Navigation Strategy Research Articles

Liquidity Tiered Navigation Strategy: A Time Series Approach to Financial Market Stability

This article investigates the dynamic changes in liquidity stratification in financial markets, emphasizing how time series analysis can be used to forecast market liquidity. In China's financial markets, since 2014, liquidity stratification has become crucial, leading to significant differences in borrowing costs and difficulties among financial firms. Focusing on small and medium-sized banks, this study explores the origins and consequences of liquidity stratification. Moreover, it provides a comprehensive analytical approach for predicting market liquidity. This framework includes Granger causality tests, cointegration analysis, Auto regressive Integrated Moving Average (ARIMA) model, and Vector Auto regression (VAR) model. The method is rooted in a sequential, logical progression and is it eratively refined based on model outcomes. The findings of this study offer insights into the complexity of liquidity in financial markets and present a robust methodology for financial analysts and policymakers to forecast and strategize under the influence of liquidity stratification.

Development of a combined harvester navigation control system based on visual simultaneous localization and mapping-inertial guidance fusion

Recently, the existing unmanned systems of combine harvesters mostly adopts satellite navigation scheme, lacking real-time observation of harvesting adjustment. To improve the operational efficiency of combine harvester assisted navigation operation, this paper designs a combine harvester navigation control system based on vision simultaneous localization and mapping (SLAM)-inertial guidance fusion. The system acquires field image information and extracts the crop boundary line as the navigation datum by binocular camera. First, the system acquires field image information through binocular camera and extracts the crop boundary line as the navigation datum. Second, fusing camera and inertial guidance information to obtain the real-time relative position of a combine harvester. Third, constrained optimization of image and inertial guidance information is achieved through a sliding window optimization method based on tightly coupled nonlinear optimization. Finally, obtain the position of the combine harvester relative to the navigation datum line, and output a signal to the steering mechanism to realize the combine harvester in the field intelligent positioning navigation control. The system consists of binocular camera, inertial measurement unit, motorized steering wheel, monitor display, angle sensor and microcontroller. During field testing, the system underwent repetitive harvesting trials over a distance of 25 m.. The testing machine performs field operations at a speed of 0.9-1.5 m/s, with an average lateral deviation range of 2.21-8.62 cm, a standard deviation range of 0.13-4.21 cm and an average cutting rate range of 92.2%-96.0%, achieving the expected harvesting effect.

Think Holistically, Act Down-to-Earth: A Semantic Navigation Strategy With Continuous Environmental Representation and Multi-Step Forward Planning

Think Holistically, Act Down-to-Earth: A Semantic Navigation Strategy With Continuous Environmental Representation and Multi-Step Forward Planning

Design, Modeling, and Experimental Validation of an Active Microcatheter Driven by Shape Memory Effects.

Microcatheters capable of active guidance have been proven to be effective and efficient solutions to interventional surgeries for cardiovascular and cerebrovascular diseases. Herein, a novel microcatheter made of two biocompatible materials, shape memory alloy (SMA) and polyethylene (PE), is proposed. It consists of a reconfigurable distal actuator and a separate polyethylene catheter. The distal actuator is created via embedding U-shape SMA wires into the PE base, and its reconfigurability is mainly dominated by the shape memory effect (SME) of SMA wires, as well as the effect of thermal mismatch between the SMA and PE base. A mathematical model was established to predict the distal actuator's deformation, and the analytical solutions show great agreement with the finite element results. Structural optimization of such microcatheters was carried out using the verified analytical model, followed by fabrication of some typical prototypes. Experimental testing of their mechanical behaviors demonstrates the feasibility of the structural designs, and the reliability and accuracy of the mathematical model. The active microcatheter, together with the prediction model, will lay a solid foundation for rapid development and optimization of active navigation strategies for vascular interventions.

Navigating the Web: A Study on Network Topology and Information Flow

The internet, as a complex network of interconnected nodes, presents a dynamic landscape for the dissemination and flow of information. This study delves into the intricate interplay between network topology and information propagation, aiming to elucidate the mechanisms underlying navigation through the web. By analyzing various network models and employing techniques from graph theory, we investigate how the structural characteristics of the web influence the spread of information. Furthermore, we explore the role of key factors such as node centrality, clustering, and connectivity patterns in shaping information diffusion dynamics. Through empirical analysis and computational simulations, our findings shed light on the mechanisms governing the flow of information across the web, offering insights into strategies for effective navigation and content dissemination in the digital age.

