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Journal articles on the topic 'Autonomous algorithms'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Tang, Yuexia, Muhammad Aizzat Zakaria, and Maryam Younas. "Path Planning Trends for Autonomous Mobile Robot Navigation: A Review." Sensors 25, no. 4 (2025): 1206. https://doi.org/10.3390/s25041206.

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With the development of robotics technology, there is a growing demand for robots to perform path planning autonomously. Therefore, rapidly and safely planning travel routes has become an important research direction for autonomous mobile robots. This paper elaborates on traditional path-planning algorithms and the limitations of these algorithms in practical applications. Meanwhile, in response to these limitations, it reviews the current research status of recent improvements to these traditional algorithms. The results indicate that these improved path-planning algorithms perform well in te

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Xu, Xincheng. "Research on visual SLAM for dynamic scenes." Applied and Computational Engineering 12, no. 1 (2023): 34–38. http://dx.doi.org/10.54254/2755-2721/12/20230291.

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Simultaneous Localization and Mapping (SLAM) technology has many applications, such as intelligent robots, autonomous driving, and smart cities. It is a key technology for achieving autonomous robot navigation and environmental awareness. Robots need to perceive the surrounding environment in real-time and construct maps while conducting autonomous localization and path planning, which involves algorithms of SLAM technology. This article aims to introduce the research on visual SLAM algorithms in dynamic scenes. This article will first introduce the traditional SLAM algorithm, its research sta

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Caselli, Nicolás, Ricardo Soto, Broderick Crawford, Sergio Valdivia, Elizabeth Chicata, and Rodrigo Olivares. "Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization." Biomimetics 9, no. 1 (2023): 7. http://dx.doi.org/10.3390/biomimetics9010007.

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In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term “autonomous” refers to these variants’ ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with auton

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Shi, Shangzhe. "Research Status of Autonomous Vehicle Path Planning." SHS Web of Conferences 144 (2022): 02019. http://dx.doi.org/10.1051/shsconf/202214402019.

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In recent years, many companies have been developing path planning algorithms and hardware for autonomous vehicles. This paper mainly introduces the latest research and applications of path planning algorithms and related hardware for autonomous vehicles. The introduced algorithms include improvements to Traditional Algorithms, Intelligent Optimization Algorithms, Reinforcement Learning Algorithms and Hybrid Algorithms, such as improving algorithm running speed, path planning ability, and environment learning speed. In terms of hardware research and application, due to the rise of sensor techn

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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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Sathyamangalam Imran, Mohammed Irshadh Ismaaeel, Satyesh Shanker Awasthi, Michael Khayyat, Stefano Arrigoni, and Francesco Braghin. "Virtual Validation and Uncertainty Quantification of an Adaptive Model Predictive Controller-Based Motion Planner for Autonomous Driving Systems." Future Transportation 4, no. 4 (2024): 1537–58. https://doi.org/10.3390/futuretransp4040074.

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In the context of increasing research on algorithms for different modules of the autonomous driving stack, the development and evaluation of these algorithms for deployment onboard vehicles is the next critical step. In the development and verification phases, simulations play a pivotal role in achieving this aim. The uncertainty quantification of Autonomous Vehicle (AV) systems could be used to enhance safety assurance and define the error-handling capabilities of autonomous driving systems (ADSs). In this paper, a virtual validation methodology for the control module of an autonomous driving

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He, Weikun. "Analysis of the Applications of Algorithm and Automatic Pathfinding." Applied and Computational Engineering 109, no. 1 (2024): 97–102. http://dx.doi.org/10.54254/2755-2721/109/20241456.

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Abstract. The current development of autonomous driving technology is very hot, which involves two fundamental aspects: one is the operating system as the foundation, and the other is the algorithm application. The theme of this review is to study the related algorithmic technologies and combine them with one of the key functions of autonomous driving: autonomous routing. The review discusses the application direction and environment of this function, and involves the use of algorithms in the backend. Autonomous routing is a key concept in multiple technical fields and plays an important role

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ANDRECUT, M., and M. K. ALI. "DEEP-SARSA: A REINFORCEMENT LEARNING ALGORITHM FOR AUTONOMOUS NAVIGATION." International Journal of Modern Physics C 12, no. 10 (2001): 1513–23. http://dx.doi.org/10.1142/s0129183101002851.

