An Intelligent Route Planning Approach Using Modified Particle Swarm Optimization for Robot Assisted Minimally Invasive Surgery
DOI:
https://doi.org/10.62411/jcta.12473Keywords:
Intelligent Surgical Navigation, Modified Particle Swarm Optimization, Optimal Route, Particle Swarm Optimization, Robot Assisted Invasive SurgeryAbstract
Minimally invasive surgery offers several advantages, including reduced blood loss, smaller incisions, less pain, and a lower risk of complications than open surgery. This approach enhances patient comfort and supports faster recovery. When guided by optimal path planning, surgical robots can accurately navigate the body to remove malignant tumors with high precision. This study proposes a Modified Particle Swarm Optimization (MPSO) algorithm to determine the optimal path for robotic-assisted minimally invasive surgery targeting brain tumors. The algorithm improves upon standard PSO by modifying the velocity update equation and incorporating an adaptive inertia weight, enhancing convergence speed, global search ability, and solution accuracy. Experimental results show that the proposed MPSO achieves a maximum fitness value of 19.10 in a sparse obstacle environment, outperforming standard PSO and IPSO in quality and in the required number of iterations. The approach effectively balances path efficiency and obstacle avoidance, making it well-suited for complex surgical scenarios. In conclusion, the MPSO-based method provides a reliable and precise solution for robotic surgical navigation, improving outcomes and safety in minimally invasive procedures.References
D. Das, S. Naseem, M. Hoque, S. Dasgupta, and I. Bhattacharya, “Detection and Localization of Malignant Cells from Surgical Images for Robot Assisted Invasive Surgery Using Deep Learning,” in Computational Intelligence in Communications and Business Analytics, vol. 2367, J. P. Singh, M. P. Singh, A. K. Singh, S. Mukhopadhyay, J. K. Mandal, and P. Dutta, Eds. Cham: Springer Nature Switzerland, 2025, pp. 194–203. doi: 10.1007/978-3-031-81339-9_17.
R. Konietschke, H. Weiss, T. Ortmaier, and G. Hirzinger, “A Preoperative Planning Procedure for Robotically Assisted Minimally Invasive Interventions,” in CURAC 2004 3. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie e.V, Jan. 2004. [Online]. Available: https://www.robotic.dlr.de/fileadmin/robotic/konietschke/Publications/CURAC_2004_ConfPaper_Planning_Procedure_Rainer_Konietschke_CameraReady.pdf
Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 1998, pp. 69–73. doi: 10.1109/ICEC.1998.699146.
N. Khlif, N. Khraief, and S. Belghith, “Comparative Analysis of Modified Q-Learning and DQN for Autonomous Robot Navigation,” J. Futur. Artif. Intell. Technol., vol. 1, no. 3, pp. 296–308, Dec. 2024, doi: 10.62411/faith.3048-3719-49.
Z. Tan, H.-G. Liang, D. Zhang, and Q.-G. Wang, “Path planning of surgical needle: A new adaptive intelligent particle swarm optimization method,” Trans. Inst. Meas. Control, vol. 44, no. 4, pp. 766–774, Feb. 2022, doi: 10.1177/0142331221998832.
F. Zhang, Z. Yan, and Z. Du, “Preoperative planning for the multi-arm surgical robot using PSO-GP-based performance optimization,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017, pp. 4208–4214. doi: 10.1109/ICRA.2017.7989484.
M. T. Ramezanlou, V. Azimirad, and M. Zakeri, “Hybrid Path Planning of Robots Through Optimal Control and PSO Algorithm,” in 2019 7th International Conference on Robotics and Mechatronics (ICRoM), Nov. 2019, pp. 259–264. doi: 10.1109/ICRoM48714.2019.9071893.
N. Supakar and A. Senthil, “PSO obstacle avoidance algorithm for robot in unknown environment,” in 2013 International Conference on Communication and Computer Vision (ICCCV), Dec. 2013, pp. 1–7. doi: 10.1109/ICCCV.2013.6906739.
D. Baek, M. Hwang, H. Kim, and D.-S. Kwon, “Path Planning for Automation of Surgery Robot based on Probabilistic Roadmap and Reinforcement Learning,” in 2018 15th International Conference on Ubiquitous Robots (UR), Jun. 2018, pp. 342–347. doi: 10.1109/URAI.2018.8441801.
