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Journal articles on the topic 'Avoidance algorithms'

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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1

Yu, Huanchen. "Application of Different Sensors in Obstacle Avoidance of Delivery Robots." Applied and Computational Engineering 80, no. 1 (2024): 81–86. http://dx.doi.org/10.54254/2755-2721/80/2024ch0081.

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People's living conditions are constantly rising as the global economy and society evolve at a rapid pace, and improving production efficiency has become a crucial concern. This paper proposes a combination of multiple obstacle avoidance schemes and algorithms to achieve dynamic obstacle avoidance for delivery robots. By analysing three different obstacle avoidance schemes including visual obstacle avoidance scheme, ultrasonic obstacle avoidance scheme and laser obstacle avoidance scheme, three robot usage scenarios including hotels, restaurants and hospitals, and three vital algorithms includ

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Katona, Kornél, Husam A. Neamah, and Péter Korondi. "Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot." Sensors 24, no. 11 (2024): 3573. http://dx.doi.org/10.3390/s24113573.

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Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without collisions. Obstacle avoidance algorithms play a key role in robotics and autonomous vehicles. These algorithms enable robots to navigate their environment efficiently, minimizing the risk of collisions and safely avoiding obstacles. This article provides an overview of key obstacle avoidance algorithms, includin

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N. Abdulnabi, Ali. "Obstacle Avoidance Techniques for Robot Path Planning." DJES 12, no. 1 (2019): 56–65. http://dx.doi.org/10.24237/djes.2019.12107.

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This paper presents a collision-free path planning approaches based on Bézier curve and A-star algorithm for robot manipulator system. The main problem of this work is to finding a feasible collision path planning from initial point to final point to transport the robot arm from the preliminary to the very last within the presence of obstacles, a sequence of joint angles alongside the path have to be determined. To solve this problem several algorithms have been presented among which it can be mention such as Bug algorithms, A-Star algorithms, potential field algorithms, Bézier curve algorithm

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Gao, Wei, Mengxue Han, Zhao Wang, Lihui Deng, Hongjian Wang, and Jingfei Ren. "Research on Method of Collision Avoidance Planning for UUV Based on Deep Reinforcement Learning." Journal of Marine Science and Engineering 11, no. 12 (2023): 2245. http://dx.doi.org/10.3390/jmse11122245.

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A UUV can perform tasks such as underwater surveillance, reconnaissance, surveillance, and tracking by being equipped with sensors and different task modules. Due to the complex underwater environment, the UUV must have good collision avoidance planning algorithms to avoid various underwater obstacles when performing tasks. The existing path planning algorithms take a long time to plan and have poor adaptability to the environment. Some collision-avoidance planning algorithms do not take into account the kinematic limitations of the UUV, thus placing high demands on the performance and control

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Zhou, Bin, and Jin Fa Qian. "Obstacle Avoidance Control Method of Mobile Robot Motion." Applied Mechanics and Materials 443 (October 2013): 119–22. http://dx.doi.org/10.4028/www.scientific.net/amm.443.119.

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Mobile robot is an intelligent system which can move freely and is scheduled to complete the task in the working environment. Obstacle avoidance of mobile robot is the research hotspot in the control field of the mobile robot. The mobile robot obstacle avoidance methods are classified, including the traditional algorithms and the intelligent algorithms. This paper summarizes the intelligent algorithm in the mobile robot obstacle avoidance technique in the present situation, and the intelligent algorithm which is the most researched in the current. Finally, this paper prospects the development

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Fan, Li. "Research on Autonomous Obstacle Avoidance Algorithm for Complex Environment of Unmanned Aerial Vehicle Based on Multi-source Sensor Fusion." MATEC Web of Conferences 410 (2025): 04008. https://doi.org/10.1051/matecconf/202541004008.

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Autonomous obstacle avoidance for UAVs in complex environments is crucial, single sensors have limitations, and multi-source sensor fusion technology has received attention. Based on the above problems, this paper summarizes the research on autonomous obstacle avoidance algorithms for UAVs in complex environments based on multi- source sensor fusion in recent years. Firstly, the classification and basic principles of multi-source sensor fusion algorithms at the data layer, feature layer and decision layer are sorted out, and the characteristics of commonly used sensors such as LiDAR and vision

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Guo, Binghua, and Nan Guo. "Cash Collection Model of Electric Power Business Office Based on Computer Algorithm." Journal of Physics: Conference Series 2146, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2146/1/012023.