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In this paper we discuss the application of reinforcement learning algorithms to the problem of autonomous robot navigation. We show that the autonomous navigation using the standard delayed reinforcement learning algorithms is an ill posed problem and we present a more efficient algorithm for which the convergence speed is greatly improved. The proposed algorithm (Deep-Sarsa) is based on a combination between the Depth-First Search (a graph searching algorithm) and Sarsa (a delayed reinforcement learning algorithm).

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Arunprasad, Veeranan, Brijendra Gupta, Karthikeyan T, and Muruganantham Ponnusamy. "HYBRID NEURO-FUZZY-GENETIC ALGORITHMS FOR OPTIMAL CONTROL OF AUTONOMOUS SYSTEMS." ICTACT Journal on Soft Computing 13, no. 4 (2023): 3015–20. http://dx.doi.org/10.21917/ijsc.2023.0424.

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In recent years, there has been an increasing demand for efficient and robust control algorithms to optimize the performance of autonomous systems. Traditional control techniques often struggle to handle the complexity and uncertainty associated with such systems. To address these challenges, hybrid neuro-fuzzy-genetic algorithms have emerged as a promising approach. This paper presents a comprehensive review of the application of hybrid neuro-fuzzy-genetic algorithms for optimal control of autonomous systems. The proposed algorithms combine the strengths of neural networks, fuzzy logic, and g

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Goldman, R. E., A. Bajo, and N. Simaan. "Algorithms for autonomous exploration and estimation in compliant environments." Robotica 31, no. 1 (2012): 71–87. http://dx.doi.org/10.1017/s0263574712000100.

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SUMMARYThis paper investigates algorithms for enabling surgical slave robots to autonomously explore shape and stiffness of surgical fields. The paper addresses methods for estimating shape and impedance parameters of tissue and methods for autonomously exploring perceived impedance during tool interaction inside a tissue cleft. A hybrid force-motion controller and a cycloidal motion path are proposed to address shape exploration. An adaptive exploration algorithm for segmentation of surface features and a predictor-corrector algorithm for exploration of deep features are introduced based on d

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Lisowski, Józef. "A Synthesis of Algorithms Determining a Safe Trajectory in a Group of Autonomous Vehicles Using a Sequential Game and Neural Network." Electronics 12, no. 5 (2023): 1236. http://dx.doi.org/10.3390/electronics12051236.

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This paper presents a solution to the problem of providing an autonomous vehicle with a safe control task when moving around many other autonomous vehicles. This is achieved by developing an appropriate computer control algorithm that takes into account the possible risk of a collision resulting from both the impact of environmental disturbances and the imperfection of the rules of maneuvering in situations where many vehicles pass each other, giving the control process a decisive character. For this purpose, three types of algorithms were synthesized: kinematic and dynamic optimization with n

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Valbahs, Edvards, and Peter Grabusts. "Path Planning Usage for Autonomous Agents." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (August 8, 2015): 40. http://dx.doi.org/10.17770/etr2013vol2.867.

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In order to achieve the wide range of the robotic application it is necessary to provide iterative motions among points of the goals. For instance, in the industry mobile robots can replace any components between a storehouse and an assembly department. Ammunition replacement is widely used in military services. Working place is possible in ports, airports, waste site and etc. Mobile agents can be used for monitoring if it is necessary to observe control points in the secret place. The paper deals with path planning programme for mobile robots. The aim of the research paper is to analyse motio

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Clark, Evan B., Nathan E. Bramall, Brent Christner, et al. "An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot." International Journal of Astrobiology 17, no. 3 (2017): 247–57. http://dx.doi.org/10.1017/s1473550417000313.

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AbstractThe development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reas

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Shu, Xiao Meng, Da Ming Jiang, and Lian Dai. "Study on Improved APF Algorithm for Autonomous Mobile Robot." Applied Mechanics and Materials 519-520 (February 2014): 1337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1337.

