Navigation Problem Research Articles

Алгоритм планирования маршрута БПЛА в условиях недостатка визуальных ориентиров

Today, unmanned aircraft is used to solve a variety of tasks. One of the problems that arise when using unmanned aerial vehicles (UAVs) is the problem of navigation, in particular, determining their exact location in space. Today, there are various methods to solve this problem. A promising way to solve it in conditions where it is impossible to use a satellite navigation system is video navigation systems that use visual landmarks to determine location. In this paper, an overview of the existing types of UAV video navigation systems was carried out. The peculiarities of the operation of video navigation systems using the comparison of visual landmarks with satellite maps were also highlighted. The task was set to find a route in conditions of a lack of visual landmarks, in which the problem of losing reference points may arise. The graph data format used is described, which stores the values of the metric of the diversity of the underlying surface, which characterizes the magnitude of the probability of the problem of losing reference points. The existing algorithms for finding a path in a graph are considered and the need to develop a new algorithm is justified. A theoretical description of the algorithm was proposed, in which the path search process is divided into two stages: first, a solution option is searched for with the maximum value of the metric of diversity of the underlying surface, and then a route with the minimum length is selected. Several options of the implementation of the proposed algorithm were also presented and their asymptotic complexity was estimated. Based on the theoretical description, a software implementation of the proposed algorithm was developed and an experimental verification of the correctness of its operation on a test card with various flight tasks was performed. According to the test results, the proposed algorithm has shown its effectiveness by constructing routes correctly in all test examples. In the future, this algorithm can be used as part of the UAV video navigation system. The main advantages and disadvantages of the proposed algorithm were also described and ways for its further improvement were proposed.

Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces the difficulty of the algorithm. But the methods without adaptive control are not suitable in low-altitude environments with complex situations, generally suffering from weak obstacle avoidance. Some UAV navigation studies with adaptive flight only have weak obstacle avoidance capabilities. To address the problem of UAV navigation in low-altitude environments, we construct autonomous UAV navigation in 3D environments with adaptive control as a Markov decision process and propose a deep reinforcement learning algorithm. To solve the problem of weak obstacle avoidance, we creatively propose the guide attention method to make a UAV’s decision focus shift between the navigation task and obstacle avoidance task according to changes in the obstacle. We raise a novel velocity-constrained loss function and add it to the original actor loss to improve the UAV’s velocity control capability. Simulation experiment results demonstrate that our algorithm outperforms some of the state-of-the-art deep reinforcement learning algorithms performing UAV navigation tasks in a 3D environment and has outstanding performance in algorithm effectiveness, with the average reward increasing by 9.35, the success rate of navigation tasks increasing by 14%, and the collision rate decreasing by 14%.

Stable solution to the problem of autonomous navigation of moving objects on analytical trajectory intervals based on the results of inertial measurements

Provision of solution of the problem of autonomous navigation on a long time interval is considered. The main problem when using inertial navigation systems is caused by the accumulation of positioning and angular orientation errors. It is shown that this problem can be solved by combining methods of nonlinear filtering in processing the results of inertial measurements of platform-free inertial navigation systems and methods of reducing the dimensionality of the vector of navigation parameters on trajectory sections described by analytical models. Orthodromic (shortest) and circular intervals, most typical for program trajectories of transport objects – highways, railroads, airlines, etc., are considered as such trajectory sections. For circular intervals, analytical dependencies of the spatial coordinates of strapdown inertial navigation systems were obtained for the first time. These dependencies make it possible to reduce the dimension of the vector of navigation parameters and, as a consequence, to build a model of an autonomous observer of navigation parameters to use equations excluded from the general system of these parameters. It is shown that using stochastic nonlinear filtering methods it is possible to solve the problem of noise-resistant autonomous navigation on trajectory intervals described by analytical models. The proposed approach to solving the problem of autonomous navigation of moving objects makes it possible to significantly reduce computational costs in the practical implementation of algorithms for nonlinear filtering of current navigation parameters in comparison with traditional models of strapdown inertial navigation systems. The effectiveness of the proposed approach is illustrated with a numerical example. The results obtained are useful in developing navigation support for various transport objects moving along program trajectories, for example, aircraft for various purposes, railway, road, river transport, etc.

