Efficient Navigation Research Articles

Microscopic augmented reality calibration with contactless line-structured light registration for surgical navigation.

The use of AR technology in image-guided neurosurgery enables visualization of lesions that are concealed deep within the brain. Accurate AR registration is required to precisely match virtual lesions with anatomical structures displayed under a microscope. The purpose of this work was to develop a real-time augmented surgical navigation system using contactless line-structured light registration, microscope calibration, and visible optical tracking. Contactless discrete sparse line-structured light point cloud is utilized to construct patient-image registration. Microscope calibration optimization with dimensional invariant calibrator is employed to enable real-time tracking of the microscope. The visible optical tracking integrates a 3D medical model with surgical microscope video in real time, generating an augmented microscope stream. The proposed patient-image registration algorithm yielded an average root mean square error (RMSE) of 0.78 ± 0.14mm. The pixel match ratio error (PMRE) of the microscope calibration was found to be 0.646%. The RMSE and PMRE of the system experiments are 0.79 ± 0.10mm and 3.30 ± 1.08%, respectively. Experimental evaluations confirmed the feasibility and efficiency of microscope AR surgical navigation (MASN) registration. By means of registration technology, MASN overlays virtual lesions onto the microscopic view of the real lesions in real time, which can help surgeons to localize lesions hidden deep in tissue.

Enhancing the Travel Experience for People with Visual Impairments through Multimodal Interaction: NaviGPT, A Real-Time AI-Driven Mobile Navigation System.

Assistive technologies for people with visual impairments (PVI) have made significant advancements, particularly with the integration of artificial intelligence (AI) and real-time sensor technologies. However, current solutions often require PVI to switch between multiple apps and tools for tasks like image recognition, navigation, and obstacle detection, which can hinder a seamless and efficient user experience. In this paper, we present NaviGPT, a high-fidelity prototype that integrates LiDAR-based obstacle detection, vibration feedback, and large language model (LLM) responses to provide a comprehensive and real-time navigation aid for PVI. Unlike existing applications such as Be My AI and Seeing AI, NaviGPT combines image recognition and contextual navigation guidance into a single system, offering continuous feedback on the user's surroundings without the need for app-switching. Meanwhile, NaviGPT compensates for the response delays of LLM by using location and sensor data, aiming to provide practical and efficient navigation support for PVI in dynamic environments.

Efficient navigation of a robotic fish swimming across the vortical flow field

Efficient navigation of a robotic fish swimming across the vortical flow field

Allocentric and egocentric spatial representations coexist in rodent medial entorhinal cortex

Successful navigation relies on reciprocal transformations between spatial representations in world-centered (allocentric) and self-centered (egocentric) frames of reference. The neural basis of allocentric spatial representations has been extensively investigated with grid, border, and head-direction cells in the medial entorhinal cortex (MEC) forming key components of a ‘cognitive map’. Recently, egocentric spatial representations have also been identified in several brain regions, but evidence for the coexistence of neurons encoding spatial variables in each reference frame within MEC is so far lacking. Here, we report that allocentric and egocentric spatial representations are both present in rodent MEC, with neurons in deeper layers representing the egocentric bearing and distance towards the geometric center and / or boundaries of an environment. These results demonstrate a unity of spatial coding that can guide efficient navigation and suggest that MEC may be one locus of interactions between egocentric and allocentric spatial representations in the mammalian brain.

TRG-Planner: Traversal Risk Graph-Based Path Planning in Unstructured Environments for Safe and Efficient Navigation

TRG-Planner: Traversal Risk Graph-Based Path Planning in Unstructured Environments for Safe and Efficient Navigation

A Comparative Perspective of the Regulatory Review Process in Gulf Cooperation Council (GCC) Countries