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Abstract With the continuous development of intelligent algorithms, mobile robot (hereinafter referred to as MR) technology is gradually mature, which has been widely used in a variety of industries, such as industry, agriculture, medical treatment, service and so on. With the improvement of intelligent level, people have higher and higher requirements for MRs, which requires MRs to constantly adapt to different environments, especially dynamic environments. In the dynamic environment, obstacle avoidance technology has become the focus of intelligent robot research, which needs to continuously

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Kadry, Seifedine, Gennady Alferov, and Viktor Fedorov. "D-Star Algorithm Modification." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 08 (2020): 108. http://dx.doi.org/10.3991/ijoe.v16i08.14243.

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One of the most effective methods for solving a navigation problem is the method of constructing a navigation system based on the simultaneous localization and mapping algorithm and obstacle avoidance algorithms. One of the most effective obstacles avoidance algorithms is the D-star algorithm [1, 2, 3], which, despite its effectiveness, has some drawbacks. This modification allows to eliminate some problems arising during the implementation of the navigation system.

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Hernandez, Carlos, and Jorge Baier. "Real-Time Adaptive A∗ with Depression Avoidance." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, no. 1 (2011): 146–51. http://dx.doi.org/10.1609/aiide.v7i1.12455.

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RTAA* is probably the best-performing real-time heuristic search algorithm at path-finding tasks in which the environ- ment is not known in advance or in which the environment is known and there is no time for pre-processing. As most real- time search algorithms do, RTAA∗ performs poorly in presence of heuristic depressions, which are bounded areas of the search space in which the heuristic is too low with respect to their border. Recently, it has been shown that LSS-LRTA∗, a well-known real-time search algorithm, can be improved when search is actively guided away of depressions. In this pape

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Zhou, Haiming, Mao Zheng, Xiumin Chu, et al. "Virtual Reality Fusion Testing-Based Autonomous Collision Avoidance of Ships in Open Water: Methods and Practices." Journal of Marine Science and Engineering 12, no. 12 (2024): 2181. http://dx.doi.org/10.3390/jmse12122181.

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With the rapid development of autonomous collision avoidance algorithms on ships, the technical demand for the testing and verification of autonomous collision avoidance algorithms is increasing; however, the current testing of autonomous collision avoidance algorithms is mainly based on the virtual simulation of the computer. To realize the testing and verification of the autonomous collision avoidance algorithm in the real ship scene, a method of virtual reality fusion testing in open water is proposed and real ship testing is carried out. Firstly, an autonomous ship collision avoidance test

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Gal, Oren. "Unified Trajectory Planning Algorithms for Autonomous Underwater Vehicle Navigation." ISRN Robotics 2013 (June 9, 2013): 1–6. http://dx.doi.org/10.5402/2013/329591.

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This paper presents two efficient methods for obstacle avoidance and path planning for Autonomous Underwater Vehicle (AUV). These methods take into account the dynamic constraints of the vehicle using advanced simulator of AUV considering low level control and stability effects. We present modified visibility graph local avoidance method and a spiral algorithm for obstacle avoidance. The algorithms were tested in challenged scenarios demonstrating safe trajectory planning.

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Abhishek, Talabattula Sai, Daniel Schilberg, and Arockia Selvakumar Arockia Doss. "Obstacle Avoidance Algorithms: A Review." IOP Conference Series: Materials Science and Engineering 1012 (January 8, 2021): 012052. http://dx.doi.org/10.1088/1757-899x/1012/1/012052.

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Abhishek, Talabattula Sai, Daniel Schilberg, and Arockia Selvakumar Arockia Doss. "Obstacle Avoidance Algorithms: A Review." IOP Conference Series: Materials Science and Engineering 1012 (January 8, 2021): 012052. http://dx.doi.org/10.1088/1757-899x/1012/1/012052.

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Hernandez, Carlos, and Jorge Baier. "Real-Time Adaptive A* with Depression Avoidance." Proceedings of the International Symposium on Combinatorial Search 2, no. 1 (2021): 193–94. http://dx.doi.org/10.1609/socs.v2i1.18215.