Research and Optimization of the Descent Phase of Civil Aviation Aircraft in the Vertical Navigation Problem

Research and Optimization of the Descent Phase of Civil Aviation Aircraft in the Vertical Navigation Problem

ЕКОЛОГІЧНА КОМПЕТЕНТНІСТЬ СУДНОВОДІЇВ В УМОВАХ СТАЛОГО РОЗВИТКУ

Relevance: Under current conditions, the problem of ship navigation gains particular relevance – the formation of ecological competence of ship operators in the context of sustainable development, the study of important aspects of the interaction of vessels and the surrounding environment, particularly their impact on water resources, water pollution, emissions of harmful substances, and other ecological aspects. Objective: To determine the role of ecological competence of ship operators in the context of sustainable development. Methods: Theoretical methods: analysis, synthesis, generalization of scientific works on the issue of ecological competence of future professionals in the field of ship navigation and official documents – to define the theoretical and methodological bases of the phenomenon; study and generalization of the existing problem status with the aim of choosing pedagogical tools – for the development of ecological competence of ship operators; comparison – to identify various approaches to solving the researched problem; modeling – to justify the structural elements of the model for the development of ecological competence of ship operators. Results: Key characteristics of ecological competence and its components have been identified; ecological competence of ship operators is considered as a complex integrative characteristic of the personality, reflecting motives of ecological activity, possession of ecological knowledge and natural science bases, practical skills and abilities regarding environmental protection; the necessity of forming ecological competence in ship operators during their professional training has been substantiated, as it directly affects the conservation of the marine water state, protecting them from pollution. It is noted that under current conditions, when sustainable development has become one of the priority directions of global policy, the ecological competence of ship operators is a key factor in ensuring safe navigation, and ecological education in the maritime sector is an important tool for forming this competence in ship operators. Conclusions: Education acts as a catalyst for the development of sustainable thinking and practices in the maritime sector, promoting ecological safety and sustainable development in this strategically important industry; as the maritime sector continues to develop, the results of the conducted research emphasize the important role of education in preparing future ship operators to navigate the ecological complexities of the global maritime industry and to contribute to sustainable development and effective communication.

Metric and Upper Dimension of Extended Annihilating-Ideal Graphs

The metric dimension problem is called navigation problem due to its application to robot navigation in space. Further this concept has wide applications in motion planning, sonar and loran station, and so on. In this paper, we study certain results on the metric dimension, upper dimension and resolving number of extended annihilating-ideal graph [Formula: see text] associated to a commutative ring [Formula: see text], denoted by [Formula: see text], [Formula: see text] and [Formula: see text], respectively. Here we prove the finiteness conditions of [Formula: see text] and [Formula: see text]. In addition, we characterize [Formula: see text], [Formula: see text] and [Formula: see text] for artinian rings and the direct product of rings.

Hierarchical reinforcement learning for handling sparse rewards in multi-goal navigation

Reinforcement learning (RL) has achieved remarkable advancements in navigation tasks in recent years. However, tackling multi-goal navigation tasks with sparse rewards remains a complex and challenging problem due to the long-sequence decision-making involved. Such multi-goal navigation tasks inherently incorporate a hybrid action space, where the robot needs to select a navigation endpoint first before executing primitive actions. To address the problem of multi-goal navigation with sparse rewards, we introduce a novel hierarchical RL framework named Hierarchical RL with Multi-Goal (HRL-MG). The main idea of HRL-MG is to divide and conquer the hybrid action space, splitting long-sequence decisions into short-sequence decisions. The HRL-MG framework is composed of two main modules: a selector and an actuator. The selector employs a temporal abstraction hierarchical architecture designed to specify a desired end goal based on the discrete action space. Conversely, the actuator utilizes a continuous goal-oriented hierarchical architecture developed to enact continuous action sequences to reach the desired end goal specified by the selector. In addition, we incorporate a dynamic goal detection mechanism, grounded in hindsight experience replay, to mitigate the challenges posed by sparse reward landscapes. We validated the algorithm’s efficacy on both the discrete environment Maze_2D and the continuous robotic environment MuJoCo ‘Ant’. The results indicate that HRL-MG significantly outperforms other methods in multi-goal navigation tasks with sparse rewards.

Belief inference for hierarchical hidden states in spatial navigation

Uncertainty abounds in the real world, and in environments with multiple layers of unobservable hidden states, decision-making requires resolving uncertainties based on mutual inference. Focusing on a spatial navigation problem, we develop a Tiger maze task that involved simultaneously inferring the local hidden state and the global hidden state from probabilistically uncertain observation. We adopt a Bayesian computational approach by proposing a hierarchical inference model. Applying this to human task behaviour, alongside functional magnetic resonance brain imaging, allows us to separate the neural correlates associated with reinforcement and reassessment of belief in hidden states. The imaging results also suggest that different layers of uncertainty differentially involve the basal ganglia and dorsomedial prefrontal cortex, and that the regions responsible are organised along the rostral axis of these areas according to the type of inference and the level of abstraction of the hidden state, i.e. higher-order state inference involves more anterior parts.