The Gulf Cooperation Council (GCC) countries have significant growth in the pharmaceutical and medical device industries. This review aims to provide an overview of the current regulatory landscape in GCC countries, highlighting challenges, opportunities, and best practices. This study evaluates the regulatory submission review timeline in GCC countries. Although their structures, approaches, and procedures differ significantly, regulatory bodies in industrialized and developing nations ensure patients have access to safe and effective medications. To provide a unified approach, the study's objective was to assess the regulatory frameworks of the Gulf Cooperation Council (GCC), which includes Saudi Arabia, the United Arab Emirates (UAE), Bahrain, Kuwait, Oman, Qatar, and Yemen. The analysis encompasses the three regulatory review phases: submission, evaluation, and authorization. This review explores each GCC country's regulatory frameworks and processes, highlighting regulatory submission requirements, timelines, and authorization processes. The analysis also includes critical tasks and requirements like good manufacturing practices (GMP) status verification, analysis of samples, validation procedures, patent protection considerations, applicant response time, regulatory fees, and pricing procedures. This review also examines the interactions between sponsors and regulatory authorities. To enable more effective and efficient navigation of these intricate systems, this thorough research attempts to offer a deeper understanding of the regulatory review procedures in the GCC nations.

A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment

Unmanned aerial vehicles (UAVs) play a crucial role in various applications, including environmental monitoring, disaster management, and surveillance, where timely data collection is vital. However, their effectiveness is often hindered by the limitations of wireless sensor networks (WSNs), which can restrict communications due to bandwidth constraints and limited energy resources. Thus, the operational context of the UAV is intertwined with the constraints on WSNs, influencing how they are deployed and the strategies used to optimize their performance in these environments. Considering the issues, this paper addresses the challenge of efficient UAV navigation in constrained environments while reliably collecting data from WSN nodes, recharging the sensor nodes’ power supplies, and ensuring the UAV detours around obstacles in the flight path. First, an integer linear programming (ILP) optimization problem named deadline and obstacle-constrained energy minimization (DOCEM) is defined and formulated to minimize the total energy consumption of the UAV. Then, a deep reinforcement learning-based algorithm, named the DQN-based UAV detouring algorithm, is proposed to enable the UAV to make intelligent detour decisions in the constrained environment. The UAV must finish its tour (data collection and recharging sensors) without exceeding its battery capacity, ensuring each sensor has the minimum residual energy and consuming energy for transmitting and generating data, after being recharged by the UAV at the end of the tour. Finally, simulation results demonstrate the effectiveness of the proposed DQN-based UAV detouring algorithm in data collection and recharging the sensors while minimizing the total energy consumption of the UAV. Compared to other baseline algorithm variants, the proposed algorithm outperforms all of them.

Vision-Based Deep Reinforcement Learning of Unmanned Aerial Vehicle (UAV) Autonomous Navigation Using Privileged Information

The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions. Our approach combines deep reinforcement learning with privileged learning to overcome the impact of observation data corruption caused by partial observability. We leverage an asymmetric Actor–Critic architecture to provide the agent with privileged information during training, which enhances the model’s perceptual capabilities. Additionally, we present a multi-agent exploration strategy across diverse environments to accelerate experience collection, which in turn expedites model convergence. We conducted extensive simulations across various scenarios, benchmarking our DPRL algorithm against state-of-the-art navigation algorithms. The results consistently demonstrate the superior performance of our algorithm in terms of flight efficiency, robustness and overall success rate.

Robotics Navigation Based on YOLO and Depth Camera

In recent years, significant advancements have been made in robotics, yet challenges remain in navigation, particularly for autonomous vehicles and bipedal robots. This paper provides a comprehensive review of three critical components in robotic navigation: YOLO neural networks, depth cameras, and A* path planning. Existing studies often address these aspects independently, lacking an integrated approach. Here, we systematically summarize and analyze the effectiveness of these techniques in navigation tasks. Through a literature review of recent publications, we compare and categorize various methods to assess trends in YOLO research and explore potential for integrated research in navigation. Furthermore, we analyze the strengths and limitations of each method in dynamic environments. The findings suggest that while YOLO and depth camera-based systems excel in real-time object detection and spatial awareness, they face challenges related to light sensitivity and high computational demands. Future research directions are proposed to enhance adaptability in complex environments, improve efficiency, and support cost-effective navigation solutions in robotics.

