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نتیجه جستجو - Navigation

تعداد مقالات یافته شده: 69
ردیف عنوان نوع
1 Spacecraft relative navigation with an omnidirectional vision sensor
ناوبری نسبی سفینه فضایی با سنسور دید همه جانبه-2021
With the onset of autonomous spacecraft formation flying missions, the ability of satellites to autonomously navigate relatively to other space objects has become essential. To implement spacecraft relative navigation, relative measurements should be taken, and processed using relative state estimation. An efficient way to generate such information is by using vision-based measurements. Cameras are passive, low-energy, and information-rich sensors that do not actively interact with other space objects. However, pointing cameras with a conventional field-of-view to other space objects requires much a-priori initialization data; in particular, dedicated attitude maneuvers are needed, which may interfere with the satellite’s main mission. One way to overcome these difficulties is to use an omnidirectional vision sensor, which has a 360-degree horizontal field of view. In this work, we present the development of an omnidirectional vision sensor for satellites, which can be used for spacecraft relative navigation, formation flying, and space situational awareness. The study includes the development of the measurement equations, dynamical models, and state estimation algorithms, as well as a numerical study, an experimental investigation, and a space scalability analysis.
Keywords: Omnidirectional vision sensor | Space navigation | Extended Kalman Filter | Computer vision | Spacecraft relative dynamics | Unified projection model
مقاله انگلیسی
2 Stereo disparity optimization with depth change constraint based on a continuous video
بهینه سازی اختلاف استریو با محدودیت تغییر عمق بر اساس یک فیلم مداوم-2021
Three-dimensional reconstruction based on stereo vision technology is an important research direction in the field of computer vision, and has a wide range of applications in industrial measurement, medical image reconstruction, cultural relic preservation, robot navigation, virtual reality and other fields. However, the three- dimensional reconstruction of moving objects usually has poor accuracy, low efficiency and poor visualization effect due to the image noise, motion blur, complex and time-consuming calculation etc. In this article, a disparity optimization method based on depth change constraint is proposed, which utilizes the correlation of the adjacent frames in the continuous video sequence to eliminate mismatches and correct the wrong disparity values by introducing a depth change constraint threshold. The experiments on the video images which are taken by a binocular stereo vision system demonstrate that our method of removing incorrect matches bears satisfactory results and it can greatly improve the effect of the three-dimensional reconstruction of the moving objects.
Keywords: Disparity optimization | Three-dimensional reconstruction | Depth change constraint | Video images
مقاله انگلیسی
3 Stereo disparity optimization with depth change constraint based on a continuous video
بهینه سازی اختلاف استریو با محدودیت تغییر عمق بر اساس یک فیلم مداوم-2021
Three-dimensional reconstruction based on stereo vision technology is an important research direction in the field of computer vision, and has a wide range of applications in industrial measurement, medical image reconstruction, cultural relic preservation, robot navigation, virtual reality and other fields. However, the three-dimensional reconstruction of moving objects usually has poor accuracy, low eciency and poor visualization eect due to the image noise, motion blur, complex and time-consuming calculation etc. In this article, a disparity optimization method based on depth change constraint is proposed, which utilizes the correlation of the adjacent frames in the continuous video sequence to eliminate mismatches and correct the wrong disparity values by introducing a depth change constraint threshold. The experiments on the video images which are taken by a binocular stereo vision system demonstrate that our method of removing incorrect matches bears satisfactory results and it can greatly improve the eect of the three-dimensional reconstruction of the moving objects.
Keywords: Disparity optimization | Three-dimensional reconstruction | Depth change constraint | Video images
مقاله انگلیسی
4 Finding the shortest path in a familiar environment: A comparison between describing and walking a path after accounting for the role of individual factors
پیدا کردن کوتاه ترین مسیر در یک محیط آشنا: مقایسه بین توصیف و پیاده روی مسیر پس از حسابداری برای نقش عوامل فردی-2021
Finding the shortest path to a destination is a refined navigation ability little explored as yet in familiar envi- ronments. The present study examined this ability when walking or describing the path, and how performance relates to individual differences. Sixty-seven undergraduates familiar with the area around their campus were asked to find the shortest path to a destination by walking there or describing it in writing. Several visuospatial tasks and questionnaires were administered. It emerged that shortest path finding performance was supported by familiarity and sense of direction. After accounting for these individual factors, participants performed better when walking than when describing a path. Overall, the results showed that retrieving spatial knowledge about familiar environments relates to individual differences and recall condition, walking a path being easier than describing it.
keywords: محیط زیست آشنا | کوتاه ترین مسیر پیدا کردن | شرح فضایی | توانایی واسطه | حس جهت | جهت یابی | Familiar environment | Shortest path finding | Spatial description | Visuospatial ability | Sense of direction | Navigation
مقاله انگلیسی
5 Development of a vision system for safe and high-precision soft landing on the Moon
توسعه یک سیستم بینایی برای فرود نرم و ایمن و با دقت بالا بر روی ماه-2021
Modern stage of space exploration is tightly related with the Moon research, with the deployment of long-term research stations on its surface. One of the critical elements here is a system for providing a highly accurate and reliable soft landing. The task of soft landing was solved many years ago, however, its accuracy is insufficient for modern lunar projects. Modern computer vision systems allow getting high-quality surface images at all stages of landing, and processing them in real time. The use of technical vision is beneficial due to the low weight, dimensions and energy consumption.The following tasks are assigned to the vision system: self-position determination of the landing unit and selection of a suitable place for a safe landing. Self-position determination by the video is necessary for INS data correction. Two methods are considered: detection and comparison of craters, and GHT. Both methods use the vector map and don’t require actual high- precision orthoplan. The safe site choosing method uses the found position for binding it to the desired and undesirable sites map. After that a precise choice follows, which takes into account the surface angle, smoothness, presence of extensive shadow areas. The algorithm is able to select landing sites close to those manually selected by the operator. The obtained algorithms can work in real time with the influence of interfering factors (noise, changes in the Sun position, changing of camera angle), and can be used to solve the problem.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 14th International Symposium “Intelligent Systems”.