An Innovative Cooperative Driving Strategy for Signal-Free Intersection Navigation with CAV Platoons

We present an innovative cooperative driving strategy known as Dynamic Resequencing and Platooning (DRP) designed to ensure the safe and efficient traversal of Connected and Automated Vehicles (CAVs) through signal-free intersections. By employing a Resequencing and Platooning Algorithm (RPA) grounded in state transition networks and CAV platooning, the optimal crossing sequence for CAVs is ascertained within a finite time. Through the utilization of a decentralized energy-optimal control framework, optimal trajectories are devised for CAVs, thereby facilitating optimal coordination among them. Simulation results underscore the substantial performance benefits of the DRP strategy compared to traffic light, First-In-First-Out (FIFO), and Local Dynamic Resequencing (LDR) strategies, with notable reductions observed in both travel delay and fuel consumption.

Evolution of Surgical Robot Systems Enhanced by Artificial Intelligence: A Review

Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks in SRS, such as the limited ability to process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating and extending human intelligence. AI offers a new direction and impetus for SRS by enhancing performance in areas such as perception, navigation, surgical planning, and control strategies. This review introduces the developmental history of AI‐aided SRS, summarizes the basic SRS architecture, and analyzes how AI can improve SRS performance. Classical cases of AI‐aided SRS, the impact of evidence in clinical settings, and associated ethical and legal considerations are explored. Finally, the challenges in AI‐aided SRS are discussed, including algorithm development, surgical data science, human–robot coordination, and trust building between humans and robots.

Target location method of intelligent deicing robot based on nonlinear auto disturbance rejection neural network

A visual navigation scheme for intermittent walking deicing robots was designed to address the issues of multiple iterations, long computation time, and poor real-time performance in the nonlinear optimization of the original SLAM system. A computer image acquisition unit, a computer image processing module, and a visual navigation parameter extraction algorithm were also designed. Implemented a visual navigation system for deicing robots based on image processing. It can meet the navigation requirements of the deicing robot. Simulation and experiments have shown that the method proposed in this paper can quickly and accurately identify abnormal point clouds. The visual servo control scheme constructed in this paper is aimed at robot task operations with unknown system calibration and target depth information. By calibrating the robot vision system, the conversion between camera pixel coordinates and robot base coordinates is achieved, with a transmission frame rate of 53.65 per second; The maximum error in positioning accuracy in space is 10.6 mm. The feature trajectory of visual space is smooth and stable within the camera's field of view, and the end movement of Cartesian space robots is stable without rebound, resulting in high grasping and positioning accuracy.

An intelligent partial charging navigation strategy for electric vehicles

ABSTRACT The rise of electric vehicles (EVs) has highlighted the issue of insufficient charging infrastructure, a major barrier to their widespread adoption. This problem is due to the limited capacity of existing charging networks and the performance constraints of EV batteries. Intelligent route planning, which optimizes travel time and energy use, could be a solution. Traditional studies on electric vehicle route planning oversimplify the charging process, assuming a linear relationship between charging level and time, neglecting its actual nonlinear nature. Additionally, these studies underestimate the impact of dynamically changing traffic conditions and charging station queue times on travel time. Integrating these factors into route planning can significantly reduce energy consumption and travel time. Our work proposes an intelligent partial charging strategy, relaxing full recharging restrictions, and fully considering the nonlinear battery characteristics, dynamic traffic conditions, and charging station queue times. Employing an improved genetic algorithm for route planning, our method demonstrates a dual advantage – reducing computational complexity and saving an average of 15% in travel time. Experimental results over a 400 km2 traffic road network validate the efficiency of our approach, outperforming existing methods facing similar challenges.

A zero-shot reinforcement learning strategy for autonomous guidewire navigation.

The treatment of cardiovascular diseases requires complex and challenging navigation of a guidewire and catheter. This often leads to lengthy interventions during which the patient and clinician are exposed to X-ray radiation. Deep reinforcement learning approaches have shown promise in learning this task and may be the key to automating catheter navigation during robotized interventions. Yet, existing training methods show limited capabilities at generalizing to unseen vascular anatomies, requiring to be retrained each time the geometry changes. In this paper, we propose a zero-shot learning strategy for three-dimensional autonomous endovascular navigation. Using a very small training set of branching patterns, our reinforcement learning algorithm is able to learn a control that can then be applied to unseen vascular anatomies without retraining. We demonstrate our method on 4 different vascular systems, with an average success rate of 95% at reaching random targets on these anatomies. Our strategy is also computationally efficient, allowing the training of our controller to be performed in only 2h. Our training method proved its ability to navigate unseen geometries with different characteristics, thanks to a nearly shape-invariant observation space.

