A review of the application of fuzzy mathematical algorithm-based approach in autonomous vehicles and drones

被引:4
作者
Singh, Rashmi [1 ]
Nishad, D. K. [2 ]
Khalid, Saifullah [3 ]
Chaudhary, Aryan [4 ]
机构
[1] Amity Univ Uttar Pradesh, Amity Inst Appl Sci, Noida, India
[2] Dr Shakuntala Misra Natl Rehabil Univ, Elect Engn Dept, Lucknow, India
[3] Airports Author India, Amritsar, India
[4] BioTech Sphere Res, Kolkata, India
关键词
Fuzzy logic; Autonomous vehicles; Drones; Uncertainty; Control; Decision-making; Path planning; Obstacle avoidance; Artificial intelligence; MOBILE ROBOT NAVIGATION; DECISION-MAKING; LOGIC; SETS; OPTIMIZATION; AGGREGATION; DESIGN; DEFUZZIFICATION; UNCERTAINTY; CONTROLLERS;
D O I
10.1007/s41315-024-00385-4
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Autonomous vehicles (AVs) and unmanned aerial vehicles (UAVs) have brought about transformative changes in transportation and aviation. However, making these systems fully autonomous and able to navigate safely in unpredictable real-world situations remains a big challenge. Fuzzy logic and related mathematical algorithms have emerged as practical tools to tackle uncertainty and complex decision-making in these systems. This paper reviews how fuzzy logic and mathematical approaches are applied in areas like navigation, control, avoiding obstacles, planning routes, and decision-making for AVs and UAVs. It delves into the key methods, designs, pros, and cons of using fuzzy logic in autonomous vehicles. The paper also compares fuzzy logic with other AI techniques. The review shows that fuzzy logic manages the uncertainties and imprecision involved in how autonomous vehicles perceive and navigate dynamic environments. Fuzzy controllers often perform better than traditional methods in vehicle control and UAV direction control. High-level decisions and route planning in AVs have also benefited from fuzzy inference systems. Still, challenges like computational efficiency, adaptability, and integrating fuzzy logic with other AI components remain. The paper concludes with suggestions for future research to make autonomous vehicles and drones smarter and safer using fuzzy logic. This review is a useful guide for anyone developing intelligent autonomous systems.
引用
收藏
页码:344 / 364
页数:21
相关论文
共 112 条
[1]  
Alonso JM, 2015, SPRINGER HANDBOOK OF COMPUTATIONAL INTELLIGENCE, P219
[2]  
[Anonymous], 1980, Fuzzy Sets and Systems: Theory and Applications
[3]  
Astudillo L, 2006, ENG LET, V13
[4]  
Auephanwiriyakul S, 2002, PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOL 1 & 2, P1321, DOI 10.1109/FUZZ.2002.1006695
[5]   Time to market prediction using type-2 fuzzy sets [J].
Baguley, P. ;
Page, T. ;
Koliza, V. ;
Maropoulos, P. .
JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT, 2006, 17 (04) :513-520
[6]   Prediction of retinopathy in diabetic patients using type-2 fuzzy regression model [J].
Bajestani, Narges Shafaei ;
Kamyad, Ali Vahidian ;
Esfahani, Ensieh Nasli ;
Zare, Assef .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2018, 264 (03) :859-869
[7]   Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy regression model [J].
Bajestani, Narges Shafaei ;
Kamyad, Ali Vahidian ;
Esfahani, Ensieh Nasli ;
Zare, Assef .
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2017, 37 (02) :281-289
[8]  
Bashir Z., 2020, APPL MATH, DOI [10.1007/s40314-019-1008-0, DOI 10.1007/S40314-019-1008-0]
[9]   Design of Mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm [J].
Belarbi, K ;
Titel, F ;
Bourebia, W ;
Benmahammed, K .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2005, 18 (07) :875-880
[10]   A unified method of defuzzification for type-2 fuzzy numbers with its application to multiobjective decision making [J].
Biswas A. ;
De A.K. .
Granular Computing, 2018, 3 (04) :301-318