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In algorithms of obstacle avoidance for autonomous mobile robot, APF algorithm is simple, real-time and smooth, but has some limitations for solving problems. For example, the local minimum point may trap mobile robots before reaching its goal. Even though many improved APF algorithms have been put forward, few articles describe the process in detail to show how these algorithms are applied. Considering above factors, this paper focuses on embodiment of abstract improved theory for APF algorithm by showing some changes with formulas and parameters. The whole work has been done in simulation en

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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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Hwang, Kitae, In Hwan Jung, and Jae Moon Lee. "Implementation of Autonomous Driving on RC-CAR with Raspberry PI and AI Server." Webology 19, no. 1 (2022): 4444–58. http://dx.doi.org/10.14704/web/v19i1/web19293.

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A lot of research is being done on autonomous driving vehicles or robots that recognize objects and drive themselves without human intervention. In order to develop autonomous driving technology, there is a fundamental difficulty in securing expensive real cars equipped with various sensors. In this paper, an autonomous driving system development platform was developed using an inexpensive RC-Car, and a test system that can test various algorithms related to autonomous driving was introduced. In the system developed in this study, the single board computer Raspberry PI was mounted on the RC-Ca

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James Whitmore, Priya Mehra, Oliver Hastings, and Emily Linford. "Optimization and Empirical Analysis of Path Planning Algorithms for High-Speed Autonomous Driving Scenarios." Frontiers in Artificial Intelligence Research 2, no. 1 (2025): 76–83. https://doi.org/10.71465/fair252.

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High-speed autonomous driving on highways demands precise and low-latency path planning to ensure vehicle safety and operational efficiency. This study proposes a novel high-speed path planning algorithm that integrates sampling-based optimization with behavior prediction mechanisms. The algorithm incorporates vehicle dynamics constraints and a real-time risk assessment model to enhance decision-making capabilities under high-speed and complex traffic conditions. Comprehensive experiments were conducted using the HighD dataset. Comparative analysis with baseline algorithms, including Rapidly-e

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Lu, Haodong, and Haoran Xu. "Uncertainty-aware motion planning for autonomous vehicle: A review." Applied and Computational Engineering 55, no. 1 (2024): 232–40. http://dx.doi.org/10.54254/2755-2721/55/20241527.

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This paper reviews a recently developed uncertainty-aware motion planning algorithm vastly applied to autonomous vehicles. Many vehicle manufacturers shifted their focus from improving vehicle energy conversion efficiency to autonomous driving, aiming to bring a better and more relaxed driving experience to drivers. However, many past motion planning algorithms used for autonomous driving were immature, so many errors were reported. These errors may put human drivers in life-threatening danger. Consisting of two connected systems supported by a well-trained graph neural network, the uncertaint

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Cong, Zheng, Ye Li, Yanqing Jiang, et al. "An evaluation of path-planning methods for autonomous underwater vehicle based on terrain-aided navigation." International Journal of Advanced Robotic Systems 16, no. 3 (2019): 172988141985318. http://dx.doi.org/10.1177/1729881419853181.

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This article presents a comparison of different path-planning algorithms for autonomous underwater vehicles using terrain-aided navigation. Four different path-planning methods are discussed: the genetic algorithm, the A* algorithm, the rapidly exploring random tree* algorithm, and the ant colony algorithm. The goal of this article is to compare the four methods to determine how to obtain better positioning accuracy when using terrain-aided navigation as a means of navigation. Each algorithm combines terrain complexity to comprehensively consider the motion characteristics of the autonomous un

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Elbanhawi, Mohamed, Milan Simic, and Reza Jazar. "Autonomous Robots Path Planning: An Adaptive Roadmap Approach." Applied Mechanics and Materials 373-375 (August 2013): 246–54. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.246.

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Developing algorithms that allow robots to independently navigate unknown environments is a widely researched area of robotics. The potential for autonomous mobile robots use, in industrial and military applications, is boundless. Path planning entails computing a collision free path from a robots current position to a desired target. The problem of path planning for these robots remains underdeveloped. Computational complexity, path optimization and robustness are some of the issues that arise. Current algorithms do not generate general solutions for different situations and require user expe

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Guo, Yinjing, Hui Liu, Xiaojing Fan, and Wenhong Lyu. "Research Progress of Path Planning Methods for Autonomous Underwater Vehicle." Mathematical Problems in Engineering 2021 (February 12, 2021): 1–25. http://dx.doi.org/10.1155/2021/8847863.