An Improved D* Lite-Based Dynamic Route Planning Algorithm for Ships in Arctic Waters

The ice conditions in Arctic waters are complex and variable, requiring ships to dynamically adjust their routes to ensure safe and efficient navigation. Traditional dynamic path planning algorithms struggle to address the extensive variability of Arctic ice conditions. To tackle this issue, this paper improves the D* Lite algorithm by leveraging the gradual and convergent nature of Arctic ice condition changes. The original algorithm’s local update and path extraction rules are modified to prevent chain updates triggered by minor localized changes, thereby reducing the frequency of updates in non-critical areas. By simulating dynamic route planning for ships in Arctic waters during both the freezing and melting periods, the improved D* Lite algorithm was compared with the original D* Lite algorithm and a global update algorithm in terms of voyage distance, risk coefficient, planning time, and the number of node updates. The computational results demonstrate that the improved D* Lite algorithm achieves planning results very similar to those of the original D* Lite algorithm and the global update algorithm at the lowest update cost, significantly enhancing the safety and efficiency of dynamic route planning for ships in Arctic waters.

The quantitative anatomy of the hippocampal formation in homing pigeons and other pigeon breeds: implications for spatial cognition.

Artificial selection for specific behavioural and physical traits indomesticated animals has resulted in a wide variety of breeds. One of the most widely recognized examples of behavioural selection is the homing pigeon (Columba livia), which has undergone intense selection for fast and efficient navigation, likely resulting in significant anatomical changes to the hippocampal formation. Previous neuroanatomical comparisons between homing and other pigeon breeds yielded mixed results, but only focused on volumes. We completed a more systematic test for differences in hippocampal formation anatomy between homing and other pigeon breeds by measuring volumes, neuron numbers and neuron densities in the hippocampal formation and septum across homing pigeons and seven other breeds. Overall, we found few differences in hippocampal formation volume across breeds, but large, significant differences in neuron numbers and densities. More specifically, homing pigeons have significantly more hippocampal neurons and at higher density than most other pigeon breeds, with nearly twice as many neurons as feral pigeons. These findings suggest that neuron numbers may be animportant component of homing behaviour in homing pigeons. Our data also provide the first evidence that neuronal density can be modified by artificial selection, which has significant implications for the study of domestication and interbreed variation in anatomy and behaviour.

Improving the efficiency of visual navigation of an unmanned aerial vehicle by choosing the most informative route

Abstract. The problem of estimating the coordinates of unmanned aerial vehicles (UAVs) using visual navigation in the absence of satellite navigation signals is considered. The UAV has a camera pointing towards the underlying surface to adjust its position using correlation algorithms. It is assumed that the UAV can choose one of several alternative route options as part of the implementation of the target task. The aim of the study is to increase the effectiveness of visual navigation by choosing a route with maximum information content. When assessing the information content, it is proposed to take into account the size and shape of the correction areas that can be observed during flight along the appropriate routes, based on the predicted density of the distribution of errors in measuring the coordinates of the UAV. During the flight, this estimate can be updated at any time when new information is received. An adaptation of the algorithm for calculating information content for the task under consideration is proposed. As an example, the flight of a UAV with a known model accumulation of an error in determining its coordinates, loaded with a table of landmarks and a correction system is considered. Model calculations show that the proposed approach can significantly increase the probability of correctly estimating the coordinates of the UAV compared to a random choice of route. It should be noted that the estimated informative value of the routes is the average predicted value. Specific implementations can produce results that differ significantly from the calculated averages, however, the proposed algorithm allows to adapt the assessment of informativeness using new information.

Web Application for WIFI based Library Book Locator

Utilizing Wi-Fi and RFID for Efficient Library Book Search and Navigation. In an era of information overload, efficient navigation within physical libraries remains a challenge. This project proposes a novel Wi-Fi-based book locator system utilizing Radio-Frequency Identification (RFID) tags and existing library infrastructure. Users access a mobile app or web portal to search for books, triggering location queries to Wi-Fi access points equipped with RFID readers. The system identifies and pinpoints the nearest available copy, significantly reducing search time and frustration. Integration with library data provides book availability status, turn-by-turn navigation, and reservation capabilities. This innovative approach leverages existing technology to enhance user experience, optimize library organization, and foster a more productive and engaging library environment. Key Words: RFID, Libraries, Navigation , Efficiency of time

