This research focuses on the routing algorithm for mobile robots, considering factors such as time, path length, and energy consumption. Various algorithms have been used to plan trajectory curves, including classical methods, probabilistic algorithms, and meta-heuristic algorithms. The artificial bee colony algorithm was used to generate practical path curves without collisions, which were then used to create the optimal path length. The basic model was a projection of the path curve using random initial paths. Spline interpolation was used to obtain the middle points of the path, making the path smooth. The proposed model has less time and length in all environments and better satisfies linear and rotational speed limitations, indicating potential for improvement.
Fatemi AghdaSAMirfakhraeiM (2020) Improved routing in dynamic environments with moving obstacles using a hybrid Fuzzy-Genetic algorithm. Future Generation Computer Systems112: 250–257.
2.
AjeilFHIbraheemIKAzarAT, et al. (2020) Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment. International Journal of Advanced Robotic Systems17(3): 172988142092949.
3.
AkkaKKhaberF (2018) Mobile robot path planning using an improved ant colony optimization. International Journal of Advanced Robotic Systems15(3): 172988141877467.
4.
BabaTKameyamaYKamegawaT, et al. (2010) A snake robot propelling inside of a pipe with helical rolling motion. In: Proceedings of SICE annual conference 2010, Taipei, 18–21 August 2010, (2319–2325). IEEE.
5.
BaoQZhengPDaiS (2023) A digital twin-driven dynamic path planning approach for multiple automatic guided vehicles based on deep reinforcement learning. Proceedings of the Institution of Mechanical Engineers - Part B: Journal of Engineering Manufacture238: 09544054231180513.
6.
BaziyadMSaadMFarehR, et al. (2021) Addressing real-time demands for robotic path planning systems: a routing protocol approach. IEEE Access9: 38132–38143.
7.
BrandãoASSarcinelli-FilhoMCarelliR (2013) An analytical approach to avoid obstacles in mobile robot navigation. International Journal of Advanced Robotic Systems, 10(6), 278.
8.
ChaariIKoubâaATriguiS, et al. (2014) SmartPATH: an efficient hybrid ACO-GA algorithm for solving the global path planning problem of mobile robots. International Journal of Advanced Robotic Systems, 11(7), 94.
9.
ChenXYuLWangT, et al. (2020) Artificial intelligence-empowered path selection: a survey of ant colony optimization for static and mobile sensor networks. IEEE Access8: 71497–71511.
10.
ChenTChenSZhangK, et al. (2022) A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot. International Journal of Advanced Robotic Systems, 19(5), 172988062211279.
11.
CherubiniAChaumetteF (2013) Visual navigation of a mobile robot with laser-based collision avoidance. The International Journal of Robotics Research32(2): 189–205.
12.
ChoiCParkBJungS (2009) The design and analysis of a feeder pipe inspection robot with an automatic pipe tracking system. IEEE15(5): 736–745.
13.
CostanziRFanelliFMeliE, et al. (2016) Generic path planning algorithm for mobile robots based on Bézier curves. IFAC-PapersOnLine49(15): 145–150.
14.
ElhosenyMShehabAYuanX (2017) Optimizing robot path in dynamic environments using genetic algorithm and Bezier curve. Journal of Intelligent and Fuzzy Systems33(4): 2305–2316.
15.
FengXWangC (2023) Adaptive neural network tracking control of an omnidirectional mobile robot. Proceedings of the Institution of Mechanical Engineers - Part I: Journal of Systems & Control Engineering237(3): 375–387.
16.
GarciaMPMontielOCastilloO, et al. (2009) Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Applied Soft Computing9(3): 1102–1110.
17.
GhanbariANooraniSMRS (2011) Optimal trajectory planning for design of a crawling gait in a robot using genetic algorithm. International Journal of Advanced Robotic Systems, 8(1), 6.
18.
GuoJLiCGuoS (2019) A novel step optimal path planning algorithm for the spherical mobile robot based on fuzzy control. IEEE Access8: 1394–1405.
19.
HichriBAdouaneLFaurouxJC, et al. (2014) Cooperative lifting and transport by a group of mobile robots. In: International symposium on distributed autonomous robotic systems, DARS 2014, Daejeon, November 2014.
20.
HosseininejadSDadkhahC (2019) Mobile robot path planning in dynamic environment based on cuckoo optimization algorithm. International Journal of Advanced Robotic Systems, 16(2), 172988141983957.
21.
HoyMMatveevASSavkinAV (2015) Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey. Robotica33(3): 463–497.
22.
JalalnezhadMFazeliSBozorgomidS, et al. (2021) Stability and control of the nonlinear system for tractor with N trailer in the presence of slip. Advances in Mechanical Engineering, 13(12), 168781402110477.
23.
KebbatiYAit‐OufroukhNIchalalD, et al. (2023) Lateral control for autonomous wheeled vehicles: a technical review. Asian Journal of Control25(4): 2539–2563.
24.
Keymasi KhalajiAJalalnezhadM (2019) Control of a tractor-trailer robot subjected to wheel slip. Proceedings of the Institution of Mechanical Engineers - Part K: Journal of Multi-Body Dynamics233(4): 956–967.
