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An omnidirectional mecanum wheel automated guided vehicle control using hybrid modified A* algorithm

Published online by Cambridge University Press:  06 December 2024

Ankur Bhargava Open the ORCID record for Ankur Bhargava [Opens in a new window] ,
Mohammad Suhaib Open the ORCID record for Mohammad Suhaib [Opens in a new window]  and
Ajay K. S. Singholi Open the ORCID record for Ajay K. S. Singholi [Opens in a new window]
Ankur Bhargava*
Affiliation:
Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, India
Mohammad Suhaib
Affiliation:
Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, India
Ajay K. S. Singholi
Affiliation:
University School of Automation & Robotics, Guru Gobind Singh Indraprastha University, New Delhi, India
*
Corresponding author: Ankur Bhargava; Email: ankurgsb21@gmail.com
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Abstract

This paper presents Hybrid Modified A* (HMA*) algorithm which is used to control an omnidirectional mecanum wheel automated guided vehicle (AGV). HMA* employs Modified A* and PSO to determine the best AGV path. The HMA* overcomes the A* technique’s drawbacks, including a large number of nodes, imprecise trajectories, long calculation times, and expensive path initialization. Repetitive point removal refines Modified A*’s path to locate more important nodes. Real-time hardware control experiments and extensive simulations using Matlab software prove the HMA* technique works well. To evaluate the practicability and efficiency of HMA* in route planning and control for AGVs, various algorithms are introduced like A*, Probabilistic Roadmap (PRM), Rapidly-exploring Random Tree (RRT), and bidirectional RRT (Bi-RRT). Simulations and real-time testing show that HMA* path planning algorithm reduces AGV running time and path length compared to the other algorithms. The HMA* algorithm shows promising results, providing an enhancement and outperforming A*, PRM, RRT, and Bi-RRT in the average length of the path by 12.08%, 10.26%, 7.82%, and 4.69%, and in average motion time by 21.88%, 14.84%, 12.62%, and 8.23%, respectively. With an average deviation of 4.34% in path length and 3% in motion time between simulation and experiments, HMA* closely approximates real-world conditions. Thus, the proposed HMA* algorithm is ideal for omnidirectional mecanum wheel AGV’s static as well as dynamic movements, making it a reliable and efficient alternative for sophisticated AGV control systems.

Keywords

automated guided vehiclehybrid modified A* (HMA*) algorithmparticle swarm optimization (PSO)omnidirectional mecanum wheelpath planning
Type
Research Article
Information
Robotica , Volume 43 , Issue 2 , February 2025 , pp. 449 - 498
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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References

Yao, Z., Zhang, W., Shi, Y., Li, M., Liang, Z., Li, F. and Huang, Q., “RimJump: Edge-based shortest path planning for a 2D map,” Robotica 37(4), 641–655 (2019). doi: 10.1017/S0263574718001236.CrossRefGoogle Scholar
Tanveer, M. H., Recchiuto, C. T. and Sgorbissa, A., “Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace,” Robotica 37(1), 80–108 (2019). doi: 10.1017/S0263574718000875.CrossRefGoogle Scholar
Sharma, M. and Voruganti, H. K., “Multi-objective optimization approach for coverage path planning of mobile robot,” Robotica 42(7), 1–25 (2024). doi: 10.1017/S0263574724000377.CrossRefGoogle Scholar