Advantages of AI-Processed Crowdsourcing Method in Map Data Update Mechanism of Navigation System of Autonomous Driving

This research delves into the advantages of the AI-processed crowdsourcing method in the map data update mechanism for the navigation system of autonomous driving. It explores how this innovative approach offers high real-time performance, enabling immediate capture of road changes. The method facilitates large-scale data collection from a wide range of sources, ensuring comprehensive coverage. Cost-effectiveness is achieved by leveraging existing user infrastructure and AI-driven data processing. Data accuracy and reliability are enhanced through crowdsourcing method and AI cleaning algorithms. Additionally, it examines real-world application cases, highlighting successful implementation and outcomes. Looking towards the future, potential developments and challenges are discussed, emphasizing the need for continuous improvement in areas like data security and standardization. The conclusion asserts that with strategic mitigation of challenges, this method holds great promise in advancing autonomous driving technology for safer and more efficient navigation.

Enhancing Swift and Socially-Aware Navigation with Continuous Spatial-Temporal Routing

AbstractRouting for autonomous robots in dynamic human environments requires paths that are collision-free, efficient, and socially considerate. This article introduces an optimization-based routing method that operates in continuous space using a spatial-temporal model of crowd dynamics. Our approach anticipates future crowd changes and adjusts routes by considering potential speed variations due to local motion planning. It optimizes navigation speed while avoiding densely crowded areas, ensuring efficient and socially-aware navigation. Simulations in three scenarios demonstrate superior performance compared to benchmark methods in terms of navigation efficiency and adaptability in crowded, dynamic environments.

Research on the path planning algorithm and obstacle-crossing motion planning strategy for a cable trench inspection robot

Abstract The path planning and obstacle-crossing motion planning of cable trench inspection robots are essential for achieving automated inspection. To improve path planning efficiency and obstacle navigation in complex environments, an enhanced global path planning algorithm based on the A* algorithm has been developed, combined with an improved Dynamic Window Approach (DWA) for local path planning. For unavoidable obstacles, a specific obstacle-crossing motion planning strategy has been formulated. The enhanced A* algorithm improves efficiency and safety through adaptive neighborhood expansion and the elimination of redundant path points. The improved DWA algorithm enables real-time dynamic obstacle avoidance in local path planning. The simulation results on a $20 \times 20$ grid map indicate that the improved A* algorithm reduces the number of nodes by 58.4% and shortens the path length by 6.1% compared to the traditional A* algorithm, demonstrating significant advantages over other conventional path planning algorithms. In the simulation experiments integrating global and local path planning, the enhanced A* algorithm combined with the improved DWA algorithm reduces the path length by 3.2% on the $20 \times 20$ grid map compared to the integration with the traditional DWA algorithm. On the $30 \times 30$ grid maps with different obstacle configurations, the path lengths are reduced by 3.5% and 3.6%, respectively. In the obstacle-crossing experiments, the robot successfully overcame obstacles of 10 cm and 20 cm in height. The proposed path planning algorithm and obstacle-crossing motion planning strategy hold substantial application potential in complex environments, offering reliable technical support for cable trench inspection robots.

Boa Fumigator: An Intelligent Robotic Approach for Mosquito Control

The mosquitoe population is reaching critical levels globally, posing significant threats to public health and ecosystems due to their role as vectors for diseases. This paper presents the development of a mobile robotic platform named Boa Fumigator with autonomous fumigation and prioritized path planning capabilities in urban landscapes. The robot’s locomotion is based on a differential drive, facilitating easier maneuverability on semi-automated planar surfaces in landscaping infrastructure. The robot’s fumigator payload consists of a spray gun and a chemical tank, which can pan and fumigate up to 4.5 m from the ground. The system incorporates a wireless charging mechanism to allow for the autonomous charging of the mosquito catchers. A genetic algorithm fused with an A*-based prioritized path planning algorithm is developed for efficient navigation and charging of mosquito catchers. The algorithm, designed for maximizing charging efficiency, considers the initial charge percentage of mosquito catchers and the time required for fumigation to determine the optimal path for charging and fumigation. The experiment results show that the path planning algorithm can generate an optimized path for charging and fumigating multiple mosquito catchers based on their initial charge percentage. This paper concludes by summarizing the key findings and highlighting the significance of the fumigation robot in landscaping applications.

