Relevant bibliographies by topics / Automatic obstacle detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Automatic obstacle detection'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Automatic obstacle detection"

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Savitha, AC, Kumar KM Madhu, V. Prathap, et al. "Automatic Detection of Obstacle in Railway Track." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary Research Journal 4, no. 5 (2025): 1–5. https://doi.org/10.5281/zenodo.15385583.

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The main aim of the work is to  enhance the safety and efficiency of train operations. The system focuses on detecting obstacles on the railway track and automatically applying the train's brakes to prevent accidents. With the growing concerns over train collisions and safety incidents, the introduction of an automated system that can detect obstructions and take immediate corrective actions is crucial. The proposed work  utilizes sensors like ultrasonic sensors, cameras, and advanced algorithms for obstacle detection, coupled with automated braking mechanisms.
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Patil, Dr Nitin S., Jagruti J. Pande, Rachna P. Borse, Rinku M. Kamble, and Pratik A. Niphade. "Automatic Headlamp Control and Obstacle Detection." Journal of Energy Engineering and Thermodynamics, no. 34 (June 15, 2023): 9–15. http://dx.doi.org/10.55529/jeet.34.9.15.

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The numberof vehicles nowadaysincreasingvery fastly and so is technology. Safety is averyprime factor and has to be considered. vehicles are fitted with lots of safety features. Automatic Upper Dipper beam control and obstacle detection also need to be installed to improve safety to the next level. Thesefeatures are mainly used in nighttime driving or foggy weather or rain. Human eyes are very sensitive to light. when the light suddenly comes in contact with the eyes after darkness, the cornea present in the eyes gets contracted. i.e., human eyes will not be able to see for a short time. it is also called light glare. a vehicle coming from the opposite side with headlight in upper mode causes blind to the driver’s eyes. Insuch cases,there are high chances of occurring an accident. So, an automatic mechanism for controlling the light upper or dipper has to be made todecrease the chances of accidents during nighttime driving. Also, sometimes the driver won’t be able to see any object in nighttime orfoggy or rainy seasons. so, a sensor-based system where obstacle detection is enabled may assist from falling into an event of a crash. these two features assure you a very safe and comfortable drive. The operatingprinciple working and design of PCB is explained in this paper
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Xu, Xixi, Meiqi Zhang, Hao Tang, Weiye Xu, Bowen Sun, and Zhu’an Zheng. "Low-Illumination Parking Scenario Detection Based on Image Adaptive Enhancement." World Electric Vehicle Journal 16, no. 6 (2025): 305. https://doi.org/10.3390/wevj16060305.

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Aiming at the problem of easily missed and misdetected parking spaces and obstacles in the automatic parking perception task under low-illumination conditions, this paper proposes a low-illumination parking space and obstacle detection algorithm based on image adaptive enhancement. The algorithm comprises an image adaptive enhancement module, which predicts adaptive parameters using CNN and integrates the low-light image enhancement via illumination map estimation and contrast-limited adaptive histogram equalization algorithms for image processing. The parking space and obstacle detection module adopts parking space corner detection based on image gradient matching, as well as obstacle detection utilizing yolov5s, whose feature pyramid network structure is optimized. The two modules are cascaded to optimize the prediction parameters of the image adaptive enhancement module, comprehensively considering the similarity loss of parking space corner matching and the obstacle detection loss. Experiments show that the algorithm makes the image pixel value distribution more balanced in low-light scenarios, the accuracy of parking space recognition reaches 95.46%, and the mean average precision of obstacle detection reaches 90.4%, which is better than the baseline algorithms, and is of great significance for the development of automatic parking sensing technology.
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Zhang, Qiang, Fei Yan, Weina Song, Rui Wang, and Gen Li. "Automatic Obstacle Detection Method for the Train Based on Deep Learning." Sustainability 15, no. 2 (2023): 1184. http://dx.doi.org/10.3390/su15021184.