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Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model sear

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Muhsen, Dena Kadhim, Firas Abdulrazzaq Raheem, and Ahmed T. Sadiq. "A Systematic Review of Rapidly Exploring Random Tree RRT Algorithm for Single and Multiple Robots." Cybernetics and Information Technologies 24, no. 3 (2024): 78–101. http://dx.doi.org/10.2478/cait-2024-0026.

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Abstract Recent advances in path-planning algorithms have transformed robotics. The Rapidly exploring Random Tree (RRT) algorithm underpins autonomous robot navigation. This paper systematically examines the uses and development of RRT algorithms in single and multiple robots to demonstrate their importance in modern robotics studies. To do this, we have reviewed 70 works on RRT algorithms in single and multiple robot path planning from 2015 to 2023. RRT algorithm evolution, including crucial turning points and innovative techniques, have been examined. A detailed comparison of the RRT Algorit

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Chen, Danhe, Konstantin Neusypin, Maria Selezneva, and Zhongcheng Mu. "New Algorithms for Autonomous Inertial Navigation Systems Correction with Precession Angle Sensors in Aircrafts." Sensors 19, no. 22 (2019): 5016. http://dx.doi.org/10.3390/s19225016.

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This paper presents new algorithmic methods for accuracy improvement of autonomous inertial navigation systems of aircrafts. Firstly, an inertial navigation system platform and its nonlinear error model are considered, and the correction schemes are presented for autonomous inertial navigation systems using internal information. Next, a correction algorithm is proposed based on signals from precession angle sensors. A vector of reduced measurements for the estimation algorithm is formulated using the information about the angles of precession. Finally, the accuracy of the developed correction

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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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Minh, Vu Trieu, and John Pumwa. "Feasible Path Planning for Autonomous Vehicles." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/317494.

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The objective of this paper is to find feasible path planning algorithms for nonholonomic vehicles including flatness, polynomial, and symmetric polynomial trajectories subject to the real vehicle dynamical constraints. Performances of these path planning methods are simulated and compared to evaluate the more realistic and smoother generated trajectories. Results show that the symmetric polynomial algorithm provides the smoothest trajectory. Therefore, this algorithm is recommended for the development of an automatic control for autonomous vehicles.

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Gafarov, Evgeny R., and Frank Werner. "Connected and Autonomous Vehicle Scheduling Problems: Some Models and Algorithms." Algorithms 17, no. 9 (2024): 421. http://dx.doi.org/10.3390/a17090421.

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In this paper, we consider some problems that arise in connected and autonomous vehicle (CAV) systems. Their simplified variants can be formulated as scheduling problems. Therefore, scheduling solution algorithms can be used as a part of solution algorithms for real-world problems. For four variants of such problems, mathematical models and solution algorithms are presented. In particular, three polynomial algorithms and a branch and bound algorithm are developed. These CAV scheduling problems are considered in the literature for the first time. More complicated NP-hard scheduling problems rel

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Gal, Michal. "Algorithmic Challenges to Autonomous Choice." Michigan Technology Law Review, no. 25.1 (2018): 59. http://dx.doi.org/10.36645/mtlr.25.1.algorithmic.

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We then explore how autonomous algorithmic assistants affect the legal framework. Some issues challenge the very use of algorithmic assistants: Should the law place an age limit on the use of such algorithms? Should legal limits be placed on their use in certain spheres? Other issues arise from the construction of preferences by algorithms. To wit, in a market in which demand is driven by algorithms, can we necessarily regard the choices made as expressions of user preferences that serve our socio-political goals? Even if the answer is positive—does this new mode of user choice fit current leg

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Li, Shuang, Ruikun Lu, Liu Zhang, and Yuming Peng. "Image Processing Algorithms For Deep-Space Autonomous Optical Navigation." Journal of Navigation 66, no. 4 (2013): 605–23. http://dx.doi.org/10.1017/s0373463313000131.

