Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The main goal of robot path planning is to design an optimal path for a robot to navigate from its starting point to its goal while avoiding obstacles and optimizing certain criteria. A novel method using marine predator algorithm which is used in the field of robot path planning is presented. The proposed method has two steps. First step is to build a mathematical model of path planning while second step is optimization process using marine predator algorithm. Simulation results show that the proposed method works well and has good performance in different situations. Therefore, this method is an effective method for robot path planning and related applications.
Czasopismo
Rocznik
Tom
Strony
225--242
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wzory
Twórcy
autor
- College of Electronic and Electrical Engineering, Bengbu University, Bengbu 233030, China
autor
- College of Computer and Information Engineering, Bengbu University, Bengbu 233030, China
Bibliografia
- [1] S.S. Ge and Y.J. Cui: New potential functions for mobile robot path planning. IEEE Transactions on Robotics and Automation, 16(5), (2000), 615-620. DOI: 10.1109/70.880813
- [2] N. Sariff and N. Buniyamin: An overview of autonomous mobile robot path planning algorithms. IEEE 4th Student Conference on Research and Development, (2006). DOI: 10.1109/scored.2006.4339335
- [3] A. Faramarzi, M. Heidarinejad, S. Mirjalili and A.H. Gandomi: Marine predators algorithm: A nature-inspired metaheuristic. Expert Systems with Applications, 152 (2020), 113377. DOI: 10.1016/j.eswa.2020.113377
- [4] T.T. Mac, C. Copot, D.T. Tran and R. De Keyser: Heuristic approaches in robot path planning: A survey. Applied Mathematics and Computation, 222 (2013), 420-437. DOI: 10.1016/j.robot.2016.08.001
- [5] A.K. Guruji, H. Agarwal and D.K. Parsediya: Time-efficient A* algorithm for robot path planning. Procedia Technology, 23 (2016), 144-149. DOI: 10.1016/j.protcy.2016.03.010
- [6] B. Fu, L. Chen, Y. Zhou, D. Zheng, Z. Wei, J. Dai and H. Pan: An improved A* algorithm for the industrial robot path planning with high success rate and short length. Robotics and Autonomous Systems, 106 (2018), 26-37. DOI: 10.1016/j.robot.2018.04.007
- [7] L. Zuo, Q. Guo, X. Xu and H. Fu: A hierarchical path planning approach based on A* and least-squares policy iteration for mobile robots. Neurocomputing 170 (2015), 257-266. DOI: 10.1016/j.neucom.2014.09.092
- [8] H. Wang, Y. Yu and Q. Yuan: Application of Dijkstra algorithm in robot path-planning. IEEE Second International Conference on Mechanic Automation and Control Engineering, (2011). DOI: 10.1109/mace.2011.5987118
- [9] J.L. Solka, J.C. Perry, B.R. Poellinger and G.W. Rogers: Fast computation of optimal paths using a parallel Dijkstra algorithm with embedded constraints. Neurocomputing, 8(2), (1995), 195-212. DOI: 10.1016/j.compeleceng.2021.107327
- [10] H.K. Tripathy, S. Mishra, H.K. Thakkar and D. Rai: CARE: A collision-aware mobile robot navigation in grid environment using improved Breadth First Search. Computers and Electrical Engineering, 94 (2021), 107327. DOI: 10.1016/j.compeleceng.2021.107327
- [11] A. Basiri, V. Mariani, G. Silano, M. Aatif, L. Iannelli and L. Glielmo: A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture.The Journal of Navigation, 75(2), (2022), 364-383. DOI: 10.1017/s0373463321000825
- [12] S. Bortoff: Path planning for UAVs. In Proceedings of the 2000 American Control Conference, 1 (2000), 364-368. DOI: 10.1109/acc.2000.878915
- [13] G. Li, Y. Tamura, A. Yamashita and H. Asama: Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning. International Journal of Mechatronics and Automation, 3(3), (2013), 141-170. DOI: 10.1504/ijma.2013.055612
- [14] M.H. Jaryani: An effective manipulator trajectory planning with obstacles using virtual potential field method. 2007 IEEE International Conference on Systems, Man and Cybernetics, (2007). DOI: 10.1109/icsmc.2007.4413685
- [15] L. Tang, S. Dian, G. Gu, K. Zhou, S. Wang and X. Feng: A novel potential field method for obstacle avoidance and path planning of mobile robot. 3rd International Conference on Computer Science and Information Technology, (2010). DOI: 10.1109/iccsit.2010.5565069