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Automatic obstacle detection is of great significance for improving the safety of train operation. However, the existing autonomous operation of trains mainly depends on the signaling control system and lacks the extra equipment to perceive the environment. To further enhance the efficiency and safety of the widely deployed fully automatic operation (FAO) systems of the train, this study proposes an intelligent obstacle detection system based on deep learning. It collects perceptual information from industrial cameras and light detection and ranging (LiDAR), and mainly implements the functionality including rail region detection, obstacle detection, and visual–LiDAR fusion. Specifically, the first two parts adopt deep convolutional neural network (CNN) algorithms for semantic segmentation and object detection to pixel-wisely identify the rail track area ahead and detect the potential obstacles on the rail track, respectively. The visual–LiDAR fusion part integrates the visual data with the LiDAR data to achieve environmental perception for all weather conditions. It can also determine the geometric relationship between the rail track and obstacles to decide whether to trigger a warning alarm. Experimental results show that the system proposed in this study has strong performance and robustness. The system perception rate (precision) is 99.994% and the recall rate reaches 100%. The system, applied to the metro Hong Kong Tsuen Wan line, effectively improves the safety of urban rail train operation.
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Ramesh, Dr V., K. Praveena, P. Venkata Lakshmi, A. Bhavana, and A. Mani Venkata Sai. "Automatic Fire Fighting Robot." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43641.

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Fire hazards pose significant threats to both life and property, necessitating rapid and efficient response mechanisms. This paper presents the design and implementation [1] of an autonomous Fire Fighting Robot capable of detecting and extinguishing fires with minimal human intervention. The system employs four fire sensors strategically placed to detect flames from multiple directions, ensuring comprehensive fire monitoring. An ultrasonic sensor enables obstacle detection and assists in navigation to prevent collisions while approaching the fire source [3]. The robot’s mobility is powered by BO motors controlled through a motor driver, while a servo motor precisely directs the fire suppression mechanism. A relay-controlled water pump is activated upon fire detection, enabling real-time fire fighting operations [5]. The system is built around a microcontroller, which processes sensor data and executes real-time decisions for fire suppression. While the robot primarily operates autonomously [2], it also features a remote control mode for enhanced flexibility in complex fire scenarios. This autonomous fire fighting system [2] demonstrates a cost-effective and efficient approach to mitigating fire hazards, making it suitable for industrial, residential, and hazardous environments. Keywords- Fire Fighting Robot, Autonomous Fire Suppression, Fire Detection, Microcontroller, Obstacle Avoidance, and Remote Control.
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Liu, Biao, Bihao Tian, and Junchao Qiao. "Mine track obstacle detection method based on information fusion." Journal of Physics: Conference Series 2229, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2229/1/012023.

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Abstract As an important part of the coal mine transportation system, coal mine underground rail transportation undertakes the core transportation task of coal mine underground. Its safe and efficient operation is directly related to the efficiency of coal mine production and transportation. In view of this, this paper proposes a mine track environmental obstacle detection system which integrates camera and Lidar information to realize real-time automatic detection of obstacles in front of the underground track mine cart. The specific research results are as follows: first of all, a point cloud clustering algorithm for the mine environment is designed to extract the obstacle information, and then the YOLOv5 algorithm is used to identify the obstacle information in the image. Finally obstacle information from image and point cloud are fused at the decision-level. The obstacle detection method proposed in this paper can be successfully identified to meet the requirements of the obstacle object detection function of the rail vehicle.
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Ramesh, Patil, Bapure Diana, Hasgonda Manjula, Jatnor Monika, and Metkar Monika. "Autonomous Obstacle Detection, Automatic Stopping, and Alert System for Train." Research and Review: VLSI Design, Tools and It's Application 1, no. 2 (2025): 8–12. https://doi.org/10.5281/zenodo.15387083.