Khan, H., Khatoon, S., Gaur, P., Abbas, M., Saleel, C. A. and Khan, S. A., “Speed control of wheeled mobile robot by nature-inspired social spider algorithm-based PID controller,” Processes 11(4), 1202 (2023). doi: 10.3390/pr11041202.CrossRefGoogle Scholar
Wahhab, O. A. R. A. and A., S., “Al-Araji “Path planning and control strategy design for mobile robot based on hybrid swarm optimization algorithm,” Int J Intell Eng Syst 14(3), 565–579 (2021). doi: 10.22266/ijies2021.0630.48.Google Scholar
Liu, S., Liu, S. and Xiao, H., “Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots,” Robotica 42(2), 536–559 (2024). doi: 10.1017/S0263574723001625.CrossRefGoogle Scholar
Kanoon, Z. E., Al-Araji, A. S. and Abdullah, M. N., “An intelligent path planning algorithm and control strategy design for multi-mobile robots based on a modified elman recurrent neural network,” Int J Intell Eng Syst 15(5), 400–415 (2022). doi: 10.2266/ijies2022.1031.35.Google Scholar
El Aziz, M. A., Ewees, A. A. and Hassanien, A. E., “Hybrid Swarms Optimization Based Image Segmentation,” In: Hybrid Soft Computing for Image Segmentation, (Bhattacharyya, S., Dutta, P., De, S. and Klepac, G.eds.) (Springer, Cham, 2016). doi: 10.1007/978-3-319-47223-2_1.Google Scholar
Al-Araji, A. S., “Development of kinematic path-tracking controller design for real mobile robot via back-stepping slice genetic robust algorithm technique,” Arab J Sci Eng 39(12), 8825–8835 (2014). doi: 10.1007/s13369-014-1461-4.CrossRefGoogle Scholar
Rasheed, A. A. A., Al-Araji, A. S. and Abdullah, M. N., “Static and dynamic path planning algorithms design for a wheeled mobile robot based on a hybrid technique,” Int J Intell Eng Syst 15(4), 167–181 (2022). doi: 10.22266/ijies2022.0831.16.Google Scholar
Kim, C., Suh, J. and Han, J.-H., “Development of a hybrid path planning algorithm and a bio-inspired control for an omni-wheel mobile robot,” Sensors 20(15), 4258 (2020). doi: 10.3390/s20154258.CrossRefGoogle Scholar
S., J. F. and R., S., “Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment,” Robotica 42(4), 977–1000 (2024). doi: 10.1017/S0263574723001819.CrossRefGoogle Scholar
Tolossa, T. D., Gunasekaran, M., Halder, K., Verma, H. K., Parswal, S. S., Jorwal, N., Maria Joseph, F. O. and Hote, Y. V., “Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters,” Robotica 1–24 (2024). doi: 10.1017/S0263574724001140.Google Scholar
Yuan, C., Chang, Y., Song, Y., Lin, S. and Jing, F., “Design and analysis of a negative pressure wall-climbing robot with an omnidirectional characteristic for cylindrical wall,” Robotica 42(7), 2226–2242 (2024). doi: 10.1017/S0263574724000493.CrossRefGoogle Scholar
Nfaileh, N., Alipour, K., Tarvirdizadeh, B. and Hadi, A., “Formation control of multiple wheeled mobile robots based on model predictive control,” Robotica 40(9), 3178–3213 (2022). doi: 10.1017/S0263574722000121.CrossRefGoogle Scholar
Mao, N., Chen, J., Spyrakos-Papastavridis, E. and Dai, J. S., “Dynamic modeling of wheeled biped robot and controller design for reducing chassis tilt angle,” Robotica, 1–29 (2024). doi: 10.1017/S0263574724001061.Google Scholar
Guo, J., Huo, X., Guo, S. and Xu, J., “A Path Planning Method for the Spherical Amphibious Robot Based on Improved A-star Algorithm,” In: 2021 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan (2021) pp. 1274–1279. doi: 10.1109/ICMA52036.2021.9512805.CrossRefGoogle Scholar
