important factors that affect the beneficial exploitation of marine hydrates. To study the influencing factors of methane drive-off water processes in porous media, we constructed nonhomogeneous geometric models using MATLAB 2020a random distribution functions. We developed a mathematical model of gas–water two-phase flow based on the Navier–Stokes equation. The gas-driven water processes in porous media were described using the level-set method and solved through the finite element method. We investigated the effects of the nonhomogeneous structure of pore media, wettability, and repulsion rate on gas-driven water channeling. The nonhomogeneity of the pore medium is the most critical factor influencing the flow. The size of the throat within the hydrophilic environment determines the level of difficulty of gas-driven water flow. In regions with a high concentration of narrow passages, the formation of extensive air-locked areas is more likely, leading to a decrease in the efficiency of the flow channel. In the gas–water drive process, water saturation changes over time according to a negative exponential function relationship. The more hydrophilic the pore medium, the more difficult the gas-phase drive becomes, and this correlation is particularly noticeable at higher drive rates. The significant pressure differentials caused by the high drive-off velocities lead to quicker methane breakthroughs. Instantaneous flow rates at narrow throats can be up to two orders of magnitude higher than average. Additionally, there is a susceptibility to vortex flow in the area where the throat connects to the orifice. The results of this study can enhance our understanding of gas–water two-phase flow in porous media and help commercialize the exploitation of clean energy in the deep ocean.

Intelligent and biocompatible cellulose aerogels featured with high-elastic and fast-hemostatic for epistaxis and wound healing

Nasal tamponade is a commonly employed and highly effective treatment method for preventing nasal bleeding. However, the current nasal packing hemostatic materials exhibit some limitations, such as low hemostatic efficiency, the potential for causing secondary injury when removed from the nasal cavity, limited intelligence in their design, and an inability to promote the healing of nasal mucosa wounds. Herein, we report the fabrication of a smart cellulose aerogel through the covalent cross-linking of carboxymethyl cellulose (CMC) macromolecules, while incorporating one-dimensional cellulose nanofibers (CNF) and two-dimensional MXene as reinforcing network scaffolds and conductive fillers. The abundant hydrogen and ether bonds in aerogels make them possess high elasticity in both dry and wet states, which can be compressed 100 times at 90 % deformation with a stress loss of <10 % under water. The highly elastic aerogels can be filled into the narrow nasal passages, pressuring the capillaries and reducing the amount of bleeding. Moreover, the strong interface between aerogels and blood can promote red blood cell aggregation, platelet adhesion and activation, activate intrinsic coagulation pathway and accelerate blood coagulation, resulting in excellent hemostatic ability. Furthermore, the aerogels exhibit excellent hemocompatibility and cytocompatibility, making them suitable for wound healing and capable of fully healing wounds within 15 days. Notably, the presence of MXene causes the aerogels to form a conductive network when exposed to blood, enabling them to perform real-time hemostatic monitoring without removing the dressing. This innovative biomedical aerogel, prepared from natural materials, shows excellent potential for applications in rapid nasal hemostasis.

Geospatial analysis of contagious infection growth and cross-boundary transmission in non-vaccinated districts of North-East Indian states during the COVID-19 pandemic

During the initial phase of the COVID-19 pandemic, the Government of India implemented a nationwide lockdown, sealing borders across states and districts. The northeastern region of India, surrounded by three international borders and connected to mainland India by a narrow passage, faced particular isolation. This isolation resulted in these states forming a relatively closed population. Consequently, the availability of population-based data from Indian Council of Medical Research, tracked through national identification cards, offered a distinctive opportunity to understand the spread of the virus among non-vaccinated and non-exposed populations. This research leverages this dataset to comprehend the repercussions within isolated populations. The inter-district variability was visualized using geospatial analysis. The patterns do not follow any established grounded theories on disease spread. Out of 7.1 million total data weekly 0.35 million COVID-19-positive northeast data was taken from April 2020 to February 2021 including "date, test result, population density, area, latitude, longitude, district, and state" to identify the spread pattern using a modified reaction-diffusion model (MRD-Model) and Geographic Information System. The analysis of the closed population group revealed an initial uneven yet rapidly expanding geographical spread characterized by a high diffusion rate α approximately 0.4503 and a lower reaction rate β approximately 0.0256, which indicated a slower growth trajectory of case numbers rather than exponential escalation. In the latter stages, COVID-19 incidence reached zero in numerous districts, while in others, the reported cases did not exceed100. The MRD-Model effectively captured the disease transmission dynamics in the abovementioned setting. This enhanced understanding of COVID-19 spread in remote, isolated regions provided by the MRD modelling framework can guide targeted public health strategies for similar isolated areas. This study is Funded by Indian Council of Medical Research (ICMR).