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<em>The development of smart transportation systems has revolutionized rail networks, improving safety, efficiency, and automation. This project aims to design an "Autonomous Obstacle Detection, Automatic Stopping, and Alert System for Smart Trains Using Arduino Uno." The system incorporates an Arduino Uno microcontroller, I2C LCD, ultrasonic sensors, motor driver, DC motors, RF (Radio Frequency) transmitter and receiver modules, LEDs, and a buzzer. The core objective is to ensure real-time detection of obstacles on railway tracks, triggering automatic stopping of the train to avoid collisions and generating alerts to notify authorities or operators. The system uses ultrasonic sensors to measure the distance to potential obstacles, and if a critical threshold is breached, the motor driving the train is halted using the motor driver.</em>
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Dr.K.Venugopal, Rao, Akhila P., Madhu G., Navya P., and Manoj M. "Design and Implementation of Automatic Engine Off and Theft Detection of Vehicle." Design and Implementation of Automatic Engine Off and Theft Detection of Vehicle 8, no. 12 (2023): 8. https://doi.org/10.5281/zenodo.10403903.

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In recent times property theft has become a major concern. Vechile theft is at the top of the list and happens frequently everywhere in the world. several technologies are developing in response to this problem, and new approaches are being created to find a solution. Since burglars are now aware of the techniques used in vehicle theft detection, they attempt to compromise the system in order to take the vehicle. This paper proposes to enhance vehicle safety by integrating alcohol detection, obstacle avoidance, and theft detection systems. The alcohol detection system ensures responsible driving by monitoring the driver's alcohol level, while the obstacle avoidance mechanism uses sensors to detect obstacles and navigate the vehicle accordingly. Additionally, a theft detection system employs sensors to identify unauthorized access. Upon detection of alcohol influence or potential theft, the system triggers an automatic engine shutdown and activates a buzzer for alerting authorities, promoting safer driving practices and enhancing vehicle security. Keywords:- Vehicle Theft, GSM, Arduino, Sensors.
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Han, Weiliang, Shixuan Leng, Lisha Ma, Bo Gao, and Xuping Wu. "Automatic Obstacle Avoidance Robot based on Artificial Intelligence Detection and Recognition." Frontiers in Science and Engineering 3, no. 4 (2023): 27–31. http://dx.doi.org/10.54691/fse.v3i4.4771.

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This paper designs an automatic obstacle avoidance robot based on artificial intelligence detection and recognition, including a shell, and a detection and obstacle avoidance mechanism is set inside the shell; the detection and obstacle avoidance mechanism includes a movable member, a screw, a first electric actuator, a first camera, a gear, a rack and a steering assembly; by setting the detection and obstacle avoidance mechanism, the first camera and the second camera can take pictures of the surrounding environment during the movement of the shell, and when the micro When the computer recognizes the obstacle, it controls the servo motor to run and makes the screw rotate, so that the movable part moves in the direction of no obstacle, and the rack at the bottom of the movable part moves together during the moving process of the movable part, and the gear will be driven to rotate the steering wheel during the moving process of the rack, so that the robot can perform obstacle avoidance operation, and the height of the camera can also be adjusted by adjusting the first electric pusher and the second electric pusher to adjust the By adjusting the first and second electric actuators, the height of the camera can be adjusted to adjust the detection range, making the robot more flexible.
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NISHINA, Takumi, Etsuro SHIMIZU, Tsuyoshi ODE, and Ayako UMEDA. "Obstacle Detection System for Automatic Ship at Night Navigation." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019 (2019): 2A1—H06. http://dx.doi.org/10.1299/jsmermd.2019.2a1-h06.

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Dissertations / Theses on the topic "Automatic obstacle detection"

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Berlin, Filip, and Sebastian Granath. "Obstacle Detection and Avoidance for an Automated Guided Vehicle." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177709.