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As Earth-based radio tracking navigation is severely limited because of communications constraints and low relative navigation accuracy, autonomous optical navigation capabilities are essential for both robotic and manned deep-space exploration missions. Image processing is considered one of the key technologies for autonomous optical navigation to extract high-precision navigation observables from a raw image. New image processing algorithms for deep-space autonomous optical navigation are developed in this paper. First, multiple image pre-processing and the Canny edge detection algorithm are

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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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Orgován, László, Tamás Bécsi, and Szilárd Aradi. "Autonomous Drifting Using Reinforcement Learning." Periodica Polytechnica Transportation Engineering 49, no. 3 (2021): 292–300. http://dx.doi.org/10.3311/pptr.18581.

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Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. Th

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Muhammad Aria and Jana Utama. "An Autonomous Parking System using the Hybridization of the Rapidly-Exploring Random Trees Star and Ant Colony System." Journal of Advanced Research in Applied Sciences and Engineering Technology 31, no. 1 (2023): 291–97. http://dx.doi.org/10.37934/araset.31.1.291297.

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One key autonomous driving car application that can be utilized to address the problem of parking spaces is autonomous parking. The goal of this study is to develop a path planning algorithm that can swiftly create a path for autonomous parking in a variety of car parking settings. The method employed is a mix of Rapidly Exploring Random Tree Star and Ant Colony Systems (RRT-ACS). The RRT-ACS method can quickly provide an ideal path. The reed sheep planner technique is also used to provide a smooth curving path that non-holonomic vehicles can follow. The performance of the RRT-ACS algorithm-ba

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Huang, Zhihong, and Jianping Geng. "Research on laser-based mobile robot SLAM and autonomous navigation." Journal of Physics: Conference Series 2711, no. 1 (2024): 012021. http://dx.doi.org/10.1088/1742-6596/2711/1/012021.

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Abstract SLAM (Simultaneous Localization and Mapping) and autonomous navigation systems, as fundamental components of mobile robots, largely determine their ability to accomplish tasks. While research on laser-based SLAM and autonomous navigation algorithms in simulated environments has achieved significant success, there is limited research on deploying these algorithms on physical mobile robots, conducting real-world experiments to provide practical data for validating the accuracy and applicability of theoretical models. This paper begins by presenting the system architecture of the mobile

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Wang, Du. "Research Status of Vehicle Trajectory Planning." Applied and Computational Engineering 111, no. 1 (2024): 54–59. http://dx.doi.org/10.54254/2755-2721/111/2024ch0093.

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As autonomous driving technology has advanced, it has drawn attention from all around the world. The implementation of autonomous driving technology has the potential to enhance traffic safety, minimize traffic accidents, boost efficiency, facilitate travel, conserve energy, and lower emissions. Autonomous driving technology includes environmental perception, path planning, behavioral decision-making and other technologies. Among them, trajectory planning and control technology is the key technology to realize autonomous driving of automobiles and is the concrete embodiment of automobile intel

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Chen, Zhen, Jialin Li, and Xiangdong Liu. "Spacecraft Autonomous GPS Navigation based on Polytopic Linear Differential Inclusion." Journal of Navigation 68, no. 3 (2014): 511–27. http://dx.doi.org/10.1017/s0373463314000770.

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Aiming at improving the poor real-time performance of existing nonlinear filtering algorithms applied to spacecraft autonomous navigation based on Global Positioning System (GPS) measurements and simplifying the algorithm design of navigation algorithms, a spacecraft autonomous navigation algorithm based on polytopic linear differential inclusion is proposed in this paper. Firstly, it is demonstrated that the nonlinear estimation error system of spacecraft autonomous navigation can be modelled as a polytopic linear differential inclusion system model according to the idea of global linearizati

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Moskalenko, V. V., A. S. Moskalenko, A. G. Korobov, and M. O. Zaretskiy. "Aerial compact drone autonomous navigation system – the model and training algorithms." Bionics of Intelligence 2, no. 91 (2018): 16–24. https://doi.org/10.30837/bi.2018.2(91).03.