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Real-time search is a well known approach to solving search problems under tight time constraints. Recently, it has been shown that LSS-LRTA∗ , a well-known real-time search algorithm, can be improved when search is actively guided away of depressions. In this paper we investigate whether or not RTAA∗ can be improved in the same manner. We propose aRTAA∗ and daRTAA∗ , two algorithms based on RTAA∗ that avoid heuristic depressions. Both algorithms outperform RTAA∗ on standard path-finding tasks, obtaining better-quality solutions when the same time deadline is imposed on the duration of the pla

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Yena, Maksym. "Uav urban mobility control: swarm intelligence and collision avoidance." INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, no. 4(30) (December 11, 2024): 59–66. https://doi.org/10.30837/2522-9818.2024.4.059.

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Subject matter: Intelligent management of traffic flows in urban environments using swarm intelligence principles and collision avoidance algorithms to ensure safe and efficient urban mobility. Special attention is given to the management of unmanned vehicles and drones. Goal: To develop and analyze an approach to managing urban mobility that combines swarm intelligence principles and collision avoidance algorithms to optimize traffic flows, improve traffic safety, and reduce the number of accidents. Tasks: Investigate the safety and efficiency problems of urban transportation in the context o

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Xiao, Wenkai, and Xinhao Ye. "Obstacle Avoidance Algorithm for Mobile Robots." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 722–31. http://dx.doi.org/10.62051/a8m8dw18.

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Automatic car obstacle avoidance technology mainly utilizes advanced sensor technology to enhance the car's perception ability of the driving environment, feedback real-time information such as vehicle speed and position obtained by the perception system to the system, and judge and analyze potential safety hazards based on comprehensive information of road conditions and traffic flow. Mobile robots are increasingly used in applications, including in industrial production, agriculture, healthcare, rescue. However, mobile robots often face the challenge of avoiding obstacles while performing th

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Xu, Libo, Chunhong Yuan, and Zuowen Jiang. "Multi-Strategy Enhanced Secret Bird Optimization Algorithm for Solving Obstacle Avoidance Path Planning for Mobile Robots." Mathematics 13, no. 5 (2025): 717. https://doi.org/10.3390/math13050717.

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Mobile robots play a pivotal role in advancing smart manufacturing technologies. However, existing Obstacle avoidance path Planning (OP) algorithms for mobile robots suffer from low stability and applicability. Therefore, this paper proposes an enhanced Secret Bird Optimization Algorithm (SBOA)-based OP algorithm for mobile robots to address these challenges, termed AGMSBOA. Firstly, an adaptive learning strategy is introduced, where individuals enhance the diversity of the algorithm’s population by summarizing relationships among candidates of varying quality, thereby strengthening the algori

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Chen, Shitu, Ling Feng, Xuteng Bao, Zhe Jiang, Bowen Xing, and Jingxiang Xu. "An Optimal-Path-Planning Method for Unmanned Surface Vehicles Based on a Novel Group Intelligence Algorithm." Journal of Marine Science and Engineering 12, no. 3 (2024): 477. http://dx.doi.org/10.3390/jmse12030477.

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Path planning is crucial for unmanned surface vehicles (USVs) to navigate and avoid obstacles efficiently. This study evaluates and contrasts various USV path-planning algorithms, focusing on their effectiveness in dynamic obstacle avoidance, resistance to water currents, and path smoothness. Meanwhile, this research introduces a novel collective intelligence algorithm tailored for two-dimensional environments, integrating dynamic obstacle avoidance and smooth path optimization. The approach tackles the global-path-planning challenge, specifically accounting for moving obstacles and current in

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Huang, Zheng, Chengling Jiang, Chao Shen, Bin Liu, Tao Huang, and Minghui Zhang. "A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection." World Electric Vehicle Journal 16, no. 1 (2025): 22. https://doi.org/10.3390/wevj16010022.

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Path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in power line inspection. In complex inspection environments characterized by densely distributed and dynamic obstacles, traditional path-planning algorithms struggle to ensure both efficiency and safety. To address these challenges, this study proposes a dynamic path-planning method that integrates an improved Rapidly exploring Random Tree Star (RRT*) algorithm with the Dynamic Window Approach (DWA). The proposed method includes key components such as sampling-point search, random tree growth, global path-node optimizatio

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Li, Juan, Jianxin Zhang, Honghan Zhang, and Zheping Yan. "A Predictive Guidance Obstacle Avoidance Algorithm for AUV in Unknown Environments." Sensors 19, no. 13 (2019): 2862. http://dx.doi.org/10.3390/s19132862.