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The need for faster and more reliable logistics solutions is rapidly increasing. This is due to higher demands on the logistical services to improve quality,  quantity, speed and reduce the error tolerance. An arising solution to these increased demands is automated solutions in warehouses, i.e., automated material  handling. In order to provide a satisfactory solution, the vehicles need to be smart and able to solve unexpected situations without human interaction.  The purpose of this thesis was to investigate if obstacle detection and avoidance in a semi-unknown environment could be achieved based on the data from a 2D LIDAR-scanner. The work was done in cooperation with the development of a new load-handling vehicle at Toyota Material Handling. The vehicle is navigating from a map that is created when the vehicle is introduced to the environment it will be operational within. Therefore, it cannot successfully navigate around new unrepresented obstacles in the map, something that often occurs in a material handling warehouse. The work in this thesis resulted in the implementation of a modified occupancy grid map algorithm, that can create maps of previously unknown environments if the position and orientation of the AGV are known. The generated occupancy grid map could then be utilized in a lattice planner together with the A* planning algorithm to find the shortest path. The performance was tested in different scenarios at a testing facility at Toyota Material Handling.  The results showed that the occupancy grid provided an accurate description of the environment and that the lattice planning provided the shortest path, given constraints on movement and allowed closeness to obstacles. However, some performance enhancement can still be introduced to the system which is further discussed at the end of the report.  The main conclusions of the project are that the proposed solution met the requirements placed upon the application, but could benefit from a more efficient usage of the mapping algorithm combined with more extensive path planning.<br><p>Digital framläggning</p>
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Monteiro, Hugo Alexandre Pereira. "Neuromorphic systems for legged robot control." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/7736.

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Locomotion automation is a very challenging and complex problem to solve. Besides the obvious navigation problems, there are also problems regarding the environment in which navigation has to be performed. Terrains with obstacles such as rocks, steps or high inclinations, among others, pose serious difficulties to normal wheeled vehicles. The flexibility of legged locomotion is ideal for these types of terrains but this alternate form of locomotion brings with it its own challenges to be solved, caused by the high number of degrees of freedom inherent to it. This problem is usually computationally intensive, so an alternative, using simple and hardware amenable bio-inspired systems, was studied. The goal of this thesis was to investigate if using a biologically inspired learning algorithm, integrated in a fully biologically inspired system, can improve its performance on irregular terrain by adapting its gait to deal with obstacles in its path. At first, two different versions of a learning algorithm based on unsupervised reinforcement learning were developed and evaluated. These systems worked by correlating different events and using them to adjust the behaviour of the system so that it predicts difficult situations and adapts to them beforehand. The difference between these versions was the implementation of a mechanism that allowed for some correlations to be forgotten and suppressed by stronger ones. Secondly, a depth from motion system was tested with unsatisfactory results. The source of the problems are analysed and discussed. An alternative system based on stereo vision was implemented, together with an obstacle detection system based on neuron and synaptic models. It is shown that this system is able to detect obstacles in the path of the robot. After the individual systems were completed, they were integrated together and the system performance was evaluated in a series of 3D simulations using various scenarios. These simulations allowed to conclude that both learning systems were able to adapt to simple scenarios but only the one capable of forgetting past correlations was able to adjust correctly in the more complex experiments.
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Klomparens, Dylan. "Automated Landing Site Evaluation for Semi-Autonomous Unmanned Aerial Vehicles." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/34641.

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A system is described for identifying obstacle-free landing sites for a vertical-takeoff-and-landing (VTOL) semi-autonomous unmanned aerial vehicle (UAV) from point cloud data obtained from a stereo vision system. The relatively inexpensive, commercially available Bumblebee stereo vision camera was selected for this study. A â point cloud viewerâ computer program was written to analyze point cloud data obtained from 2D images transmitted from the UAV to a remote ground station. The program divides the point cloud data into segments, identifies the best-fit plane through the data for each segment, and performs an independent analysis on each segment to assess the feasibility of landing in that area. The program also rapidly presents the stereo vision information and analysis to the remote mission supervisor who can make quick, reliable decisions about where to safely land the UAV. The features of the program and the methods used to identify suitable landing sites are presented in this thesis. Also presented are the results of a user study that compares the abilities of humans and computer-supported point cloud analysis in certain aspects of landing site assessment. The study demonstrates that the computer-supported evaluation of potential landing sites provides an immense benefit to the UAV supervisor.<br>Master of Science
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Chen, Bo-An, and 陳柏安. "Obstacle Detection and Environment Scanning by Computer Vision Techniquefor Automatic Vehicle System." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/91561390442582150273.