- [16] M. Elbanhawi and M. Simic: Sampling-based robot motion planning: A review. IEEE Access, 2 (2014), 56-77. DOI: 10.1109/access.2014.2302442
- [17] L.E. Kavraki, M.N. Kolountzakis and J.-C. Latombe: Analysis of probabilistic roadmaps for path planning. IEEE Transactions on Robotics and Automation, 14(1), (1998), 166-171. DOI: 10.1109/70.660866
- [18] G. Chen, N. Luo, D. Liu, Z. Zhao and C. Liang: Path planning for manipulators based on an improved probabilistic roadmap method. Robotics and Computer-Integrated Manufacturing, 72 (2021), 102196. DOI: 10.1016/j.rcim.2021.102196
- [19] S. Rodriguez, X. Tang, J.M. Lien and N.M. Amato: An obstacle-based rapidly-exploring random tree. Proceedings 2006 IEEE International Conference on Robotics and Automation, (2006). DOI: 10.1109/robot.2006.1641823
- [20] I. Noreen, A. Khan and Z. Habib: Optimal path planning using RRT* based approaches: A survey and future directions. International Journal of Advanced Computer Science and Applications, 7(11), (2016), 97-107. DOI: 10.14569/ijacsa.2016.071114
- [21] I. Noreen, A. Khan and Z. Habib: A comparison of RRT, RRT* and RRT*-smart path planning algorithms. International Journal of Computer Science and Network Security, 16(10), (2016), 20-27.
- [22] L. Zhang, Y.J. Kim and D. Manocha: A hybrid approach for complete motion planning. 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, (2007). DOI: 10.1109/IROS.2007.4399064
- [23] J. van den Berg and M. Overmars: Path planning in repetitive environments. 12th IEEE International Conference on Methods and Models in Automation and Robotics, (2006) 657-662.
- [24] K. Belghith, F. Kabanza, L. Hartman and R. Nkambou: Anytime dynamic path-planning with flexible probabilistic roadmaps. Proceedings 2006 IEEE International Conference on Robotics and Automation, (2006). DOI: 10.1109/ROBOT.2006.1642057
- [25] B. Burns and O. Brock: Sampling-based motion planning using predictive models. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, (2005). DOI: 10.1109/ROBOT.2005.1570590
- [26] M.N. Ab Wahab, S. Nefti-Meziani and A. Atyabi: A comparative review on mobile robot path planning: Classical or meta-heuristic methods? Annual Reviews in Control, 50 (2020), 233-252. DOI: 10.1016/j.arcontrol.2020.10.001
- [27] Y. Hu and S.X. Yang: A knowledge based genetic algorithm for path planning of a mobile robot. IEEE International Conference on Robotics and Automation, (2004). DOI: 10.1109/ROBOT.2004.1302402
- [28] G. Li and H. Shi: Path planning for mobile robot based on particle swarm optimization. 2008 Chinese Control and Decision Conference, (2008). DOI: 10.1109/CCDC.2008.4597938
- [29] B. Song, Z. Wang and L. Zou: An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing, 100 (2021), 106960. DOI: 10.1016/j.asoc.2020.106960
- [30] H.S. Dewang, P.K. Mohanty and S. Kundu: A robust path planning for mobile robot using smart particle swarm optimization. Procedia Computer Science, 133 (2018), 290-297. DOI: 10.1016/j.procs.2018.07.036
- [31] M. Panda, B. Das and B. Bhusan Pati: Global path planning for multiple AUVs using GWO. Archives of Control Sciences, 30(1), (2020), 77-100. DOI: 10.24425/acs.2020.132586
- [32] S. Vaidyanathan, K. Benkouider, A. Sambas and P. Darwin: Bifurcation analysis, circuit design and sliding mode control of a new multistable chaotic population model with one prey and two predators. Archives of Control Sciences, 33(1), (2023), 127-153. DOI: 10.24425/acs.2023.145117
- [33] N.E. Humphries, N. Queiroz, J.R.M. Dyer, N.G. Pade, M.K. Musyl and K.M. Schaefer: Environmental context explains Lévy and Brownian movement patterns of marine predators. Nature, 465(7301), (2010), 1066-1069. DOI: 10.1038/nature09116
Uwagi
The generous support of Anhui Science and Technology Research Project (2022AH040255), Bengbu Science and Technology Research Plan Project (2022gx23), and scientific research project of Bengbu University (2021ZR04zd) are gratefully acknowledged.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-846fa2a0-0264-4e8d-b5e5-6eb3cd755205