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A predictive guidance obstacle avoidance algorithm (PGOA) in unknown environments is proposed for autonomous underwater vehicle (AUV) that must adapt to multiple complex obstacle environments. Using the environmental information collected by the Forward-looking Sonar (FLS), the obstacle boundary is simplified by the convex algorithm and Bessel interpolation. Combining the predictive control secondary optimization function and the obstacle avoidance weight function, the predicting obstacle avoidance trajectory parameters are obtained. According to different types of obstacle environments, the c

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Sukwadi, Ronald, Gregorius Airlangga, Widodo Widjaja Basuki, et al. "Comparative Analysis of Path Planning Algorithms for Multi-UAV Systems in Dynamic and Cluttered Environments: A Focus on Efficiency, Smoothness, and Collision Avoidance." International Journal of Robotics and Control Systems 4, no. 4 (2024): 1602–16. https://doi.org/10.31763/ijrcs.v4i4.1555.

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This study evaluates the performance of various path planning algorithms for multi-UAV systems in dynamic and cluttered environments, focusing on critical metrics such as path length, path smoothness, collision avoidance, and computational efficiency. We examined several algorithms, including A*, Genetic Algorithm, Modified A*, and Particle Swarm Optimization (PSO), using comprehensive simulations that reflect realistic operational conditions. Key evaluation metrics were quantified using standardized methods, ensuring the reproducibility and clarity of the findings. The A* Path Planner demonst

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Abulail, Rawan Nassri. "Premature Avoidance in Genetic Algorithm using Dynamic Mutation Probability." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 16, no. 1 (2025): 528–42. https://doi.org/10.58346/jowua.2025.i1.031.

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Evolutionary algorithms are optimization techniques based on biological and natural evolution mechanisms. These algorithms are a subset of evolutionary computation and fall under unsupervised learning. The Genetic Algorithm (GA) is one of the most common types of evolutionary algorithms. It begins with an initial set of candidate solutions and starts the evolutionary process by applying certain operators to generate new solutions. The newly produced solutions are expected to outperform the previous ones. Premature convergence is a problem encountered by most evolutionary algorithms, particular

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Lee, Hae-In, Hyo-Sang Shin, and Antonios Tsourdos. "A Probabilistic–Geometric Approach for UAV Detection and Avoidance Systems." Sensors 22, no. 23 (2022): 9230. http://dx.doi.org/10.3390/s22239230.

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This paper proposes a collision avoidance algorithm for the detection and avoidance capabilities of Unmanned Aerial Vehicles (UAVs). The proposed algorithm aims to ensure minimum separation between UAVs and geofencing with multiple no-fly zones, considering the sensor uncertainties. The main idea is to compute the collision probability and to initiate collision avoidance manoeuvres determined by the differential geometry concept. The proposed algorithm is validated by both theoretical and numerical analysis. The results indicate that the proposed algorithm ensures minimum separation, efficienc

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Hou, Yew Cheong, Khairul Salleh Mohamed Sahari, Leong Yeng Weng, et al. "Development of collision avoidance system for multiple autonomous mobile robots." International Journal of Advanced Robotic Systems 17, no. 4 (2020): 172988142092396. http://dx.doi.org/10.1177/1729881420923967.

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This article presents a collision avoidance system for multiple robots based on the current autonomous car collision avoidance system. The purpose of the system is to improve the current autonomous car collision avoidance system by including data input of other vehicles’ velocity and positioning via vehicle-to-vehicle communication into the current autonomous car collision avoidance system. There are two TurtleBots used in experimental testing. TurtleBot is used as the robot agent while Google Lightweight Communication and Marshalling is used for inter-robot communication. Additionally, Gazebo

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Song, Qisong, Shaobo Li, Jing Yang, et al. "Intelligent Optimization Algorithm-Based Path Planning for a Mobile Robot." Computational Intelligence and Neuroscience 2021 (September 29, 2021): 1–17. http://dx.doi.org/10.1155/2021/8025730.

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The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an imp

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Feng, Guoxu, Songbo Gu, and Shihu Sun. "Intelligent Ship Collision Avoidance Support System Based on the Algorithm of Anthropomorphic Physics." International Journal of Ambient Computing and Intelligence 15, no. 1 (2024): 1–20. https://doi.org/10.4018/ijaci.365340.

