Autonomous ground vehicles: technological advancements, implementation challenges, and future directions

被引：2

作者：

Patkar, Vivek ^{[1
]}

Mehendale, Ninad ^{[1
]}

机构：

[1] Somaiya Vidyavihar Univ, KJ Somaiya Coll Engn, Mumbai 400077, Maharashtra, India

来源：

INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS | 2025年

关键词：

Autonomous ground vehicles; Indian road scenarios; Future mobility; Artificial intelligence; Logistics; COLLISION-AVOIDANCE; STEERING CONTROL; OPTIMIZATION; PERFORMANCE; PERCEPTION; SYSTEMS; FUSION; LIDAR; RRT;

D O I：

10.1007/s41315-024-00418-y

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

Autonomous ground vehicles (AGVs) are revolutionizing transportation with their self-operating capabilities. This review delves into the key technologies powering AGVs, such as perception systems, control mechanisms, and AI integration. It outlines the benefits AGVs offer to transportation, logistics, agriculture, and defense. The paper examines implementation challenges, especially in complex settings like Indian roads. AGVs utilize advanced sensors and algorithms for environmental perception, decision-making, and safe navigation. Machine learning is pivotal in enhancing their performance over time. Despite their potential to improve efficiency and safety, AGVs encounter obstacles including regulatory issues, public acceptance, and technical constraints. The review concludes by exploring the future potential of AGVs and their prospective societal impact. With technological progress, AGVs are set to transform mobility, potentially reshaping urban planning, reducing accidents, and boosting productivity across various industries.

引用

页数：38

共 104 条

[21]

Fang W., 2021, J. Big Data, V3, P97, DOI DOI 10.32604/JBD.2021.016993

[22] Kalman Filter With Recursive Covariance Estimation-Sequentially Estimating Process Noise Covariance [J].

Feng, Bo ;

Fu, Mengyin ;

Ma, Hongbin ;

Xia, Yuanqing ;

Wang, Bo .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2014, 61 (11) :6253-6263

[23] A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision [J].

Georgiou, Theodoros ;

Liu, Yu ;

Chen, Wei ;

Lew, Michael .

INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL, 2020, 9 (03) :135-170

[24]

Ghazi A., An approach of adaptation of autonomous vehicle in the indian smart city

[25] The scope and adaptation strategy for autonomous vehicles from the perspective of Indian smart city [J].

Ghazi, Abdullah ;

Yadav, Devendra ;

Muthusamy, Suresh ;

Mishra, Om Prava ;

Loganathan, Ashok Kumar .

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2023, 45 (03) :8716-8736

[26] State of the art of autonomous agricultural off-road vehicles driven by renewable energy systems [J].

Ghobadpour, Amin ;

Boulon, Loic ;

Mousazadeh, Hossein ;

Malvajerdi, Ahmad Sharifi ;

Rafiee, Shahin .

EMERGING AND RENEWABLE ENERGY: GENERATION AND AUTOMATION, 2019, 162 :4-13

[27]

Graf U, 2019, IEEE INT CONF ROBOT, P5614, DOI [10.1109/ICRA.2019.8794331, 10.1109/icra.2019.8794331]

[28] An LSTM-Based Autonomous Driving Model Using a Waymo Open Dataset [J].

Gu, Zhicheng ;

Li, Zhihao ;

Di, Xuan ;

Shi, Rongye .

APPLIED SCIENCES-BASEL, 2020, 10 (06) :1-14

[29]

Gupta Savyasachi, 2021, Computer Vision and Image Processing: 5th International Conference, CVIP 2020. Communications in Computer and Information Science (1378), P237, DOI 10.1007/978-981-16-1103-2_21

[30]

Haltli J., 2019, Childrens Book and Media Review, V40, P49

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