SpanogianopoulosSGhribiWAlkhayyatA, et al.Non-singular terminal sliding mode with perturbation observer for ear surgery robot. J Intell Rob Syst2025; 111(4): 125.
2.
JalalnezhadM. Neural network-based intelligent path tracking for nonlinear predictive control in wheeled robots. J Braz Soc Mech Sci Eng2025a; 47: 669.
3.
JalalnezhadMSayedBTAlotaibiY, et al.Real-time vision-based obstacle avoidance for mobile robots using lightweight monocular depth estimation and behavior-driven control. J Braz Soc Mech Sci Eng2025b; 47: 668.
4.
WangZSpanogianopoulosSPrasadKDV, et al.Optimal mobile robot routing with neural network and kernel-based dimensionality reduction in unknown environments. J Braz Soc Mech Sci Eng2025; 47: 527.
5.
KareemAKChammamAAl AttabiK. Intelligent vibration control of composite and FGM plates using piezoelectric actuators and optimized sliding mode-based controllers. Journal of Vibration Engineering & Technologies2025; 13(7): 474.
6.
JalalnezhadM. Design of intelligent control systems for damping vibrations of composite sheets using piezoelectricity as a sensor and actuator under various disturbance conditions. J Braz Soc Mech Sci Eng2025c; 47: 495.
7.
RodriguesPAskarSSudhamsuG, et al. Multi-UAV traffic management using predicted control. Aerospace Systems. 2025; 8: 1–29.
8.
SpanogianopoulosSAlferouniMChammamA, et al. Robust finite-time nonlinear control of exoskeleton robots in the presence of unknown friction force, parametric sectional number, and bounded external disturbances. J Braz Soc Mech Sci Eng. 2025; 47(7): 346.
9.
LinXMXiaLGeZ, et al.Designing the optimal path curve based on spline functions for mobile robots using a hybrid bee colony and genetic algorithm. J Vib Control2025; 31(7–8): 1359–1376.
10.
JalalnezhadMFazeliS. Sliding mode control of AUV trajectory tracking in the presence of disturbance. Proc Inst Mech Eng Part E J Process Mech Eng. 2025; 239: 09544089231212424.
11.
JalalnezhadMKeymasi-KhalajiAGhaneM. Adaptive backstepping control for underwater robots in complex environments. Proc IME J Syst Control Eng. 2025; 239: 09596518241300663.
12.
JalalnezhadM. Quadrotor position control using fuzzy adaptive feedback linearization and sliding mode control. Journal of Vibration Engineering & Technologies. 2025; 239: 09596518241300663.
13.
BijuSChammamAAskarS, et al. Prediction-based radial neural network controller for traction control systems. J Vib Control. 2024; 30: 10775463241296911.
14.
HsiehHYChenKHShiehCJ, et al. Urban mobile robot routing using rapid-exploring random tree (RRT) in obstacle environments. J Braz Soc Mech Sci Eng. 2024; 46(10): 612.
15.
Rodriguez-BenitesCBijuNSharmaMK. Robust predictive control of wheeled mobile robots with variable parameters. J Braz Soc Mech Sci Eng. 2023; 237: 09544062241232229.
16.
LinXXiaLRuoyiZ, et al.Design of integral backstepping sliding mode and admittance control of a knee joint robot under noise. ISA (Instrum Soc Am) Trans2021; 115: 32–45.
17.
KhalajiAKJalalnezhadM. Stabilization of a tractor with n trailers in the presence of wheel slip effects. Robotica2021; 39(5): 787–797.
18.
Keymasi KhalajiAJalalnezhadM. Control of a tractor–trailer robot subjected to wheel slip. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multibody Dynamics. 2019; 233(4): 956–967.
19.
LeXLingKZhouL, et al.A novel hybrid biological optimisation algorithm for tackling reservoir optimal operation problem. Ain Shams Eng J2025; 16(4): 103342. https://doi.org/10.1016/j.asej.2025.103342
20.
RahamanSHMaitiMK. A hybridization of grey wolf optimizer and genetic algorithm for the traveling salesman problems. Soft Comput2024; 28(23): 13127–13148. https://doi.org/10.1007/s00500-024-10340-x
21.
ZhenCQuXZhouY, et al.A safe route planning method for aircraft carriers based on improved Dijkstra algorithm. Proc IME M J Eng Marit Environ2025; 239: 14750902251318486–14750902251318834. https://doi.org/10.1177/14750902251318486
22.
MiyomboMELiuYKMulengaCM, et al.Optimal path planning in a real-world radioactive environment: a comparative study of A-star and Dijkstra algorithms. Nucl Eng Des2024; 420: 113039. https://doi.org/10.1016/j.nucengdes.2024.113039
23.
ChuLWangYLiS, et al.Intelligent vehicle path planning based on optimized A* algorithm. Sensors2024; 24(10): 3149. https://doi.org/10.3390/s24103149
24.