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Most of the collision-related decisions of ships at sea depend on the working experience of drivers and determining a reasonable avoidance decision quickly when facing a multivessel encounter situation is difficult, so applying intelligent algorithms to assist these decisions is necessary. On the basis of this, the authors researched the construction of intelligent decision support systems for ship collision avoidance that relies on an anthropomorphic physics optimization algorithm. They used this algorithm to obtain the global range optimal solutions through iteration, which provides effectiv

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Zhang, Guangyu, Yan Wang, Jian Liu, Wei Cai, and Hongbo Wang. "Collision-Avoidance Decision System for Inland Ships Based on Velocity Obstacle Algorithms." Journal of Marine Science and Engineering 10, no. 6 (2022): 814. http://dx.doi.org/10.3390/jmse10060814.

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Due to the complex hydrology and narrow channels of inland rivers, ship collision accidents occur frequently. The traditional collision-avoidance algorithms are often aimed at sea areas, and not often at inland rivers. To solve the problem of inland-ship collision avoidance, this paper proposes an inland-ship collision-avoidance decision system based on the velocity obstacle algorithm. The system is designed to assist ships in achieving independent collision-avoidance operations under the limitation of maneuverability while meeting inland-ship collision-avoidance regulations. First, the paper

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Xu, Zhao, Jinwen Hu, Yunhong Ma, Man Wang, and Chunhui Zhao. "A Study on Path Planning Algorithms of UAV Collision Avoidance." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 1 (2019): 100–106. http://dx.doi.org/10.1051/jnwpu/20193710100.

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The unmanned aerial vehicle (UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms, the artificial potential field, fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile, an improved

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Li, Jinxin, Hongbo Wang, Wei Zhao, and Yuanyuan Xue. "Ship’s Trajectory Planning Based on Improved Multiobjective Algorithm for Collision Avoidance." Journal of Advanced Transportation 2019 (April 9, 2019): 1–12. http://dx.doi.org/10.1155/2019/4068783.

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With vigorous development of the maritime trade, many intelligent algorithms have been proposed to avoid collisions due to resulting casualties and increased costs. According to the international regulations for preventing collisions at sea (COLREGs) and the self-evolution ability of the intelligent algorithm, the collision avoidance trajectory can be more consistent with the requirements of reality and maritime personnel. In this paper, the optimization of ship collision avoidance strategies is realized by both an improved multiobjective optimization algorithm NSGA-II and the ship domain unde

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Lu, Liang, Adrian Carrio, Carlos Sampedro, and Pascual Campoy. "A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor." Remote Sensing 13, no. 9 (2021): 1796. http://dx.doi.org/10.3390/rs13091796.

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Collision-avoidance is a crucial research topic in robotics. Designing a collision-avoidance algorithm is still a challenging and open task, because of the requirements for navigating in unstructured and dynamic environments using limited payload and computing resources on board micro aerial vehicles. This article presents a novel depth-based collision-avoidance method for aerial robots, enabling high-speed flights in dynamic environments. First of all, a depth-based Euclidean distance field mapping algorithm is generated. Then, the proposed Euclidean distance field mapping strategy is integra

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Yunhong, Zhou. "Collaborative obstacle avoidance algorithm and simulation of swarm robots with limited viewing angle." Journal of Physics: Conference Series 2580, no. 1 (2023): 012007. http://dx.doi.org/10.1088/1742-6596/2580/1/012007.

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Abstract In this paper, the problem of collaborative obstacle avoidance of swarm robots with limited view angles is studied. In the case of limited view angles, the control model of swarm robots system is designed. The swarm robots can safely avoid a single static obstacle when the obstacle is within the robot’s visual angle. The main research result of this paper is to design a collaborative obstacle avoidance control algorithm for swarm robots, and subdivide the overall control algorithm into three sub algorithms: formation, obstacle avoidance and navigation. The condition of limited view an

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Rahayu, Abdul Rahman, Masrom Suraya, Omar Normah, and Zakaria Maheran. "An application of machine learning on corporate tax avoidance detection model." International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (2020): 721–25. https://doi.org/10.11591/ijai.v9.i4.pp721-725.

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Corporate tax avoidance reduces government revenues which could limit country development plans. Thus, the main objectives of this study is to establish a rigorous and effective model to detect corporate tax avoidance to assist government to prevent such practice. This paper presents the fundamental knowledge on the design and implementation of machine learning model based on five selected algorithms tested on the real dataset of 3,365 Malaysian companies listed on bursa Malaysia from 2005 to 2015. The performance of each machine learning algorithms on the tested dataset has been observed base

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Hao, Qixuan. "The Achievement of Dynamic Obstacle Avoidance Based on Improved Q-Learning Algorithm." Highlights in Science, Engineering and Technology 63 (August 8, 2023): 252–58. http://dx.doi.org/10.54097/hset.v63i.10883.