Wei, K., Gao, Y., Zhang, W. and Lin, S., “A Modified Dijkstra’s Algorithm for Solving the Problem of Finding the Maximum Load Path,” In: 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT), Kahului, HI, USA (2019) pp. 10–13. doi: 10.1109/INFOCT.2019.8711024.CrossRefGoogle Scholar
Guo, J., Liu, L., Liu, Q. and Qu, Y., “An Improvement of D* Algorithm for Mobile Robot Path Planning in Partial Unknown Environment,” In: 2009 Second International Conference on Intelligent Computation Technology and Automation, Changsha, China (2009) pp. 394–397. doi: 10.1109/ICICTA.2009.561.CrossRefGoogle Scholar
Karur, K., Sharma, N., Dharmatti, C. and Siegel, J. E., “A survey of path planning algorithms for mobile robots,” Vehicles 3(3), 448–468 (2021). doi: 10.3390/vehicles3030027.CrossRefGoogle Scholar
Wang, X., Wei, J., Zhou, X., Xia, Z. and Gu, X., “AEB-RRT*: An adaptive extension bidirectional RRT* algorithm,” Auton Robot 46(6), 685–704 (2022). doi: 10.1007/s10514-022-10044-x.CrossRefGoogle Scholar
Li, Q., Xu, Y., Bu, S. and Yang, J., “Smart vehicle path planning based on modified PRM algorithm,” Sensors 22(17), 6581 (2022). doi: 10.3390/s22176581.CrossRefGoogle ScholarPubMed
He, S., Xing, T. and Ma, J., “Research on Solid Rate Filtering Technique based on Inverse Distance Weighted Interpolation of Navigation Radar,” In: 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China (2022) pp. 838–841. doi: 10.1109/ITAIC54216.2022.9836465.CrossRefGoogle Scholar
Gopika, M. P., Bindu, G. R., Ponmalar, M., Usha, K. and Haridas, T. R., “Smooth PRM Implementation for Autonomous Ground Vehicle,” In: 2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS), Bangalore, India (2022) pp. 1–5. doi: 10.1109/ICDDS56399.2022.10037275.CrossRefGoogle Scholar
Yang, N., Han, L., Xiang, C., Liu, H., Ma, T. and Ruan, S., “Real-time energy management for a hybrid electric vehicle based on heuristic search,” IEEE Trans Veh Technol 71(12), 12635–12647 (2022). doi: 10.1109/TVT.2022.3195769.CrossRefGoogle Scholar
Eshtehardian, S. A. and Khodaygan, S., “A continuous RRT*-based path planning method for non-holonomic mobile robots using B-spline curves,” J Ambient Intell Human Comput 14(7), 8693–8702 (2023). doi: 10.1007/s12652-021-03625-8.CrossRefGoogle Scholar
Li, C., Huang, X., Ding, J., Song, K. and Lu, S., “Global path planning based on a bidirectional alternating search A* algorithm for mobile robots,” Comput Ind Eng 168, 108123 (2022). doi: 10.1016/j.cie.2022.108123.CrossRefGoogle Scholar
Katiyar, S. and Dutta, A., “Comparative analysis on path planning of ATR using RRT*, PSO, and modified APF in CG-space,” Proc Inst Mech Eng Pt C: J Mech Eng Sci 236(10), 5663–5677 (2022). doi: 10.1177/09544062211062435.CrossRefGoogle Scholar
Zhang, L., Shi, X., Yi, Y., Tang, L., Peng, J. and Zou, J., “Mobile robot path planning algorithm based on RRT_Connect,” Electronics 12(11), 2456 (2023). doi: 10.3390/electronics12112456.CrossRefGoogle Scholar
Wang, J., Chi, W., Shao, M. and Meng, M. Q.-H., “Finding a high-quality initial solution for the RRTs algorithms in 2D environments,” Robotica 37(10), 1677–1694 (2019). doi: 10.1017/S0263574719000195.CrossRefGoogle Scholar
Umar, U. A., Ariffin, M. K. A., Ismail, N. and Tang, S. H., “Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment,” Int J Adv Manuf Technol 81(9-12), 2123–2141 (2015). doi: 10.1007/s00170-015-7329-2.CrossRefGoogle Scholar
Wang, T., Dong, R., Zhang, R. and Qin, D., “Research on stability design of differential drive fork-type AGV based on PID control,” Electronics 9(7), 1072 (2020). doi: 10.3390/electronics9071072.CrossRefGoogle Scholar