Keywords: Visual navigation | safe landing | landing site selection | hough transform | computer vision
مقاله انگلیسی
6 Optimal guidance laws with prescribed degree of stability
قوانین راهنمایی بهینه با درجه ثبات تعیین شده-2020
The prescribed degree of stability criterion is used. This quadratic criterion involves an increasing exponential time dependent term in the integral part of the criterion. This criterion is used for derivation of guidance laws. The derived guidance law has the classical structure of guidance gain times the zero-effort miss. The important issue is the fact that initially the guidance gain and thus the commanded acceleration are larger than in the conventional Proportional Navigation guidance law, but near the end, the commanded acceleration is smaller. The new guidance law attempts to close the zero effort miss earlier in the scenario than the conventional guidance law.
Keywords: Guidance | Zero-effort-miss | Quadratic criterion | Proportional navigation
مقاله انگلیسی
7 AIS-Based Vessel Trajectory Reconstruction with U-Net Convolutional Networks
بازسازی مسیر کشتی مبتنی بر AIS با شبکه های کانولوشن U-Net-2020
The vessel trajectory data indicated by the Automatic Identification System (AIS) is important and useful in maritime data analysis, navigational safety and maritime risk assessment. However, the raw trajectory data contains noise, missing data and other errors which can lead to a wrong conclusion. Therefore, it is essential to develop a vessel trajectory reconstruction method, which is meaningful for enhancing the applicability of vessel trajectory and improving the navigation safety. In recent years, there have been many studies about vessel trajectory reconstruction, but the performance of these methods will degrade when they are faced with curved trajectories with high loss rate. In this paper, we propose a novel trajectory reconstruction method via U-net. Benefiting from the architecture of U-net, this method makes great use of historical trajectories and takes advantage of the rich skip connections in this network which help copy low-level features to corresponding high-level features. Consequently, this method is robust to the trajectories with different sampling rates, missing points, and noisy data. In addition, the proposed method is tested and compared with cubic spline interpolation. The results show that our method is capable of higher accuracy than the cubic spline interpolation especially when the trajectories are curved and have a high loss rate.
Keywords: Trajectory reconstruction | U-net | Machine learning | AIS data | Traffic safety
مقاله انگلیسی
8 The autonomous navigation and obstacle avoidance for USVs with ANOA deep reinforcement learning method
هدایت خود مختار و جلوگیری از مانع برای USV ها با روش یادگیری تقویتی عمیق ANOA-2020
The unmanned surface vehicle (USV) has been widely used to accomplish missions in the sea or dangerous marine areas for ships with sailors, which greatly expands protective capability and detection range. When USVs perform various missions in sophisticated marine environment, autonomous navigation and obstacle avoidance will be necessary and essential. However, there are few effective navigation methods with real-time path planning and obstacle avoidance in dynamic environment. With tailored design of state and action spaces and a dueling deep Q-network, a deep reinforcement learning method ANOA (Autonomous Navigation and Obstacle Avoidance) is proposed for the autonomous navigation and obstacle avoidance of USVs. Experimental results demonstrate that ANOA outperforms deep Q-network (DQN) and Deep Sarsa in the efficiency of exploration and the speed of convergence not only in static environment but also in dynamic environment. Furthermore, the ANOA is integrated with the real control model of a USV moving in surge, sway and yaw and it achieves a higher success rate than Recast navigation method in dynamic environment.
Keywords: Autonomous navigation | Obstacle avoidance | Reinforcement learning | Unmanned surface vehicle (USV)
مقاله انگلیسی
9 AIS Data-Based for Statistics and Analysis of Maritime Traffic Dangerous Features: A Case Study of San Diego Costal Water
AIS مبتنی بر داده ها برای آمار و تجزیه و تحلیل ویژگی های خطرناک ترافیک دریایی: مطالعه موردی آب پایدار سن دیگو-2020
For analyzing the distribution characteristics of maritime traffic dangers and ensure the safety of ships navigating at sea, plotting the ship’s trajectory and making statistic based on the relevant dynamic data that can be extracted and analyzed from the Automatic Indentification System (AIS) information, the Distance to Closet Point of Approach (DCPA) between any two ships have been computed and applied. Through constructing the innovative three-dimensional bubbles chart to reflect the spatial characteristics from the DCPA matrix. Utilizing the Kernel Density Estimation (KDE) method, draws the heat map in the vicinity of San Diego sea area, measures the traffic danger characteristics of the costal sea area, and compares the analysis results with ArcGIS Pro 2.3. It reflects that the analysis results are consistent with the actual situation. The heat map has the powerful scientificity and practicability, can accurately measure the traffic distribution in the sea area, and provides auxiliary references for the planning and design of the ships routing system and maritime traffic safety management.
Key Words: Maritime Navigational Safety | Ship Automatic Identification System | Kernel Density Estimation | Distance to Closet Point of Approach
مقاله انگلیسی
10 UAV navigation in high dynamic environments: A deep reinforcement learning approach
هدایت پهپاد در محیط های پویا بالا: یک رویکرد یادگیری تقویتی عمیق-2020
Unmanned Aerial Vehicle (UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast. In this work, we propose an improved DRL-based method to tackle these fundamental limitations. To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory (LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.
KEYWORDS : Autonomous vehicles | Deep learning | Motion planning | Navigation | Reinforcement learning | Unmanned Aerial Vehicle (UAV)
مقاله انگلیسی
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