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Dynamic obstacle avoidance is a classic problem in robot control, which involves the ability of a robot to avoid obstacles in the environment and reach its destination. Among various path planning algorithms, the dynamic obstacle avoidance issue may be resolved using the reinforcement learning algorithm Q-learning. This article provides a comprehensive review of the recent research progress and achievements in the field of dynamic obstacle avoidance, through the analysis and improvement of the Q-learning algorithm. The article begins by introducing the background and research status of dynamic

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Yang, Weishan, Yuepeng Chen, and Yixin Su. "A Double-Layer Model Predictive Control Approach for Collision-Free Lane Tracking of On-Road Autonomous Vehicles." Actuators 12, no. 4 (2023): 169. http://dx.doi.org/10.3390/act12040169.

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This paper proposes a double-layer model predictive control (MPC) algorithm for the integrated path planning and trajectory tracking of autonomous vehicles on roads. The upper module is responsible for generating collision-free lane trajectories, while the lower module is responsible for tracking this trajectory. A simplified vehicle model based on the friction cone is proposed to reduce the computation time for trajectory planning in the upper layer module. To achieve dynamic and accurate collision avoidance, a polygonal distance-based dynamic obstacle avoidance method is proposed. A vertical

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Zhang, Y. J., F. Du, J. Wang, et al. "A Safety Collision Avoidance Algorithm Based on Comprehensive Characteristics." Complexity 2020 (March 17, 2020): 1–13. http://dx.doi.org/10.1155/2020/1616420.

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Aiming at the requirements of vehicle safety collision avoidance system, a safety collision avoidance algorithm based on environmental characteristics and driver characteristics is proposed. By analyzing the relationship between collision avoidance time and the environment, a safety time model is established. In the established safety time model, parameters based on driver characteristics are added, which increases the flexibility of the algorithm. The algorithm can adapt to more different driving conditions and give appropriate warning thresholds. After simulation and comparison with other al

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Yuan, Jianya, Hongjian Wang, Honghan Zhang, Changjian Lin, Dan Yu, and Chengfeng Li. "AUV Obstacle Avoidance Planning Based on Deep Reinforcement Learning." Journal of Marine Science and Engineering 9, no. 11 (2021): 1166. http://dx.doi.org/10.3390/jmse9111166.

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In a complex underwater environment, finding a viable, collision-free path for an autonomous underwater vehicle (AUV) is a challenging task. The purpose of this paper is to establish a safe, real-time, and robust method of collision avoidance that improves the autonomy of AUVs. We propose a method based on active sonar, which utilizes a deep reinforcement learning algorithm to learn the processed sonar information to navigate the AUV in an uncertain environment. We compare the performance of double deep Q-network algorithms with that of a genetic algorithm and deep learning. We propose a line-

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Sang, Songzhen, and Wanlin Li. "P‐19.6: Research on Path Planning Algorithms in Autonomous Driving." SID Symposium Digest of Technical Papers 55, S1 (2024): 1577–80. http://dx.doi.org/10.1002/sdtp.17431.

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Path planning is a critical aspect of autonomous driving, necessitating the calculation of the most efficient route considering real‐time traffic conditions, road status, and obstacles. The primary goal of path planning algorithms is to identify the optimal route from a starting point to a destination within a given environment. In autonomous driving, these algorithms are broadly categorizedinto global planning and local planning. Noteworthy global path planning algorithms include Dijkstra's algorithm, A*, and others. Local path planning algorithms encompass local perception and obstacle avoid

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Wu, Jun Hui, Tong Di Qin, Jie Chen, et al. "Complete Coverage Path Planning and Obstacle Avoidance Strategy of the Robot." Advanced Materials Research 756-759 (September 2013): 497–503. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.497.

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In order to solve the problems of complete coverage path and obstacle avoidance with the mobile robot, the complete coverage planning was described first, and then the algorithm of the complete coverage path planning was analyzed. The complete traversal algorithm and the obstacle avoidance strategy of the robot around the barrier were put forward. Finally, the traversal control flow chart of the traversal robot implemented in Single Chip Microcomputer (SCM) was obtained. After the above analysis, the algorithm was simple, practical, and low repeatability, and high efficiency. The algorithms co

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Seong, Jae-Dong, and Hae-Dong Kim. "Optimization of collision avoidance maneuver planning for cluster satellites in space debris explosion situation." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 3 (2016): 407–22. http://dx.doi.org/10.1177/0954410016682270.