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碩士<br>國立成功大學<br>工程科學系碩博士班<br>91<br>Due to the development of Autonomous Mobile Robot (AMR), the AMR is applied widely. The applications of the AMR include the works from transport in the automatic factory to household cleaning in our daily life. In order to move arbitrarily in the unknown environment, a reliable intelligent navigation system for the AMR should be established. This system will make the AMR to get correct information on implementing the capability of avoidance obstacle in the control environment. In this thesis, Epipolar Geometry is introduced to explain the geometric relation of binocular stereo vision. The works of extracting the feature points and matching corresponding points in binocular vision are explained. In the aspect of features and shape of the obstacle, the concept of plane induced parallax from planar homography is applied to collect the border between the obstacle and ground. These borders will be the feature points of the obstacle for calculating the distance from the AMR to the obstacle. Lastly, the images shot at different angles would be use to integrate the position of the obstacle. These results display the environment information around the AMR. It will be useful for the AMR to determine the path.
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Chen, Qing-Wei, and 陳卿瑋. "Obstacle Detection and Distance Estimation for A Low-Speed Automatic Vehicle with A Fisheye camera." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/c52jqs.

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碩士<br>國立中央大學<br>資訊工程學系<br>105<br>For most developed countries, a car is one of the most popular transportation devices in daily life, but it easily causes accident when driving backward because of the car's structure and driver’s view frustum. Therefore, many motor companies and the related parts suppliers have invested the related monitor system, such as Mazda’s rear vehicle monitoring system, and Nissan’s Around view monitor. However, these systems have only the monitor function. They cannot actively rise warning before any possible collision. In this paper, we install a camera with a fish-eye lens to capture wide-view-angle images to detect obstacles behind the moving vehicle. The images were transformed into top-view images to easily separate the real and false obstacles. The proposed system consists of two parts. The first part is an off-line camera calibration module. Before the camera is used for 3D measurement, the intrinsic, extrinsic, and distortion parameters of the camera should be corrected to obtain more accurate measurement. A composite calibration method of fisheye model and stereographic projection model is proposed to calibrate the distortion parameters. The second part is an on-line transformation-detection module. After top-view transformation, we estimate the vehicle’s ego-motion to set the vehicle's forward direction and distance. In addition to find the surrounding obstacles. The obstacle’s corners are extracted to estimate optical flows. At last, the real obstacles are detected by comparing their optical flows and the vehicle’s ego-motion. In experiments, the proposed system is evaluated. Three scenarios: on cement road, on asphalt road, and in corridor of a large building. The proposed method improves ego-motion error per frame to 89.95% in cement ground, 86.03% in asphalt road, and 89.49% in the building. In three scenarios, the detection rate is 81.94%, 72.38%, and 65.15%, respectively.
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Wang, Yu-Da, and 王鈺達. "Obstacle Detection and Road Classification Applied to Navigation of Automatic Land Vehicle Based on Back-propagation Neural Network and Ground Plane Stereo Techniques." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/dtyy2x.

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碩士<br>國立臺北科技大學<br>自動化科技研究所<br>100<br>In this thesis, we present an approach for environment recognition in automatic land vehicle (ALV). By using block matching for the two ground plane alignment images, we can obtain the disparity map. In the map, we can clearly detect obstacles from ground plane, and calculate the height and depth information of the obstacles. To sufficient environment information, we can obtain the features of the road (like colors, textures, etc.) from the original image. By the environment information obtained, and using back-propagation neural network (BPNN) to train and classification, the ALV will be able to recognize drivable surface, with a recognition rate of 85%, providing various options for navigation. Due to classification results are only template similar, using 3D information and road features to verify classified image will be able to achieve ALV navigation system. In this study, ALV platform is controlled by a PIC controller, and the controlling instruction is sent from a computer. The motor control system is divided into two parts: one for controlling the direction derived by a servo motor, and the other for controlling the speed of going forward or backward with a DC motor. The proposed ALV system can reach at a rate of 15 frames per second. The ALV system has been performed in NTUT campus to demonstrate the effectiveness of the proposed method.
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Mustapha, Baharuddin. "Automated Obstacle Detection System for Safe Locomotion." Thesis, 2016. https://vuir.vu.edu.au/34339/.