I. Bhattacharya and U. K. Roy, “Optimal Placement of Readers in an RFID Network Using Particle Swarm Optimization,” Int. J. Comput. Networks Commun., vol. 2, no. 6, pp. 225–234, Nov. 2010, doi: 10.5121/ijcnc.2010.2615.
S. Malik and D. Kim, “Optimal Travel Route Recommendation Mechanism Based on Neural Networks and Particle Swarm Optimization for Efficient Tourism Using Tourist Vehicular Data,” Sustainability, vol. 11, no. 12, p. 3357, Jun. 2019, doi: 10.3390/su11123357.
X. Li, D. Wu, J. He, M. Bashir, and M. Liping, “An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot,” J. Control Sci. Eng., vol. 2020, pp. 1–12, May 2020, doi: 10.1155/2020/3857894.
A. V. Prabu, P. Jaswanth, S. Yogendhraa, K. Sudheer, S. Rajasoundaran, and S. Velliangiri, “Performance Analysis of WSN using Efficient PSO Approach,” in 2022 2nd International Conference on Intelligent Technologies (CONIT), Jun. 2022, pp. 1–6. doi: 10.1109/CONIT55038.2022.9847742.
M. Jakubcová, P. Máca, and P. Pech, “A Comparison of Selected Modifications of the Particle Swarm Optimization Algorithm,” J. Appl. Math., vol. 2014, pp. 1–10, 2014, doi: 10.1155/2014/293087.
M. Jain, V. Saihjpal, N. Singh, and S. B. Singh, “An Overview of Variants and Advancements of PSO Algorithm,” Appl. Sci., vol. 12, no. 17, p. 8392, Aug. 2022, doi: 10.3390/app12178392.
J. Xin, J. Kim, S. Chu, and N. Li, “OkayPlan: Obstacle Kinematics Augmented Dynamic Real-time Path Planning via Particle Swarm Optimization,” ArXiv. Jan. 10, 2024. doi: 10.1016/j.oceaneng.2024.117841.
B. Mishra and H. E. Sevil, “Path planning algorithm design using particle swarms optimization and artificial potential fields,” Electron. Lett., vol. 60, no. 18, p. e70038, Sep. 2024, doi: 10.1049/ell2.70038.
J. Xin, Z. Li, Y. Zhang, and N. Li, “Efficient Real-time Path Planning with Self-evolving Particle Swarm Optimization in Dynamic Scenarios,” ArXiv. Aug. 20, 2023. [Online]. Available: http://arxiv.org/abs/2308.10169
H. Belaidi and F. Demim, “NURBs Based Multi-robots Path Planning with Obstacle Avoidance,” J. Comput. Theor. Appl., vol. 1, no. 4, pp. 478–491, May 2024, doi: 10.62411/jcta.10387.
Q. Yuan, R. Sun, and X. Du, “Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm,” Processes, vol. 11, no. 1, p. 26, Dec. 2022, doi: 10.3390/pr11010026.
A. G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Arch. Comput. Methods Eng., vol. 29, no. 5, pp. 2531–2561, Aug. 2022, doi: 10.1007/s11831-021-09694-4.
R. Poli, “Analysis of the Publications on the Applications of Particle Swarm Optimisation,” J. Artif. Evol. Appl., vol. 2008, no. 1, p. 685175, Jan. 2008, doi: 10.1155/2008/685175.
M. A. H. Akhand, S. Akter, S. Sazzadur Rahman, and M. M. Hafizur Rahman, “Particle Swarm Optimization with partial search to solve Traveling Salesman Problem,” in 2012 International Conference on Computer and Communication Engineering (ICCCE), Jul. 2012, pp. 118–121. doi: 10.1109/ICCCE.2012.6271164.
A. Khare and S. Rangnekar, “A review of particle swarm optimization and its applications in Solar Photovoltaic system,” Appl. Soft Comput., vol. 13, no. 5, pp. 2997–3006, May 2013, doi: 10.1016/j.asoc.2012.11.033.
Y. Zhao and Z. Fang, “Particle swarm optimization algorithm with weight function’s learning factor,” J. Comput. Appl., vol. 33, no. 8, pp. 2265–2268, Nov. 2013, doi: 10.3724/SP.J.1087.2013.02265.
Azmary, “Brain Tumor Detection Using YOLO,” Kaggle, 2023. https://www.kaggle.com/code/azmary/brain-tumor-detection-using-yolo/log
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Copyright (c) 2025 Sudakshina Dasgupta, Disha Das, Muktarul Hoque, Indrajit Bhattacharya
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