Hybrid search method for Zermelo’s navigation problem

Hybrid search method for Zermelo’s navigation problem

Decentralized Navigation with Optimality for Multiple Holonomic Agents in Simply Connected Workspaces.

Multi-agent systems are utilized more often in the research community and industry, as they can complete tasks faster and more efficiently than single-agent systems. Therefore, in this paper, we are going to present an optimal approach to the multi-agent navigation problem in simply connected workspaces. The task involves each agent reaching its destination starting from an initial position and following an optimal collision-free trajectory. To achieve this, we design a decentralized control protocol, defined by a navigation function, where each agent is equipped with a navigation controller that resolves imminent safety conflicts with the others, as well as the workspace boundary, without requesting knowledge about the goal position of the other agents. Our approach is rendered sub-optimal, since each agent owns a predetermined optimal policy calculated by a novel off-policy iterative method. We use this method because the computational complexity of learning-based methods needed to calculate the global optimal solution becomes unrealistic as the number of agents increases. To achieve our goal, we examine how much the yielded sub-optimal trajectory deviates from the optimal one and how much time the multi-agent system needs to accomplish its task as we increase the number of agents. Finally, we compare our method results with a discrete centralized policy method, also known as a Multi-Agent Poli-RRT* algorithm, to demonstrate the validity of our method when it is attached to other research algorithms.

Investigating the influence of human errors in master-pilot information exchange on maritime accident risk during pilotage

Pilotage-related errors have led to too many accidents in recent years, resulting in significant economic losses and disruptions in global logistics. Despite its importance, Master-Pilot Exchange (MPX) can be overlooked due to several factors. This study focuses on MPX, a standard protocol for information exchange between shipmasters and maritime pilots to ensure safe ship handling. Deficiencies in MPX procedures have been highlighted in various accident reports, emphasizing the need for improvement. This research aims to identify an optimal MPX process, evaluate potential shortcomings, assess their likelihood and severity, and analyse the human factors contributing to these deficiencies. The study utilizes a fuzzy Bayesian Network (BN) to model the MPX process and identify factors that may lead to its failure. It also employs the Success Likelihood Index Method (SLIM) approach to calculate the probability of human errors in the MPX process and identify the performance-shaping factors triggering them. The finding of the Bayesian method shows that the machinery problem and the navigational equipment problem are the two most significant root causes of MPX process failure. According to the SLIM findings, providing details of "the ship's contingency plan", and "navigational information" are the two most significant tasks for this process. Among the identified performance-shaping factors, the degree of teamwork and harmony, experience and knowledge appear to be the three most significant contributors with the highest human error probability (HEP) in mentioned tasks. This study represents a significant contribution to research on master-pilot interactions, addressing a critical aspect of maritime safety.

UAV control in autonomous object-goal navigation: a systematic literature review

Research interest in autonomous control of unmanned aerial vehicles (UAVs) has increased rapidly over the past decade. They are now widely used in civilian, military, and private areas. Applications include surveillance, search and rescue, and delivery tasks. More broadly, they excel at solving problems where a significant amount of space must be covered and traveled. However, using UAVs to solve navigation problems with full autonomy necessitates the mastering of complex subtasks. A solution that includes control, planning, localization, and mapping remains an open challenge. Object-goal navigation contains the same navigation problems where the main objective is to reach a target object. The search and identification of this target are central to the vehicle’s navigation. This requires an understanding of what it is and where it can be located to move around the scene. This paper presents a systematic literature review on object-goal navigation and its subtasks, using autonomous UAVs. Survey taxonomies were found for the tasks and methods behind navigation and target localization problems using UAVs. The review analyzed 67 articles found between 2011 and 2022. They were found in the ACM, IEEE Xplore, WebOfScience, Science Direct, and Scopus databases. This review revealed essential issues related to autonomous navigation task dependencies. Moreover, it highlighted gaps in UAV development and framework standardization. Open challenges for autonomous UAV control for object-goal navigation must address the research on finding methods for problems. For example, autonomy level and comparison metrics, considering safety, ethics, and legal implications.

Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics

When autonomous agents are deployed in an unknown environment, obstacle-avoiding movement and navigation are required basic skills, all the more so when agents are limited by partial-observability constraints. This paper addresses the problem of autonomous agent navigation under partial-observability constraints by using a novel approach: Artificial Potential Fields (APF) assisted by heuristics. The well-known problem of local minima is addressed by providing the agents with the ability to make individual choices that can be exploited in a swarm. We propose a new potential function, which provides precise control of the potential field’s reach and intensity, and the use of auxiliary heuristics provides temporary target points while the agent explores, in search of the position of the real intended target. Artificial Potential Fields, together with auxiliary search heuristics, are integrated into a novel navigation model for autonomous agents who have limited or no knowledge of their environment. Experimental results are shown in 2D scenarios that pose challenging situations with multiple obstacles, local minima conditions and partial-observability constraints, clearly showing that an agent driven using the proposed model is capable of completing the navigation task, even under the partial-observability constraints.

Complexation of correlation-extreme navigation systems

The constantly increasing intensity of air traffic, due to the growth in the number of manned and unmanned aerial vehicles, as well as the increasingly frequent disruptions of radio navigation systems in recent years, require the development of new methods for increasing the accuracy and continuity of navigation support for flights. The use of correlation-extreme navigation systems (CENS), using digital terrain matrices (DEM) of the earth's surface, makes it possible to solve navigation problems when flying over many types of terrain. However, in the case of a multi-extreme correlation dependence, the problem of ambiguity in determining the location of the aircraft arises, which reduces the accuracy and continuity of the data generated by such a CENS. The aim of article to develop a method for integrating of airborne CENSs using DEM and optical images of the earth's surface to improve the accuracy and continuity of navigation determinations. A method has been developed for integrating CENS, built on the use of DEM and digital devices for the formation and processing of optical images, and allowing to increase the accuracy of classical correlation-extremal methods in the conditions of the emergence of multi-extremal correlation dependence. The paper examines the possibility of using digital devices for forming and processing images as part of the on-board equipment of an aircraft as a means of clarifying its current location when solving autonomous navigation problems, in particular, to correct deviations of the flight path of the aircraft from a given course.

Early Detection of Alzheimers Disease through Deep Learning in MRI Scans

Abstract: Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory impairment, leading to dementia and irreversible decline in cognitive abilities. This disease causes the person to suffer from memory loss, unusual behaviour, and language problems. The initial symptoms, such as episodic memory impairment and the navigational problem of the patient, are typical variants. In the proposed system we are using The Deep Learning Models as Modified CNN along with MobileNet, and VGG16 algorithms and classify the images as either Mild Demented or Moderate Demented or Non-Demented or Very Mild Demented. In this study we are using Augmented ADNI dataset which consists of 40000 images. This can be utilized in the future to classify the types of different classifications easily which is easy to find out the infections in initial stages and can be cured in the initial stages only.

Balancing the Quantum Speed Limit and Instantaneous Energy Cost in Adiabatic Quantum Evolution

Adiabatic time-optimal quantum controls are extensively used in quantum technologies to break the constraints imposed by short coherence times. However, practically it is crucial to consider the trade-off between the quantum evolution speed and instantaneous energy cost of process because of the constraints in the available control Hamiltonian. Here, we experimentally show that using a transmon qubit that, even in the presence of vanishing energy gaps, it is possible to reach a highly time-optimal adiabatic quantum driving at low energy cost in the whole evolution process. This validates the recently derived general solution of the quantum Zermelo navigation problem, paving the way for energy-efficient quantum control which is usually overlooked in conventional speed-up schemes, including the well-known counter-diabatic driving. By designing the control Hamiltonian based on the quantum speed limit bound quantified by the changing rate of phase in the interaction picture, we reveal the relationship between the quantum speed limit and instantaneous energy cost. Consequently, we demonstrate fast and high-fidelity quantum adiabatic processes by employing energy-efficient driving strengths, indicating a promising strategy for expanding the applications of time-optimal quantum controls in superconducting quantum circuits.

The problem of winter navigation in the medieval Black Sea

Unlike the pre-industrial practice of winter navigation in the Mediterranean Sea, which has been the subject of numerous studies, winter seafaring on the medieval Black Sea has not previously been the subject of thorough scholarly investigation, not least because the available sources are relatively few and fairly circumstantial. Being aware of the limitations of the present survey, the author attempts to gather together and reinterpret several instances dating from the period of the Byzantine and Italian domination over the Black Sea, which can shed some light on the seamen's often complicated relation with the winter season. The majority of the extant sources date from the last two centuries of the Middle Ages, an era that witnessed substantial technological developments which contributed to the amelioration of winter navigation. This study chiefly aims to highlight the unresolved issues and address the challenges that such a topic of inquiry presents.