Joining SUMO and Unreal Engine to Create a Bespoke 360 Degree Narrow Passage Driving Simulator

The use of simulators is widespread in driver behavioural research. The ability of driving simulators to achieve the high levels of behavioural fidelity desired by behavioural researchers is argued to be resultant of the physical fidelity of the simulator. Whilst attempts to maximise the physical fidelity of simulators have often been focused on the hardware capabilities of the simulator, the software of the simulator has been argued to be as important. This is because the software of a simulator controls the intelligence and the heterogeneity of the behaviours of the simulated vehicles, as well as the quality of the graphics of the simulation. Despite the importance of intelligent simulated agents, previous driving simulator studies have tended to simplify the behaviours of simulated vehicles and the scenarios that are presented to participants. This is particularly true of simulator studies investigating the decision-making of drivers at narrow passages, a relatively unregulated but hazardous situation in which two opposing vehicles must negotiate how to safely pass through a road narrowing, in which the interactive nature of the interaction has often been neglected. Following a review of the requirements for a representative narrow passage driving simulator, it is argued that co-simulation, an approach which combines multiple simulator types to create a global simulation, provides the best approach to creating intelligent simulated agents within an immersive environment for narrow passage behavioural research. As such, the development of a simulator for narrow passage behavioural research that combines SUMO and Unreal Engine is described. In particular, the development of a novel narrow passage behavioural model within SUMO that utilises previous behavioural findings is highlighted. To this end, it is argued that this approach facilitates higher levels of behavioural fidelity for narrow passage interaction studies and provides a framework for the investigation of other driver behaviours.

An automatic robot for ultrasonic partial discharge detection of gas-insulated switchgear

Purpose Gas-insulated switchgear (GIS) stands as a pivotal component in power systems, susceptible to partial discharge occurrences. Nevertheless, manual inspection proves labor-intensive, exhibits a low defect detection rate. Conventional inspection robots face limitations, unable to perform live line measurements or adapt effectively to diverse environmental conditions. This paper aims to introduce a novel solution: the GIS ultrasonic partial discharge detection robot (GBOT), designed to assume the role of substation personnel in inspection tasks. Design/methodology/approach GBOT is a mobile manipulator system divided into three subsystems: autonomous location and navigation, vision-guided and force-controlled manipulator and data detection and analysis. These subsystems collaborate, incorporating simultaneous localization and mapping, path planning, target recognition and signal processing, admittance control. This paper also introduces a path planning method designed to adapt to the substation environment. In addition, a flexible end effector is designed for full contact between the probe and the device. Findings The robot fulfills the requirements for substation GIS inspections. It can conduct efficient and low-cost path planning with narrow passages in the constructed substation map, realizes a sufficiently stable detection contact and perform high defect detection rate. Practical implications The robot mitigates the labor intensity of grid maintenance personnel, enhances inspection efficiency and safety and advances the intelligence and digitization of power equipment maintenance and monitoring. This research also provides valuable insights for the broader application of mobile manipulators in diverse fields. Originality/value The robot is a mobile manipulator system used in GIS detection, offering a viable alternative to grid personnel for equipment inspections. Comparing with the previous robotic systems, this system can work in live electrical detection, demonstrating robust environmental adaptability and superior efficiency.

Voltage mapping in subcellular nanodomains using electro-diffusion modeling.

Voltage distribution in sub-cellular micro-domains such as neuronal synapses, small protrusions, or dendritic spines regulates the opening and closing of ionic channels, energy production, and thus, cellular homeostasis and excitability. Yet how voltage changes at such a small scale in vivo remains challenging due to the experimental diffraction limit, large signal fluctuations, and the still limited resolution of fast voltage indicators. Here, we study the voltage distribution in nano-compartments using a computational approach based on the Poisson-Nernst-Planck equations for the electro-diffusion motion of ions, where inward and outward fluxes are generated between channels. We report a current-voltage (I-V) logarithmic relationship generalizing Nernst law that reveals how the local membrane curvature modulates the voltage. We further find that an influx current penetrating a cellular electrolyte can lead to perturbations from tens to hundreds of nanometers deep, depending on the local channel organization. Finally, we show that the neck resistance of dendritic spines can be completely shunted by the transporters located on the head boundary, facilitating ionic flow. To conclude, we propose that voltage is regulated at a subcellular level by channel organization, membrane curvature, and narrow passages.