ZhangBCaiXLiG, et al.A modified A* algorithm for path planning in the radioactive environment of nuclear facilities. Ann Nucl Energy2025; 214: 111233. https://doi.org/10.1016/j.anucene.2025.111233
25.
ZhangWLiWZhengX, et al.Improved A* and DWA fusion algorithm based path planning for intelligent substation inspection robot. Measurement and Control2025; 59: 00202940251316687. https://doi.org/10.1177/00202940251316687
26.
NguyenTHNguyenXTPhamDA, et al.A new approach for mobile robot path planning based on RRT algorithm. Mod Phys Lett B2023; 37(18): 2340027. https://doi.org/10.1142/S0217984923400274
27.
LeiSLiTGaoX, et al.Research on improved RRT path planning algorithm based on multi-strategy fusion. Sci Rep2025; 15(1): 13312. https://doi.org/10.1038/s41598-025-92675-5
28.
YangHLuoXDuanC, et al. (2025) Research on multi-objective point path planning for mobile inspection robot based on multi-informed-rapidly exploring random tree. Eng Appl Artif Intell151:110645. https://doi.org/10.1016/j.engappai.2025.11064
29.
FanJQuL. A multi-strategy improved sparrow search algorithm for mobile robots path planning. Meas Sci Technol2024; 35(10): 106207. https://doi.org/10.1088/1361-6501/ad56b2
30.
ZhangXDuanYLiX, et al.Path planning for greenhouse robots using a hybrid Dung beetle algorithm. Intelligent Service Robotics2025; 18: 473–497. https://doi.org/10.1007/s11370-025-00592-3
31.
MaKWangLCaiJ, et al.Robot path planning using fusion algorithm of ant colony optimization and genetic algorithm. International Journal of Modeling, Simulation, and Scientific Computing2023; 14(06): 2341032. https://doi.org/10.1142/S1793962323410325
32.
ZhangDYinYBLuoR, et al.Hybrid IACO-A*-PSO optimization algorithm for solving mult objective path planning problem of mobile robot in radioactive environment. Prog Nucl Energy2023; 159: 104651. https://doi.org/10.1016/j.pnucene.2023.104651
33.
SuiFTangXDongZ, et al.ACO plus PSO plus A*: a bi-layer hybrid algorithm for multi-task path planning of an AUV. Comput Ind Eng2023; 175: 108905. https://doi.org/10.1016/j.cie.2022.108905
34.
YinSXiangZ. Adaptive collision avoidance strategy for USVs in perception-limited environments using dynamic priority guidance. Adv Eng Inform2025; 65: 103355.
35.
TangZFaFLuG, et al. Reinforcement learning for autonomous agents: scene-specific dynamic obstacle avoidance and target pursuit in unknown environments. IEEE Access. 2024; 12: 145496–145510.
36.
WuXWeiCGuanD, et al.Risk-aware deep reinforcement learning for mapless navigation of unmanned surface vehicles in uncertain and congested environments. Ocean Eng2025; 322: 120446.
37.
YinSXiangZ. A hyper-heuristic algorithm via proximal policy optimization for multi-objective truss problems. Expert Syst Appl2024; 256: 124929.
38.
DanachKHarbHJose HejaseH, et al.A hybrid metaheuristic framework with reinforcement learning–based heuristic selection for large-scale combinatorial optimization. European Journal of Pure and Applied Mathematics2025; 18(3): 6602.
39.
ZhangYChangROmranyH, et al.Policy-gradient scheduling optimisation under multi-skill constraints: a comparative study on computational algorithms. Journal of Building Design and Environment2025; 3(3): 202571.
40.
ChenXBaiRQuR, et al. Deep reinforcement learning assisted genetic programming ensemble hyper-heuristics for dynamic scheduling of container port trucks. IEEE Trans Evol Comput. 2024; 8(9).
41.
AhmedMRama Mohan BabuG. Hyper-heuristic multi-objective online optimization for cyber security in big data. Int J Syst Assur Eng Manag2024; 15(1): 314–323.
42.
NybergTPekCDal ColL, et al.Risk-aware motion planning for autonomous vehicles with safety specifications. In: 2021 IEEE intelligent vehicles symposium (iv). IEEE, 2021.
MachavaramR. Intelligent path planning for autonomous ground vehicles in dynamic environments utilizing adaptive Neuro-Fuzzy control. Eng Appl Artif Intell2025; 144: 110119.
45.
MirjaliliSMirjaliliSMLewisA. Grey wolf optimizer. Adv Eng Software2014; 69: 46–61.
46.
AlkafaweenEElmougySEssaE, et al.IAM-TSP: iterative approximate methods for solving the travelling salesman problem. Int J Adv Comput Sci Appl2023; 14: 11.