Path Planning for Autonomous Mobile Robots Using the RFO-GWO Optimization Algorithm

Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper, present a thre Path planning is a challenging navigation problem that can be handled using multi-objective methods. This paper presents a three-stage multi-objective path-planning method. The first stage is to locate the best or near-best solution path and avoid detected obstacles using a hybrid of the red fox–gray wolf optimizer (RFO–GWO), which finds a route from the start position to the target position. In the second step, a mutation operation using an evolutionary algorithm is utilized to enhance the length, integrity, and smoothness of the route generated by the RFO–GWO algorithm. The final step of the suggested method is refined further using a multiphase technique. By integrating the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path-planning method resembles the actual world. The simulation results indicate that this strategy creates the most viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when compared to prior path-planning methods, the simulation’s outcomes indicate that the suggested RFO–GWO method is effective in terms of the route, and the strategy is extremely competitive. The results showed a significant improvement, where the total percentage convergence time (in seconds) for RFO–GWO for the three maps was 15%, 12%, and 10%, respectively, whereas it was 35%, 41%, and 43% seconds in GWO and 34%, 35%, and 37% seconds in RFO. There was also a significant improvement in the number of nodes for RFO-GWO (2%, 3%, and 2%) compared to GWO nodes (64%, 65%, and 62%), and RFO nodes (32%, 30%, and 35%) for the same three maps. Subsequently, the smoothness of the path formed by the recommended approach was enhanced using the evolutionary algorithm (EA), where the total percentage length of the path in the worst scenario for GWO was 28% and for RFO was 26% in units, but after improvement with the RFO-GWO with EA, it became 22% in units. stages multi-objective path planning method: The first stage is to locate the best or near-best solution path and avoid the detected obstacles using a hybrid of the Red Fox-Grey Wolf optimizer (RFO-GWO) method, which finds a rout from the start position to the target position. In the second step, a mutation operation by evolutionary, are utilized to enhance the length, integrity, and smooth of the rout generated by the RFO-GWO method. the final step the suggested method is refined further by using the multiphase technique. By integrating both the real sizes of the mobile robots and the size of the barriers and phrasing the issue as a traveling object in the available area, the suggested path planning method resembles the actual world. The simulation results indicate that this strategy creates the best viable path even in complicated surroundings, overcoming the disadvantages of traditional approaches. Furthermore, when contrasted with prior path-planning methods, simulation outcomes indicate that the suggested RFO-GWO in terms of route effectivity, the strategy is extremely competitive.

Self-Localization of Guide Robots Through Image Classification

The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots. To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accurate solution for indoor robot navigation. The more accurate solution of the guide robotic system opens a new window of the self-localization system and solves the more complex problem of indoor robot navigation. It makes a reliable interface between humans and robots. This study successfully demonstrated how a robot finds its initial position inside a room. A deep learning system, such as a convolutional neural network, trains the self-localization system as an image classification problem. The robot was placed inside the room to collect images using a panoramic camera. Two datasets were created from the room images based on the height above and below the chest. The above-mentioned method achieved a localization accuracy of 98.98%.

Research on autonomous mobile robot maze navigation problem based on Dijkstras algorithm

In recent years, the field of autonomous mobile robotics has garnered significant attention due to its potential applications in various domains such as logistics, surveillance, and search and rescue operations. A crucial challenge in this area is the efficient navigation of robots within complex and dynamic environments, particularly when navigating through maze-like structures. The maze navigation problem involves finding optimal paths for robots to traverse from their initial positions to designated destinations while avoiding obstacles and making intelligent decisions to ensure timely and safe navigation. This study aims to investigate and apply Dijkstras algorithm to solve the maze navigation problem for autonomous mobile robots. By analyzing the navigation challenges faced by autonomous mobile robots in maze environments, a solution based on Dijkstras algorithm is proposed. In conclusion, this study contributes to the field of autonomous mobile robotics by proposing and evaluating the application of Dijkstras algorithm for maze navigation. The experimental results validate its potential to address the challenges of navigating intricate maze environments. However, it is acknowledged that further refinement and innovation are possible to continue improving the performance of autonomous mobile robots in maze navigation scenarios.