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In this study, the conjunction situation when a number of explosion fragments approach four cluster satellites was simulated, and an optimum avoidance maneuver plan that mitigates this risk was established. The orbits of four deputy satellites around a virtual chief satellite were defined using the Hill–Clohessy–Wiltshire equation, and NASA’s breakup model was applied to similar altitude rocket body to simulate explosions situation. The distribution of 230 explosion fragments after an explosion was simulated, and the size and direction of the Delta-V were applied to each fragment. In this situ

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Basha, Mudasar, Munuswamy Siva Kumar, Mangali Chinna Chinnaiah, et al. "A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot." Sensors 23, no. 23 (2023): 9480. http://dx.doi.org/10.3390/s23239480.

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Service robots perform versatile functions in indoor environments. This study focuses on obstacle avoidance using flock-type indoor-based multi-robots. Each robot was developed with rendezvous behavior and distributed intelligence to perform obstacle avoidance. The hardware scheme-based obstacle-avoidance algorithm was developed using a bio-inspired flock approach, which was developed with three stages. Initially, the algorithm estimates polygonal obstacles and their orientations. The second stage involves performing avoidance at different orientations of obstacles using a heuristic based Bug2

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Bykova, V. S., A. I. Mashoshin, and I. V. Pashkevich. "Safe Navigation Algorithm for Autonomous Underwater Vehicles." Giroskopiya i Navigatsiya 29, no. 1 (2021): 97–110. http://dx.doi.org/10.17285/0869-7035.0058.

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Two safe navigation algorithms for autonomous underwater vehicles are described: algorithm for avoidance of point obstacles including all the moving underwater and surface objects, and limited size bottom objects, and algorithm for bypassing extended obstacles such as bottom elevations, rough lower ice edge, garbage patches. These algorithms are developed for a control system of a heavyweight autonomous underwater vehicle.

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Jin, Huawei, Haitao Ji, and Fangzheng Yan. "An Effective Obstacle Avoidance and Motion Planning Design for Underwater Telescopic Arm Robots Based on a Tent Chaotic Dung Beetle Algorithm." Electronics 12, no. 19 (2023): 4128. http://dx.doi.org/10.3390/electronics12194128.

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As the underwater environment is complex, the existence of obstacles will produce a certain collision interference to underwater robot operations, which causes the overall path planning and time costs to increase. In this paper, we propose a Tent chaotic mapping and dung beetle hybrid algorithm (MDBO) application for trajectory optimal planning and effective obstacle avoidance for an underwater telescopic arm robot. The method invokes the unique obstacle avoidance habit and foraging optimization idea of the dung beetle algorithm. Introducing it into the chaotic Tent mapping idea prevents the d

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Xiao, Yineng. "Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms." Computational Intelligence and Neuroscience 2022 (September 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/2938011.

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The age of algorithms is here, and it is really changing people’s lives. More and more ethical problems related to algorithms have attracted people’s attention, but the related ethical research is still far behind the research of algorithms. As more intelligent algorithms emerge in an endless stream, there will also be a lot of algorithmic ethical issues. On the other hand, with the continuous improvement of the development level of the automobile industry, people have a stronger demand for the safety and stability of modern transportation, and more and more autonomous driving technology has b

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Wang, Dan, and Yan Jing. "Obstacle Avoidance for Ship Navigation Safety Combining Heuristic Search Algorithm and Improved ACO Algorithm." Archives of Transport 72, no. 4 (2024): 75–88. https://doi.org/10.61089/aot2024.0ycg1622.

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The safety of ship navigation has always been a focus of attention in the field of maritime transport and navigation. In the complex marine environment, ships face a variety of obstacles, such as other ships, reefs, buoys, etc., which may pose a threat to navigation safety. Traditional obstacle avoidance methods mainly rely on the navigator's empirical judgement, but there are limitations and risks associated with this method. The standard ant colony optimisation algorithm tends to fall into local optimal solutions during path search, while the A* algorithm is easily limited by the search spac

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Han-Pang, Huang, and Lee Pei-Chien. "A Real-time Algorithm for Obstacle Avoidance of Autonomous Mobile Robots." Robotica 10, no. 3 (1992): 217–27. http://dx.doi.org/10.1017/s0263574700007955.