Xiao, J., Yu, X., Sun, K., Zhou, Z. and Zhou, G., “Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA[J],” Math Biosci Eng 19(12), 12532–12557 (2022). doi: 10.3934/mbe.2022585.CrossRefGoogle Scholar
Cao, X. and Zhu, M., “Research on global optimization method for multiple AGV collision avoidance in hybrid path,” Opti Control Appl Meth 42(4), 1064–1080 (2021). doi: 10.1002/oca.2716.CrossRefGoogle Scholar
Ruiz Molledo, V. and Sierra Garcia, J. E., “Simulation tool for hybrid AGVs based on IEC-61131,” IEEE Lat Am Trans 20(2), 317–325 (2022). doi: 10.1109/TLA.2022.9661472.CrossRefGoogle Scholar
Lin, R., “Research into the automatic guidance system for AGVs used for logistics based on millimeter wave radar imaging,” Wire Commun Mobile Comput 2022, 8 (2022). doi: 10.1155/2022/3104017.Google Scholar
Gad, A. G., “Particle swarm optimization algorithm and its applications: A systematic review,” Arch Computat Methods Eng 29(5), 2531–2561 (2022). doi: 10.1007/s11831-021-09694-4.CrossRefGoogle Scholar
Qiao, Y., Fu, Y. and Yuan, M., “Communication-control co-design in wireless networks: A cloud control AGV example,” IEEE Internet Things J 10(3), 2346–2359 (2023). doi: 10.1109/JIOT.2022.3211766.CrossRefGoogle Scholar
Durst, P., Jia, X. and Li, L., “Multi-Objective Optimization of AGV Real-Time Scheduling Based on Deep Reinforcement Learning,” In: 42nd Chinese Control Conference (CCC), Tianjin, China (2023) pp. 5535–5540. doi: 10.23919/CCC58697.2023.10240797.CrossRefGoogle Scholar
Yuan, X., Yuan, X. and Wang, X., “Path planning for mobile robot based on improved bat algorithm,” Sensors 21(13), 4389 (2021). doi: 10.3390/s21134389.CrossRefGoogle ScholarPubMed
Si, Q. and Li, C., “Indoor robot path planning using an improved whale optimization algorithm,” Sensors 23(8), 3988 (2023). doi: 10.3390/s23083988.CrossRefGoogle ScholarPubMed
Jiang, M., Yuan, D. and Cheng, Y., “Improved Artificial Fish Swarm Algorithm,” In: 2009 Fifth International Conference on Natural Computation, Tianjian, China (2009) pp. 281–285. doi: 10.1109/ICNC.2009.343.CrossRefGoogle Scholar
Dai, W.-M. and Kuh, E. S., “Simultaneous floor planning and global routing for hierarchical building-block layout,” IEEE Tran Comput-Aided Des Integr Circuits and Syst 6(5), 828–837 (1987). doi: 10.1109/TCAD.1987.1270326.Google Scholar
Korayem, M. H., Nosoudi, S., Khazaei Far, S. and Hoshiar, A. K., “Hybrid IPSO-automata algorithm for path planning of micro-nanoparticles through random environmental obstacles, based on AFM,” J Mech Sci Technol 32(2), 805–810 (2018). doi: 10.1007/s12206-018-0129-x.CrossRefGoogle Scholar
Bhargava, A., Suhaib, M. and Singholi, A. S., “A review of recent advances, techniques, and control algorithms for automated guided vehicle systems,” J Braz Soc Mech Sci Eng 46(7), 419 (2024). doi: 10.1007/s40430-024-04896-w.CrossRefGoogle Scholar
Abi, S., Benhala, B. and Bouyghf, H., “A Hybrid DE-ACO Algorithm for the Global Optimization,” In: 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), Kenitra, Morocco (2020) pp. 1–6. doi: 10.1109/ICECOCS50124.2020.9314533.CrossRefGoogle Scholar
Nie, Z. and Zhao, H., “Research on Robot Path Planning Based on Dijkstra and Ant Colony Optimization,” In: 2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Shanghai, China (2019) pp. 222–226. doi: 10.1109/ICIIBMS46890.2019.8991502.CrossRefGoogle Scholar