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The dramatic growth in aging population has opened up the opportunity for engineers and scientists to develop sophisticated devices, especially in supporting the elderly for safety navigation. Mobility assistive device is a supportive tool which can assist the elderly during walking either indoor or outdoor environment. The number of fall-related diseases among the elderly could be reduced using sensor based mobility assistive devices. These machines will grow further as new supportive tools of electronic devices for daily locomotion, and become more feasible and pervasive. Sensor embedded mobility assistive devices with wireless technology compatibilities are the solution to accommodate the elderly’s safety in navigation. The newly designed system must be highly reliable, efficient, hands-free, cheap and most importantly, practical for use in real life activities. These technologies are said to bring a number of significant improvements into the next generation of mobility assistive devices, including miniaturization, low power consumption, full integration of system capability and low cost of production. Miniaturization is a great advantage as it means that the devices or systems should require only small volumes of space and suitability to embed insole of shoes. With low power consumption, only small batteries might be needed as power supply or even energy scavenging can be sufficient to power them, if not a combination of these. As full system integration on a single chip is also possible, signal processing and computation can be performed on the same chip with greatly improved overall system performance. Most interestingly, the low per-unit cost is what business and consumers are looking for in every product and this has been a significant trend.
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Cavaleiro, João Pedro Falhas. "Andarilho de reabilitação assistido eletronicamente." Master's thesis, 2017. http://hdl.handle.net/10316/83112.

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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia<br>Ao observarmos a natureza humana, apercebemo-nos de que a capacidade de locomoção e movimentação é um dos fatores mais importantes e práticos do nosso dia-a-dia. Sem ela, as nossas ações são imediatamente reduzidas e passamos a necessitar de ajuda constante. Os andarilhos são aparelhos muito utilizados, servem de apoio aos utentes para uma locomoção muito mais segura devido à sua base de sustentação. Se estes aparelhos já são suficientemente bons por si só, se os equipássemos com diversas tecnologias para um melhor serviço e apoio a estes utentes, seria ainda melhor. Sendo assim, o que se pretende com este projeto é equipar um andarilho comum com sensores de distância para detetar obstáculos à sua volta e a distância entre o andarilho e o utente, criando, deste modo, um sistema de deteção de obstáculos; construir e automatizar travões, para que desta forma se possa desenvolver um sistema de travagem automático, que funcione em conjunto com os sensores de distância, isto é, caso seja detetado um obstáculo o sistema trava automaticamente para garantir a segurança do utente; equipar as rodas com sensores de efeito de Hall para contar as rotações, definir o sentido e medir velocidades. Irá também, ter um sistema de controlo de velocidade, isto é, caso o utente ultrapasse um determinado limite de velocidade (velocidade de segurança) o andarilho trava automaticamente, para que assim o utente não corra o risco de, por exemplo, cair enquanto desce uma rua inclinada. Finalmente, criar um sistema de alarme de perigo e várias luzes de aviso, para dar um maior apoio audiovisual ao utente. Todos estes mecanismos especificados em cima, serão controlados por um Arduino para que o utente se sinta mais seguro e confortável ao utilizar este aparelho.<br>As we observe human nature, we realize that the ability to walk and move on our own is one of the most important and practical factors of our daily life. Without it, our actions are immediately reduced and we need constant help. Walkers are widely used devices serving as support for users, for a much safer locomotion due to their support base. If these devices are already good enough by themselves, if we equip them with several technologies for better service and support to their users, it would be even better. Therefore, what is intended with this project is to equip a common walker with distance sensors to detect obstacles around it and the distance between the walker and the user, thereby creating an obstacle detection system; build an automatic braking system that can work together with the distance sensors, if an obstacle is detected the system automatically locks to ensure the safety of the user; equip wheels with Hall effect sensors to count rotations, set direction and measure speeds. It will also have a speed control system, if the user exceeds a certain speed limit (safety speed) the walker automatically locks up, so that the user does not run the risk of, for example, falling while walking in a inclined street. Finally, create a danger alarm system and several warning lights, to give a much greater audio-visual support to the user. All of these mechanisms specified above will be controlled by an Arduino so that the user feels more secure and comfortable when using this device.
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Books on the topic "Automatic obstacle detection"

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Steven, Legowik, Nashman Marilyn, and National Institute of Standards and Technology (U.S.), eds. Obstacle detection and mapping system. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.