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The paper presents a novel model of convolutional neural network for visual feature extraction, extreme-learning machine for position displacement estimation and boosted information-extreme classifier for obstacle prediction with new training algorithms to build decision rules of autonomous navigation system for compact drones are presented in the paper. Growing sparse-coding neural gas algorithm for unsupervised training of the convolution filters, supervised learning algorithms for training decision rules and simulated annealing search algorithm for fine tuning are proposed. The complex crit

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Gebrekidan, Semere B., and Steffen Marburg. "Autonomous design of noise-mitigating structures using deep reinforcement learning." Journal of the Acoustical Society of America 156, no. 1 (2024): 151–63. http://dx.doi.org/10.1121/10.0026474.

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This paper explores the application of deep reinforcement learning for autonomously designing noise-mitigating structures. Specifically, deep Q- and double deep Q-networks are employed to find material distributions that result in broadband noise mitigation for reflection and transmission problems. Unlike conventional deep learning approaches which require prior knowledge for data labeling, the double deep Q-network algorithm learns configurations that result in broadband noise mitigations without prior knowledge by utilizing pixel-based inputs. By employing unified hyperparameters and network

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Gillies, E. A., A. G. Y. Johnston, and C. R. McInnes. "Action Selection Algorithms for Autonomous Microspacecraft." Journal of Guidance, Control, and Dynamics 22, no. 6 (1999): 914–16. http://dx.doi.org/10.2514/2.4473.

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Chanthery, Elodie, Magali Barbier, and Jean-Loup Farges. "PLANNING ALGORITHMS FOR AUTONOMOUS AERIAL VEHICLE." IFAC Proceedings Volumes 38, no. 1 (2005): 61–66. http://dx.doi.org/10.3182/20050703-6-cz-1902.02062.

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Manikas, Theodore, Kaveh Ashenayi, and Roger Wainwright. "Genetic algorithms for autonomous robot navigation." IEEE Instrumentation & Measurement Magazine 10, no. 6 (2007): 26–31. http://dx.doi.org/10.1109/mim.2007.4428579.

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Achtenová, Gabriela, and Ondrej Korec. "Autonomous driving algorithms in scaled environment." Journal of Middle European Construction and Design of Cars 9, no. 3 (2011): 23–27. http://dx.doi.org/10.2478/v10138-011-0016-y.

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SHRNUTÍ Članek se zabyva zhodnocenim vyvoje projektu SMALL na ČVUT v Praze, ktery se tykal zkoušek systemů vedouci k autonomnimu navaděni vozidel. Pomoci zmenšeneho modelu vozidla byly odzkoušeny algoritmy adaptivniho tempomatu, včetně systemu Stop&Go a sledovani trajektorie vpředu jedouciho vozidla. Pomoci podobnostniho teoremu je možne vysledky přepočitat na skutečne vozidlo a předpovidat tak chovani vozidla v koloně, či stabilitu kolony vozidel.

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Maturana, Jorge, Frédéric Lardeux, and Frédéric Saubion. "Autonomous operator management for evolutionary algorithms." Journal of Heuristics 16, no. 6 (2010): 881–909. http://dx.doi.org/10.1007/s10732-010-9125-3.

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Jin, Xianjian, Hang Yang, Xin Liao, et al. "A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving." Machines 10, no. 7 (2022): 507. http://dx.doi.org/10.3390/machines10070507.

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Robust and precise vehicle detection is the prerequisite for decision-making and motion planning in autonomous driving. Vehicle detection algorithms follow three steps: ground segmentation, obstacle clustering and bounding box fitting. The ground segmentation result directly affects the input of the subsequent obstacle clustering algorithms. Aiming at the problems of over-segmentation and under-segmentation in traditional ground segmentation algorithms, a ground segmentation algorithm based on Gaussian process is proposed in this paper. To ensure accurate search of real ground candidate points

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Wu, Fei, and Yueyang Zhao. "Non-contact automatic COVID-19 nucleic acid detection robot." Applied and Computational Engineering 5, no. 1 (2023): 848–52. http://dx.doi.org/10.54254/2755-2721/5/20230726.