Shial, G., Sahoo, S. and Panigrahi, S., “An enhanced GWO algorithm with improved explorative search capability for global optimization and data clustering,” Appl Artif Intell 37(1), 1 (2023). doi: 10.1080/08839514.2023.2166232.CrossRefGoogle Scholar
Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A. and Mirjalili, S., “Particle swarm optimization: A comprehensive survey,” IEEE Access 10, 10031–10061 (2022). doi: 10.1109/ACCESS.2022.3142859.CrossRefGoogle Scholar
Shami, T. M., El-Saleh, A. A. and Kareem, A. M., “On the Detection Performance of Cooperative Spectrum Sensing using Particle Swarm Optimization Algorithms,” In: 2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT), Langkawi, Malaysia (2014) pp. 110–114. doi: 10.1109/ISTT.2014.7238187.CrossRefGoogle Scholar
Chen, X., Zhao, Y., Fan, J. and Liu, H., “Three-Dimensional UAV Track Planning based on the GB-PQ-RRT* Algorithm,” In: 42nd Chinese Control Conference (CCC), Tianjin, China (2023) pp. 4639–4644. doi: 10.23919/CCC58697.2023.10240961.CrossRefGoogle Scholar
Guan, W. and Wang, K., “Autonomous collision avoidance of unmanned surface vehicles based on improved A-star and dynamic window approach algorithms,” IEEE Intel Transp Syst Magaz 15(3), 36–50 (2023). doi: 10.1109/MITS.2022.3229109.CrossRefGoogle Scholar
Cao, B., Yang, Z., Yu, L. and Zhang, Y., “Research on the Star Algorithm for Safe Path Planning,” In: 2023 IEEE International Conference on Control, Jilin, China (2023) pp. 105–109. doi: 10.1109/ICCECT57938.2023.10141167.CrossRefGoogle Scholar
Bhargava, A., Singholi, A. S. and Suhaib, M., “A Study on Design and Control of Omni-Directional Mecanum Wheels based AGV System,” In: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India (2023) pp. 1–6. doi: 10.1109/ICCCNT56998.2023.10306665.CrossRefGoogle Scholar
Li, S., Gu, J., Li, Z., Li, S., Guo, B., Gao, S., Zhao, F., Yang, Y., Li, G. and Dong, L., “A visual SLAM-based lightweight multi-modal semantic framework for an intelligent substation robot,” Robotica 42(7), 1–15 (2024). doi: 10.1017/S0263574724000511.CrossRefGoogle Scholar
Chen, Z., Zhang, X., Wang, L. and Xia, Y., “A Fast Path Planning Method Based on RRT Star Algorithm,” In: 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China (2023) pp. 258–262. doi: 10.1109/ICCECE58074.2023.10135365.CrossRefGoogle Scholar
Zhang, S., Li, A., Ren, J. and Ren, R., “Kinematics inverse solution of assembly robot based on improved particle swarm optimization,” Robotica 42(3), 833–845 (2024). doi: 10.1017/S0263574723001789.CrossRefGoogle Scholar
Pak, Y.-J., Kong, Y.-S. and Ri, J.-S., “Robust PID optimal tuning of a delta parallel robot based on a hybrid optimization algorithm of particle swarm optimization and differential evolution,” Robotica 41(4), 1159–1178 (2023). doi: 10.1017/S0263574722001606.CrossRefGoogle Scholar
Liu, Y., Wang, X., Zhang, Y. and Liu, L., “An integrated flow shop scheduling problem of preventive maintenance and degradation with an improved NSGA-II Algorithm,” IEEE Access 11, 3525–3544 (2023). doi: 10.1109/ACCESS.2023.3234428.CrossRefGoogle Scholar
Liu, X., Feng, R., Zhou, S. and Yang, Y., “A Novel PSO-SGD with Momentum Algorithm for Medical Image Classification,” In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, TX, USA (2021) pp. 3408–3413. doi: 10.1109/BIBM52615.2021.9669876.CrossRefGoogle Scholar
Jingjing, H., Xun, L., Wenzhe, M., Xin, Y. and Dong, Y.. Path Planning Method for Mobile Robot Based on Multiple Improved PSO. In: 2021 40th Chinese Control Conference (CCC), Shanghai, China (2021) pp. 1485–1489, 10.23919/CCC52363.2021.9550590 Google Scholar