CNN-Based Multi-Object Detection and Segmentation in 3D LiDAR Data for Dynamic Industrial Environments

Autonomous navigation in dynamic environments presents a significant challenge for mobile robotic systems. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) for multi-object detection in 3D space and 2D segmentation using bird’s eye view (BEV) maps derived from 3D Light Detection and Ranging (LiDAR) data. Our method aims to enable mobile robots to localize movable objects and their occupancy, which is crucial for safe and efficient navigation. To address the scarcity of labeled real-world datasets, a synthetic dataset based on a simulation environment is generated to train and evaluate our model. Additionally, we employ a subset of the NVIDIA r2b dataset for evaluation in the real world. Furthermore, we integrate our CNN-based detection and segmentation model into a Robot Operating System 2 (ROS2) framework, facilitating communication between mobile robots and a centralized node for data aggregation and map creation. Our experimental results demonstrate promising performance, showcasing the potential applicability of our approach in future assembly systems. While further validation with real-world data is warranted, our work contributes to advancing perception systems by proposing a solution for multi-source, multi-object tracking and mapping.

A 3D Model-Based Framework for Real-Time Emergency Evacuation Using GIS and IoT Devices

Advancements in 3D modelling technology have facilitated more immersive and efficient solutions in spatial planning and user-centred design. In healthcare systems, 3D modelling is beneficial in various applications, such as emergency evacuation, pathfinding, and localization. These models support the fast and efficient planning of evacuation routes, ensuring the safety of patients, staff, and visitors, and guiding them in cases of emergency. To improve urban modelling and planning, 3D representation and analysis are used. Considering the advantages of 3D modelling, this study proposes a framework for 3D indoor navigation and employs a multiphase methodology to enhance spatial planning and user experience. Our approach combines state-of-the art GIS technology with a 3D hybrid model. The proposed framework incorporates federated learning (FL) along with edge computing and Internet of Things (IoT) devices to achieve accurate floor-level localization and navigation. In the first phase of the methodology, Quantum Geographic Information System (QGIS) software was used to create a 3D model of the building’s architectural details, which are required for efficient indoor navigation during emergency evacuations in healthcare systems. In the second phase, the 3D model and an FL-based recurrent neural network (RNN) technique were utilized to achieve real-time indoor positioning. This method resulted in highly precise outcomes, attaining an accuracy rate over 99% at distances of no less than 10 metres. Continuous monitoring and effective pathfinding ensure that users can navigate safely and effectively during emergencies. IoT devices were connected with the building’s navigation software in Phase 3. As per the performed analysis, it was observed that the proposed framework provided 98.7% routing accuracy between different locations during emergency situations. By improving safety, building accessibility, and energy efficiency, this research addresses the health and environmental impacts of modern technologies.

Estimation of Spacecraft Angular Velocity Based on the Optical Flow of Star Images Using an Optimized Kalman Filter.

Biomimetic vision is a promising method for efficient navigation and perception, showing great potential in modern navigation systems. Optical flow information, which comes from changes in an image on an organism's retina as it moves relative to objects, is crucial in this process. Similarly, the star sensor is a critical component to obtain the optical flow for attitude measurement using sequences of star images. Accurate information on angular velocity obtained from star sensors could guarantee the proper functioning of spacecraft in complex environments. In this study, an optimized Kalman filtering method based on the optical flow of star images for spacecraft angular velocity estimation is proposed. The optimized Kalman filtering method introduces an adaptive factor to enhance the adaptability under dynamic conditions and improve the accuracy of angular velocity estimation. This method only requires optical flow from two consecutive star images. In simulation experiments, the proposed method has been compared with the classic Kalman filtering method. The results demonstrate the high precision and robust performance of the proposed method.