BEYOND WORDS: A COMPREHENSIVE EXPLORATION OF LEXICAL, GRAMMATICAL, CULTURAL, AND PRAGMATIC CHALLENGES IN TRANSLATION

Summary: This comprehensive article delves into the intricate world of translation challenges, exploring the dimensions of lexical, grammatical, cultural, and pragmatic hurdles faced by translators. From the nuances of vocabulary and sentence structures to the impact of cultural context and the subtleties of social dynamics, each challenge type is dissected, exemplified, and accompanied by strategies for effective navigation. Emphasizing the need for a holistic approach, the article calls for a nuanced understanding of the interconnected facets involved in the translation process. The conclusion resonates with a call to action, urging language enthusiasts and practitioners to recognize and navigate these challenges for the sake of fostering effective cross-cultural communication. This exploration underscores that successful translation is not just about transferring words; it's a transformative journey that requires cultural competence, linguistic finesse, and an appreciation for the intricate dance between languages and cultures. Keywords: translation challenges, lexical hurdles, grammatical complexity, cultural nuances, pragmatic dimensions, cross-cultural communication, holistic approach, effective translation strategies

Inspection Robot Navigation Based on Improved TD3 Algorithm.

The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short. Current deep reinforcement learning navigation strategies can navigate successfully without pre-existing map data, yet they grapple with issues like inefficient training, slow convergence, and infrequent rewards. To tackle these challenges, this study introduces an improved two-delay depth deterministic policy gradient algorithm (LP-TD3) for local planning navigation. Initially, the integration of the long-short-term memory (LSTM) module with the Prioritized Experience Re-play (PER) mechanism into the existing TD3 framework was performed to optimize training and improve the efficiency of experience data utilization. Furthermore, the incorporation of an Intrinsic Curiosity Module (ICM) merges intrinsic with extrinsic rewards to tackle sparse reward problems and enhance exploratory behavior. Experimental evaluations using ROS and Gazebo simulators demonstrate that the proposed method outperforms the original on various performance metrics.

Navigating Conflict

Situated strategically in the Atlantic Ocean, the Azores Islands emerged as pivotal theaters during World War I and World War II and profoundly shaped these global conflicts through its geographic significance and technological advancements. In this paper, the evolution of navigation techniques, routes, and the strategic importance of the Azores according to the changing nature of warfare was analyzed. Through an examination of historical narratives and technological progress, insights were made into how the islands developed into critical hubs for naval and air operations. Advancements in airport infrastructure, telecommunications, and harbor facilities transformed the Azores into essential nodes for transatlantic shipping, communication networks, and surveillance in the Atlantic theater. Navigational strategies, informed by technological innovations, optimized air routes and access to strategic bases which bolstered military capabilities in both conflicts. Through the exploration of the relationship between technological progress, evolving warfare dynamics, and geopolitical significance, the enduring impact of the Azores Islands on shaping global conflicts throughout history was revealed.

RL-TEE: Autonomous Probe Guidance for Transesophageal Echocardiography Based on Attention-Augmented Deep Reinforcement Learning

Ultrasound image acquisition in conventional transesophageal echocardiography (TEE) requires complex manual operation of the probe in the esophagus based on the interpretation of ultrasound images and in-depth knowledge of the cardiac anatomy. In this work, we formulate the TEE probe guidance task as a reinforcement learning (RL) problem, and present the first learning-based solution to 3-DOF control of a TEE probe based on the ultrasound image feedback, named RL-TEE, in order to mimic the visual search and navigation strategies of expert echocardiographers. The probe-tissue interaction in TEE is carefully modeled in our framework by considering both the requirements for navigation towards the standard views and compliance in the esophageal environment. Furthermore, we propose a hybrid deep Q-network model that augments a convolutional neural network backbone with self-attention mechanisms to better capture spatial information in ultrasound images to guide navigation decisions. The presented methods are preliminarily validated in a TEE simulation environment built with data from 25 subjects to acquire four standard views of the heart. Our results show that the proposed method can effectively learn to accurately and compliantly guide the probe movement for TEE standard view acquisition tasks and has a good generalization ability to unseen patient data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to realize 3-DOF movement guidance of a TEE probe to acquire the standard views of the heart based on the real-time images, which can be applied to existing robotic control systems or used to assist novice echocardiographers in TEE examination, thereby relieving operator workload and improving ease of use. This paper suggests a novel approach that uses the deep RL technique to achieve automatic interpretation of TEE images and intelligent guidance of the probe movement. The RL framework is designed to take into account both the navigation efficiency and compliance with the esophageal environment for the targeted intracorporeal application. A hybrid deep Q-network model that augments a convolutional neural network with attention mechanisms is designed to better capture spatial information from ultrasound images to predict the probe movement. The effectiveness of the framework is preliminarily validated in extensive experiments in a simulation environment built with real patient data. The proposed method can be applied in clinical use to provide real-time TEE probe guidance for novice echocardiographers, and can be integrated with a robotic system to fully automate the TEE acquisition, thereby relieving the doctors from tedious manual operation to focus on the diagnosis and treatment.