Demir, M. H. and Demirok, M., “Designs of particle-swarm-optimization-based intelligent PID controllers and DC/DC buck converters for PEM fuel-cell-powered four-wheeled automated guided vehicle,” Appl Sci 13(5), 2919 (2023). doi: 10.3390/app13052919.CrossRefGoogle Scholar
Ding, M., Zheng, X., Liu, L., Guo, J. and Guo, Y., “Collision-free path planning for cable-driven continuum robot based on improved artificial potential field,” Robotica 42(5), 1350–1367 (2024). doi: 10.1017/S026357472400016X.CrossRefGoogle Scholar
Khan, H., Khatoon, S. and Gaur, P., “Stabilization of wheeled mobile robot by social spider algorithm based PID controller,” Int J Inf Tecnol 16(3), 1437–1447 (2023). doi: 10.1007/s41870-023-01438-w.CrossRefGoogle Scholar
Bhargava, A., Singholi, A. S. and Suhaib, M., “Design and Development of a Visual - SLAM based Automated Guided Vehicle,” In: 2023 IEEE Fifth International Conference on Advances in Electronics, Computers and Communications (ICAECC), Bengaluru, India (2023) pp. 1–7. doi: 10.1109/ICAECC59324.2023.10560331.CrossRefGoogle Scholar
Sandoval-Castro, X. Y., Muñoz-Gonzalez, S., Garcia-Murillo, M. A., Ferrusca-Monroy, P. D. and Ruiz-Torres, M. F., “Four-bar linkage reconfigurable robotic wheel: Design, kinematic analysis, and experimental validation for adaptive size modification,” Robotica 42(6), 1–15 (2024). doi: 10.1017/S026357472400078X.CrossRefGoogle Scholar
Chen, W., Cheng, H., Zhang, W., Wu, H., Liu, X. and Men, Y., “Modeling and invariably horizontal control for the parallel mobile rescue robot based on PSO-CPG algorithm,” Robotica 41(11), 3501–3523 (2023). doi: 10.1017/S0263574723001133.CrossRefGoogle Scholar
Wang, B., Li, P., Yang, C., Hu, X. and Zhao, Y., “Robotica: Decoupled elastostatic stiffness modeling of hybrid robots,” Robotica 42(7), 1–19 (2024). doi: 10.1017/S0263574724000675.CrossRefGoogle Scholar
Weidong, Z., Xianlin, H., Xiao-Zhi, G. and Hongjun, P., “Multi-Objective Longitudinal Trajectory Optimization for Hypersonic Reentry Glide Vehicle based on PSO Algorithm,” In: 34th Chinese Control Conference (CCC), Hangzhou, China (2015) pp. 2350–2356. doi: 10.1109/ChiCC.2015.7260001.CrossRefGoogle Scholar
Raptis, I. A., Hansen, C. and Sinclair, M. A., “Design, modeling, and constraint-compliant control of an autonomous morphing surface for omnidirectional object conveyance,” Robotica 40(2), 213–233 (2022). doi: 10.1017/S0263574721000473.CrossRefGoogle Scholar
Liu, X., Wang, L. and Yang, Y., “Model-free adaptive robust control based on TDE for robot with disturbance and input saturation,” Robotica 41(11), 3426–3445 (2023). doi: 10.1017/S0263574723001078.CrossRefGoogle Scholar
Gao, G., Li, D., Liu, K., Ge, Y. and Song, C., “A study on path-planning algorithm for a multi-section continuum robot in confined multi-obstacle environments,” Robotica 1–24 (2024). doi: 10.1017/S0263574724001383.Google Scholar
Liu, B., Jiang, G., Zhao, F. and Mei, X., “Collision-free motion generation based on stochastic optimization and composite signed distance field networks of articulated robot,” IEEE Robot Autom Lett 8(11), 7082–7089 (2023). doi: 10.1109/LRA.2023.3311357.CrossRefGoogle Scholar
Nguyen, T. D., “Kinematic Model and Stable Control Law Proposed for Four Mecanum Wheeled Mobile Robot Platform Based on Lyapunov Stability Criterion,” In: 2023 International Symposium on Electrical and Electronics Engineering (ISEE), Ho Chi Minh, Vietnam (2023) pp. 144–149. doi: 10.1109/ISEE59483.2023.10299844.CrossRefGoogle Scholar