25.
KhalajiAKJalalnezhadM (2017) Dynamic feedback linearizing controller for a wheeled vehicle. In 2017 IEEE 4th international conference on knowledge-based engineering and innovation (KBEI), Tehran, 22–22 December 2017, 88–94. IEEE.
26.
Keymasi KhalajiAJalalnezhadM (2021) Robust forward\backward control of wheeled mobile robots. ISA Transactions115: 32–45.
27.
Keymasi KhalajiAJalalnezhadM (2021) Stabilization of a tractor with n trailers in the presence of wheel slip effects. Robotica39(5): 787–797.
28.
KlamtTSchwarzMLenzC, et al. (2020) Remote mobile manipulation with the centauro robot: full‐body telepresence and autonomous operator assistance. Journal of Field Robotics37(5): 889–919.
29.
KomoriyaKTanieK (1990) Trajectory control of a wheel-type mobile robot using B-spline curve. Journal of the Robotics Society of Japan8(2): 133–143.
30.
KumarSParhiDRMuniMK (2023) Path planning and obstacle avoidance of multi-robotic system in static and dynamic environments. Proceedings of the Institution of Mechanical Engineers - Part B: Journal of Engineering Manufacture237(9): 1376–1390.
31.
LingYWuJLyuZ, et al. (2020) Backstepping controller for laser ray tracking of a target mobile robot. Measurement and Control53(7-8): 1540–1547.
32.
MasehianEAmin‐NaseriMR (2004) A voronoi diagram‐visibility graph‐potential field compound algorithm for robot path planning. Journal of Robotic Systems21(6): 275–300.
33.
MiaoHTianYC (2013) Dynamic robot path planning using an enhanced simulated annealing approach. Applied Mathematics and Computation222: 420–437.
34.
MihalevGYordanovS (2021) Kinematic model and analysis of an anthropomorphic robotic finger. In 2021 international conference automatics and informatics (ICAI), Varna, 30 September 2021–02 October 2021, 322–327. IEEE.
35.
MorenoJSlawiñskiEChicaizaFA, et al. (2023) Design and analysis of an input–output linearization-based trajectory tracking controller for skid-steering mobile robots. Machines11(11): 988.
36.
PopovVLAhmedSATopalovAV, et al. (2018) Development of mobile robot target recognition and following behaviour using deep convolutional neural network and 2D range data. IFAC-PapersOnLine51(30): 210–215. https://imr.ciirc.cvut.cz/planning/maps.xml
37.
RollinsonD (2014) Control and Design of Snake Robots. Pittsburgh, PA: School of Computer Science Carnegie Mellon University.
38.
RubioFValeroFLlopis-AlbertC (2019) A review of mobile robots: concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems16(2): 172988141983959.
39.
SchonebaumJKAlijaniFRadaelliG (2021) Review on mobile robots that exploit resonance. Proceedings of the Institution of Mechanical Engineers - Part C: Journal of Mechanical Engineering Science235(24): 7907–7924.
40.
SunYZhangCLiuC (2021) Collision-free and dynamically feasible trajectory planning for omnidirectional mobile robots using a novel B-spline based rapidly exploring random tree. International Journal of Advanced Robotic Systems18(3): 172988142110166.
41.
TangJSunJLuC, et al. (2019) Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering233(16): 6032–6043.
42.
VirgalaIKelemenMPradaE, et al. (2021) A snake robot for locomotion in a pipe using trapezium-like travelling wave. Mechanism and Machine Theory158: 104221.
43.
XuYLiQXuX, et al. (2023) Research progress of nature-inspired metaheuristic algorithms in mobile robot path planning. Electronics12(15): 3263.
44.
YaoFSongYJZhangZS, et al. (2019) Multi-mobile robots and multi-trips feeding scheduling problem in smart manufacturing system: an improved hybrid genetic algorithm. International Journal of Advanced Robotic Systems16(4): 172988141986812.
45.
ZhangTMoH (2021) Reinforcement learning for robot research: a comprehensive review and open issues. International Journal of Advanced Robotic Systems18(3): 172988142110073.
46.
ZhangBZhuD (2021) A new method on motion planning for mobile robots using jump point search and Bezier curves. International Journal of Advanced Robotic Systems18(4): 172988142110192.
47.
ZhangYHuHFuW, et al. (2017) Particle swarm optimization–based minimum residual algorithm for mobile robot localization in indoor environment. International Journal of Advanced Robotic Systems14(5): 172988141772927.
48.
ZhangPXiongCLiW, et al. (2018) Path planning for mobile robot based on modified rapidly exploring random tree method and neural network. International Journal of Advanced Robotic Systems15(3): 172988141878422.
49.
ZhangSPuJSiY, et al. (2021) Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization. International Journal of Advanced Robotic Systems18(3): 172988142110192.
50.
ZhouWXingZWenbinB, et al. (2019) Route planning algorithm for autonomous underwater vehicles based on the hybrid of particle swarm optimization algorithm and radial basis function. Transactions of the Institute of Measurement and Control41(4): 942–953.
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