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Book chapters on the topic "Automatic obstacle detection"

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Cui, Huihai, Jinze Song, and Daxue Liu. "Ultrasonic Array Based Obstacle Detection in Automatic Parking." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38460-8_15.

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Gal, Oren. "Automatic Obstacle Detection for USV’s Navigation Using Vision Sensors." In Robotic Sailing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22836-0_9.

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Shao, Haiyan, Zhenhai Zhang, Kejie Li, et al. "A Line Structured Light Visual Sensor for Road Obstacle Detection." In Proceedings of the Second International Conference on Mechatronics and Automatic Control. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13707-0_91.

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Dent, Matthew, and Marin Marinov. "Introducing Automated Obstacle Detection to British Level Crossings." In Sustainable Rail Transport. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78544-8_3.

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Wolf, Patrick, Thorsten Ropertz, and Karsten Berns. "Behavior-Based Obstacle Detection in Off-Road Environments Considering Data Quality." In Informatics in Control, Automation and Robotics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11292-9_39.

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Kano, Guilherme, Tiago Andrade, and Alexandra Moutinho. "Automatic Detection of Obstacles in Railway Tracks Using Monocular Camera." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34995-0_26.

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Stasinopoulos, Sotirios, and Mingguo Zhao. "Laser-Based Obstacle Avoidance and Road Quality Detection for Autonomous Bicycles." In Proceedings of the 2015 Chinese Intelligent Automation Conference. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46466-3_22.

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Parashar, Komal. "Automated Robotic Navigation with Obstacle Detection Using a Deep Learning Algorithm." In Advancing Sustainable Science and Technology for a Resilient Future. CRC Press, 2024. http://dx.doi.org/10.1201/9781003490210-81.

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Chen, Xiong, and Zhidong Deng. "Detection of Road Obstacles Using 3D Lidar Data via Road Plane Fitting." In Proceedings of the 2015 Chinese Intelligent Automation Conference. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46466-3_44.

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Stillig, Javier, Carolin Brenner, and André Colomb. "Adaptive Intralogistics with Low-Cost AGVs for a Modular Production System." In Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_12.

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AbstractDue to ever faster changing market requirements, industrial production equipment needs to become much more flexible. For this reason, the Institute of Mechanical Handling and Logistics and the Institute of Electrical Energy Conversion are developing versatile, automated, and easily adaptable solutions to increase the flexibility of future intralogistics systems. As part of the ANTS 4.0 research project, a modular low-cost automated guided vehicle has been created, which breaks down the flow of goods into its smallest units: A small load carrier. The vehicle is prepared to be charged inductively and guided by color coded LED strips inside the floor, controlled from a superordinated artificial intelligence algorithm. In case of finding an obstacle by the object detection integrated in the floor, the route is recalculated and adapted in real-time.
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Conference papers on the topic "Automatic obstacle detection"

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Dutt, Krishan, Alok Misra, Raj Gaurang Tiwari, Ravinder Singh, Mohit Mishra, and Amninder Kaur. "Automatic Controlled Robotic Car Based on Node MCU for Real-Time Obstacle and Explosive Detection." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10840726.

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Shu, Kai, Yuhe Luo, Wenbo Bai, and Yixin Wang. "Automatic cruise and intelligent obstacle avoidance method of geological radar auxiliary equipment based on YOLOX-nano lightweight target detection." In 2024 4th International Conference on Intelligent Power and Systems (ICIPS). IEEE, 2024. https://doi.org/10.1109/icips64173.2024.10899967.

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Khelifi, Amine, Giuseppina Carannante, Nidhal Bouaynaya, and Charles Johnson. "Enhancing Rotorcraft Safety: Zero-Shot Visual Language Model for Obstacle Detection around Helipads from Satellite Imagery." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-289.