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SUMMARYA real-time obstacle avoidance algorithm is proposed for autonomous mobile robots. The algorithm is sensor-based and consists of a H-mode and T-mode. The algorithm can deal with a complicated obstacle environment, such as multiple concave and convex obstacles. It will be shown that the algorithm is more efficient and more robust than other sensor-based algorithms. In addition, the algorithm will guarantee a solution for the obstacle avoidance problem. Since the algorithm only takes up a small computational time, it can be implemented in real time.

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Kandathil, Jom J., Robins Mathew, and Somashekhar S. Hiremath. "Modified bug-1 algorithm based strategy for obstacle avoidance in multi robot system." MATEC Web of Conferences 144 (2018): 01012. http://dx.doi.org/10.1051/matecconf/201814401012.

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One of the primary ability of an intelligent mobile robot system is obstacle avoidance. BUG algorithms are classic examples of the algorithms used for achieving obstacle avoidance. Unlike many other planning algorithms based on global knowledge, BUG algorithms assume only local knowledge of the environment and a global goal. Among the variations of the BUG algorithms that prevail, BUG-0, BUG-1 and BUG-2 are the more prominent versions. The exhaustive search algorithm present in BUG-1 makes it more reliable and safer for practical applications. Overall, this provides a more predictable and depe

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Chen, Rui. "Research on Indoor Automatic Path Planning Algorithm for Robots." Frontiers in Computing and Intelligent Systems 9, no. 3 (2024): 49–55. http://dx.doi.org/10.54097/sg3gvf34.

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The field of robotics has significantly advanced, especially in indoor autonomous navigation, with applications in households, offices, factories, and tourism. This research focuses on developing and optimizing algorithms for indoor autonomous path planning, aiming to enhance robots' ability to navigate efficiently and safely in various indoor environments. The research adopts a comprehensive methodological approach, beginning with a literature review to understand current state-of-the-art techniques in path planning and obstacle avoidance. Novel algorithms were designed and implemented, focus

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Yu, Mengxue, Qiang Luo, Haibao Wang, and Yushu Lai. "Electric Logistics Vehicle Path Planning Based on the Fusion of the Improved A-Star Algorithm and Dynamic Window Approach." World Electric Vehicle Journal 14, no. 8 (2023): 213. http://dx.doi.org/10.3390/wevj14080213.

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The study of path-planning algorithms is crucial for an electric logistics vehicle to reach its target point quickly and safely. In light of this, this work suggests a novel path-planning technique based on the improved A-star (A*) fusion dynamic window approach (DWA). First, compared to the A* algorithm, the upgraded A* algorithm not only avoids the obstruction border but also removes unnecessary nodes and minimizes turning angles. Then, the DWA algorithm is fused with the enhanced A* algorithm to achieve dynamic obstacle avoidance. In addition to RVIZ of ROS, MATLAB simulates and verifies th

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Qin, Hongwei, Shiliang Shao, Ting Wang, Xiaotian Yu, Yi Jiang, and Zonghan Cao. "Review of Autonomous Path Planning Algorithms for Mobile Robots." Drones 7, no. 3 (2023): 211. http://dx.doi.org/10.3390/drones7030211.

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Mobile robots, including ground robots, underwater robots, and unmanned aerial vehicles, play an increasingly important role in people’s work and lives. Path planning and obstacle avoidance are the core technologies for achieving autonomy in mobile robots, and they will determine the application prospects of mobile robots. This paper introduces path planning and obstacle avoidance methods for mobile robots to provide a reference for researchers in this field. In addition, it comprehensively summarizes the recent progress and breakthroughs of mobile robots in the field of path planning and disc

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Cui, Zhewen, Wei Guan, Xianku Zhang, and Cheng Zhang. "Autonomous Navigation Decision-Making Method for a Smart Marine Surface Vessel Based on an Improved Soft Actor–Critic Algorithm." Journal of Marine Science and Engineering 11, no. 8 (2023): 1554. http://dx.doi.org/10.3390/jmse11081554.

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In this study, an intelligent hybrid algorithm based on deep-reinforcement learning (DRL) is proposed to achieve autonomous navigation and intelligent collision avoidance for a smart autonomous marine surface vessel (SMASV). First, the kinematic model of the SMASV is used, and clauses 13 to 17 of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) are introduced. Then, the electronic chart is rasterized and used for path planning. Next, states, actions, and reward functions are designed, and collision avoidance strategies are formulated. In addition, a te

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