Dynamic path planning of autonomous bulldozers using activity-value-optimised bio-inspired neural networks and adaptive cell decomposition

Autonomous bulldozers encounter critical challenges in dynamic path planning in complex construction environments such as dynamic obstacles and narrow passages. Bio-inspired neural networks (BINN) are commonly employed for real-time path planning in dynamic environments. However, the algorithm is not feasible enough to calculate the positive input of neighbour cells, thus resulting in unreasonable distribution of activity values in the obstacle-dense regions. Additionally, the use of regular grids results in excessive computation. These drawbacks cause the algorithm to plan irrational paths and reduce computational efficiency. This study proposed an activity-value-optimised BINN and adaptive cell decomposition for the dynamic path planning of autonomous bulldozers. First, an adaptive cell decomposition method was proposed to model a highly intricate and constantly evolving environment, resulting in a reduction in the computational workload of the BINN. Second, the calculation of the activity value is optimised by enhancing the shunt equation of the BINN, which considers both obstacles and grid levels. This improvement enabled more reasonable distribution results. The smoothness of dynamic path planning is refined by incorporating the turning radius, which can be more effectively integrated with local path planning. Compared with the original BINN algorithm and other baseline algorithms, a practical large-scale hydropower project application demonstrated that the proposed method can effectively reduce the number of computational iterations, enrich the travel direction, and improve path quality while ensuring dynamic obstacle avoidance.

Stable pushing in narrow passage environment using a modified hybrid A* algorithm

Stable pushing in narrow passage environment using a modified hybrid A* algorithm

DE3D-NURBS: A differential evolution-based 3D path-planner integrating kinematic constraints and obstacle avoidance

This work presents a novel path planner, DE3D-NURBS, for 3D path planning considering the maximum and minimum climb/dive angle and the maximum curvature imposed by a robot, such as an autonomous fixed-wing aircraft. To deal with this constrained problem, we propose a Differential Evolution (DE) based algorithm, assuming the partitioning of feasible and infeasible individuals and a novel mutation operator. This solution can provide a short differentiable smooth path, directly parameterized by a kth-order Non-Uniform Rational B-Spline (NURBS) curve, a generalization of B-spline curves. This representation provides more flexibility to satisfy essential kinematic constraints and to take into account obstacles and topography. This path can guide an Unmanned Aerial Vehicle (UAV) through a set of desired robot configurations (position and orientation) in 3D space. The simulation results provide a more comprehensive insight into the capabilities of the proposed planner to generate feasible paths, considering simple scenarios in a free obstacle environment and also complex scenarios. Such scenarios include topographic environments, narrow passages such as a tunnel, and a solution for closed-loop missions in a modular approach. We also provide a comparative benchmark with other nature-inspired algorithms. These results serve as a baseline for evaluating the effectiveness of the proposed DE algorithm in complex and challenging case studies.

Should I Just Stick to the Wall? Evaluating the Social Acceptance and Preferred Driving Side of Wall Following

The need for safe, predictable, and reliable robot navigation is fundamental for mobile robots to move around in home and office environments. Shortest-path navigation is a popular robot navigation method that uses the most efficient path to get to the desired goal. This behaviour is not always easy to interpret, understand, and avoid in a congested hallway. Instead, more predictable navigation methods, such as a robot following a wall, can help increase social acceptance and help avoid the robot crossing the pedestrian path. If a robot follows along a wall, a key variable to consider is the preferred driving side of the robot (left or right) in areas such as in narrow passages, and its perceived impact on social acceptance. This international user study (n = 143) involved an online video-based test to compare robot evaluation and social acceptance for two types of mobile navigation (Wall-Following and Shortest Path), including the preferred driving side for Wall-Following. A Fetch robot navigated from start to goal position in a series of indoor scenarios with a pedestrian. Select scenarios included a hallway, doorway, and intersection. Independent Sample T-Tests results found that Wall-Following was rated significantly higher than Shortest Path for being perceived as more comfortable and predictable, regardless of robot driving side. The preference for the driver side of the robot did not match the country of residence, nor did it have a significant impact on robot ratings. There were significant interaction effects for comfort, safety and predictable scores across two timepoints. Given the popularity of Shortest Path navigation, the findings indicate that this approach might not be the most appropriate for human settings. Additional investigation into Wall-Following behaviours is recommended for social acceptance, even if the method compromises the efficiency of the robot to acheive its objective.