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Rotorcraft continue to experience higher fatal accident rates compared to fixed-wing aircraft, primarily due to low altitude flight operations and reduced situational awareness in complex environments. A critical factor is the limited availability of accurate, up-to-date information on helipads and surrounding obstacles - such as trees, poles, and buildings - that pose significant risks during takeoff and landing. Existing resources, including the Federal Aviation Administration's heliport registry, are often outdated and incomplete, particularly for private or state-operated sites, and fail to report nearby obstacles. This lack of up-to-date data is largely due to privacy restrictions at certain locations and the high cost associated with comprehensive obstacle surveys. To address this challenge, we develop a deep learning (DL) framework that automatically detects helipads and nearby obstacles from high-resolution satellite imagery. Our approach combines Mask R-CNN for precise pixel-level helipad segmentation with Grounding DINO, a zero-shot vision-language model that identifies obstacles using flexible text prompts (e.g., "Pole", "Tree") without task-specific training. This text-guided, scalable detection method adapts to diverse and evolving operational settings. We validate our framework across helipads in the United States, and demonstrate strong performance in both helipad localization and obstacle detection. In addition, we build a web-based application that automates image processing, updates incorrect heliport coordinates, and provides obstacle reports. This work aims to enhance aviation safety, modernize infrastructure records, and deliver scalable tools to the aviation and machine learning communities.
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Dikarew, Alexej, and Tobias Winkler. "Rotorcraft Guidance with Sampling based Model Predictive Control." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1051.

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A key technology in automation of rotorcraft flight is collision-free guidance. Especially in operations close to the ground, detection and automatic avoidance of ground-based obstacles and aircraft is a demanding task. This paper presents a sampling-based model predictive approach for collision-free guidance of rotorcraft. The method calculates control inputs for the flight controller by predicting the closed-loop dynamics of the rotorcraft for a short time horizon and evaluating the predictions with a cost function, which can take an arbitrary form. The approach implements a simple algorithm, mitigating the need for iterative optimization and allowing for deterministic execution time. The cost function is set to ensure collision-free maneuvering while following a desired path, as well as considering constraints of the rotorcraft states and control inputs. The path following performance is tested in closed-loop simulations with a non-linear helicopter model. The algorithm is implemented on a graphics processing unit for parallel execution, strongly decreasing the computation time.
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Noguchi, Chihiro, Toshiaki Ohgushi, and Masao Yamanaka. "Road Obstacle Detection based on Unknown Objectness Scores." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610249.

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B, Blessed Sam, Taarun M, and Sai Ganesh T. "Smart Railway Track Obstacle and Crack Detection System with Automated Alert Mechanism." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988119.

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Bian, Lu, Weimin Xia, Yuanpeng Tan, and Miao Zhao. "Research on obstacle detection algorithm based on object recognition." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898405.

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Griesser, Dennis, Matthias O. Franz, and Georg Umlauf. "Enhancing Inland Water Safety: The Lake Constance Obstacle Detection Benchmark." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610600.

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Li, Jiang, Mingze Ma, Xiankuan Zhang, and Leilei Shi. "Research on Helicopter Obstacle Detection Algorithm Based on Object Recognition." In 2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2024. https://doi.org/10.1109/auteee62881.2024.10869756.

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Wang, Huihui, and Qiang Wang. "Ripnet: Real-Time Obstacle Detection for USVs Based on Improved PSPNet." In 2024 International Conference on Automation in Manufacturing, Transportation and Logistics (ICaMaL). IEEE, 2024. https://doi.org/10.1109/icamal62577.2024.10919594.

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Reports on the topic "Automatic obstacle detection"

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Moorehead, Stewart. Unsettled Topics in Obstacle Detection for Autonomous Agricultural Vehicles. SAE International, 2021. http://dx.doi.org/10.4271/epr2021029.

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Agricultural vehicles often drive along the same terrain day after day or year after year. Yet, they still must detect if a moveable object, such as another vehicle or an animal, happens to be on their path or if environmental conditions have caused muddy spots or washouts. Obstacle detection is one of the major missing pieces that can remove humans from highly automated agricultural machines today and enable the autonomous vehicles of the future. Unsettled Topics in Obstacle Detection for Autonomous Agricultural Vehicles examines the challenges of environmental object detection and collision prevention, including air obscurants, holes and soft spots, prior maps, vehicle geometry, standards, and close contact with large objects.
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