Amphibious Robots Locomotion Strategies in Unstructured Complex Environments: A Review

In the previous literature, amphibious robots focused mainly on locomotion in underwater and flat land surface manoeuvring. Few amphibious robots focused on unstructured land environments. The amphibious robot designs were more emphasised in academics, leading to more work done in building amphibious robots that mimic biological amphibians, imitating the geometry and overall functionality of the amphibious robots. Developing amphibious robots with propulsive mechanisms for manoeuvring in a water environment received more attention than other functionalities like adaptability on rough natural terrain and obstacle repositioning capability. However, practical applications like reconnaissance and surveying posed challenges in the ground environment, which had unstructured and complex terrain profiles, especially in the transition area. Therefore, reviewing the amphibious robots focused on manoeuvring complex uneven surfaces was essential. The literature had comprehensive review papers on navigation strategies encompassing manoeuvring on flat ground surfaces and underwater locomotion. There was a need for a focused study that highlighted the amphibious robot that manoeuvred in an unstructured land environment. The open challenges and recent solutions by designing new mechanisms and deployment issues were highlighted and reviewed. Hence, the paper addressed a more specific review of amphibious robot locomotion in an unstructured environment. The paper also discussed a case study of an amphibious robot capable of locomotion in unstructured environments. It was envisaged that the review would provide directions and insights to researchers and robotic system designers on developing robust propulsive mechanisms for amphibious robots capable of locomotion in unstructured environments.Keywords: manoeuvrability, mobility, terrain, unified locomotion, mechanism, propulsion

Border crossing perspectives of prospective teachers’ first-time science microteaching experiences

ABSTRACT The purpose of this study was on understanding and interpreting five minoritised pre-service teachers’ past educational experiences that impacted their first-time science microteaching experiences in a science teaching methods course. The study is unique because it illuminates and contributes to the literature on the importance of minoritised prospective teachers’ educational experiences in their pre-service preparation and how these experiences challenge or contradict the urgency of equitable science instruction in traditional teacher education instructional discourse. The study draws on the literature on border crossing as a guiding theoretical framework, and phenomenology as a guiding methodological framework to analyse first time science teaching experiences. Data included videos and written narratives of first-time science teaching experiences. Analysis indicated that past educational experience of being left out in doing science underscored minoritised pre-service teachers’ first-time science teaching experiences. Furthermore, they had created navigation strategies to counter the past educational experiences by challenging and modifying science teaching routines and devising equitable ways to use science teaching resources to cultivate a sense of belonging. The study suggests that future science education research would benefit from attention to minoritised pre-service teachers’ experiences used to challenge or contradict past educational inequities.

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

Autonomous navigation of orchard spraying robots can effectively improve operational efficiency and reduce workload. The conventional robot navigation solutions based on the Global Navigation Satellite System (GNSS) are susceptible to signal loss in orchards due to interference from the canopy of fruit trees. In this paper, an autonomous navigation scheme for orchard spraying robots based on multi-sensor devices and the three-dimensional (3D) LiDAR Simultaneous Localization and Mapping (SLAM) technology. The initial step of the scheme involved the construction of a 3D point cloud map of the orchard using the 3D LiDAR SLAM algorithm. Subsequently, a designed point cloud map transformation algorithm was used to convert the 3D point cloud map of the orchard into a two-dimensional (2D) grid map containing key feature information of the orchard. To enhance the mapping accuracy of the orchard point cloud, a point cloud registration algorithm combining Iterative Closet Point (ICP) and Normal Distributions Transform (NDT) was proposed. This algorithm initially utilized the NDT algorithm for coarse point cloud registration to obtain transformation parameters. Then, the ICP algorithm was applied in conjunction with the obtained parameters for fine registration. Regarding obstacle avoidance, a cooperative obstacle avoidance algorithm based on the Robot Operating System (ROS) multithreaded communication mechanism was introduced. Experimental trials conducted in a standardized spindle-shaped peach orchard validated the successful completion of the SLAM mapping trajectory, which was achieved through the utilization of the proposed NDT_ICP point cloud registration algorithm. The average absolute pose error between the mapped trajectory and the reference trajectory was 1.173 m, with a standard deviation of 0.498 m. The robot's positioning and navigation errors increased with higher speeds, with the largest positioning deviation observed at 1.2 m/s speed, with a maximum lateral positioning error of 7.04 cm. The robot's average lateral navigation error did not exceed 16 cm, and the average heading deviation was less than 8° at different speeds of 0.4, 0.8 and 1.2 m/s. Both the positioning and navigation accuracies of the robot were sufficient to meet the autonomous operational requirements of orchard spraying robots.

Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract)

How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can explain a wide range of simple planning problems by adding structure information to the task to facilitate planning in more complex tasks. The extended model was then applied to a more complex planning domain of spatial navigation. Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment.

Discussion on different controllers used for the navigation of mobile robot

Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR's can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed.

Corrigendum: Sex differences in spatial tasks: considering environmental factors, navigation strategies, and age

Corrigendum: Sex differences in spatial tasks: considering environmental factors, navigation strategies, and age