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This product is mainly used to realise the contactless automatic COVID-19 nucleic acid detection robot, which can navigate to the workstation or location of the set personnel through the camera and radar and is mainly applied to the autonomous movement function of the existing automatic nucleic acid detection robot. The robot will conduct intelligent navigation according to the position of the nucleic acid person, avoid obstacles autonomously through radar and camera, and use clever navigation to plan the nearest route to the position part of the needed nucleic acid. This product carries a cam

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Cao, Jingwei, Chuanxue Song, Shixin Song, et al. "Robust Object Tracking Algorithm for Autonomous Vehicles in Complex Scenes." Remote Sensing 13, no. 16 (2021): 3234. http://dx.doi.org/10.3390/rs13163234.

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Object tracking is an essential aspect of environmental perception technology for autonomous vehicles. The existing object tracking algorithms can only be applied well to simple scenes. When the scenes become complex, the algorithms have poor tracking performance and insufficient robustness, and the problems of tracking drift and object loss are prone to occur. Therefore, a robust object tracking algorithm for autonomous vehicles in complex scenes is proposed. Firstly, we study the Siam-FC network and related algorithms, and analyze the problems that need to be addressed in object tracking. Se

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Kurdi, Heba, Shaden Almuhalhel, Hebah Elgibreen, et al. "Tide-Inspired Path Planning Algorithm for Autonomous Vehicles." Remote Sensing 13, no. 22 (2021): 4644. http://dx.doi.org/10.3390/rs13224644.

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With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the M

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Naz, Neelma, Muhammad Khurram Ehsan, Muhammad Rizwan Amirzada, Md Yeakub Ali, and Muhammad Aasim Qureshi. "Intelligence of Autonomous Vehicles: A Concise Revisit." Journal of Sensors 2022 (April 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/2690164.

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Artificial intelligence- (AI-) empowered machines are devised to mimic human actions. In the automotive industry, AI plays a significant role in the development of vehicular technology. AI joins hands with the field of mechatronics to assist in the accurate execution of the vehicle functionalities. Autonomous vehicles get the scene information by using onboard sensors such as laser, radar, lidar, Global Positioning System (GPS), and vehicular communication networks. The data obtained is then used for various path planning and control techniques to make the vehicles capable of autonomously driv

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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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HE, YI, XING-BIAO HU, HON-WAH TAM, and YING-NAN ZHANG. "A new method to generate non-autonomous discrete integrable systems via convergence acceleration algorithms." European Journal of Applied Mathematics 27, no. 2 (2015): 194–212. http://dx.doi.org/10.1017/s0956792515000479.

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In this paper, we propose a new algebraic method to construct non-autonomous discrete integrable systems. The method starts from constructing generalizations of convergence acceleration algorithms related to discrete integrable systems. Then the non-autonomous version of the corresponding integrable systems are derived. The molecule solutions of the systems are also obtained. As an example of the application of the method, we propose a generalization of the multistep ϵ-algorithm, and then derive a non-autonomous discrete extended Lotka–Volterra equation. Since the convergence acceleration algo

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Tamkhade, Dr Jayashree, Avanti Savji, Ved Thorat, and Vedashri Patil. "Object Detection and Path Planning Algorithms for Autonomous Vehicles." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem39191.

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Abstract—With the increasing development in Artificial Tech- nology (Ai), the area of application increases as well. One such application of AI is Computer Vision, where the computer learns through images and videos. In this paper, we will explore various path planning and object detection algorithms used in autonomous vehicles. Autonomous vehicles drive without a driver, they are equipped with RADAR, LIDAR sensors and many cameras which acquire the data from the environment. The path planning algorithm uses the data to predict the optimal path for the vehicle to move. Index Terms—Autonomous V

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Han, Yijia. "What role does object detection play in autonomous driving?" Advances in Engineering Innovation 10, no. 1 (2024): 76–84. http://dx.doi.org/10.54254/2977-3903/10/2024105.

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Deep learning-based object detection algorithm is becoming more and more important in autonomous driving area with an increasing amount and trending these days. This article first provides definitions and introduces an autonomous driving and object detection. Subsequently, a detailed discussion is conducted on object detection, comparing traditional object detection methods with deep learning object detection algorithms. The shortcomings of traditional methods highlighted the advantages of deep learning-based object detection algorithms, laying the groundwork for the use of deep learning objec

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