Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, arteries, bones and so on in the puncture path, it is a challenging work to design the optimal path for surgical needle. In this paper, we propose a new path planning method based on the adaptive intelligent particle swarm optimization (PSO) algorithm with parameter adjustment mechanism. First, force and motion analysis are carried out on the bevel-tip flexible needle after piercing into human tissues, the motion model of the needle and the spatial transformation model of puncture route in three-dimensional space are obtained, respectively. Then, a multi-objective function is established, which includes puncture path length function, puncture error function and collision detection function. Finally, the optimal puncture path is obtained based on the adaptive intelligent PSO algorithm. The simulation results show that the newly proposed path planning method has higher efficiency, better adaptability to complex environments and higher accuracy than other path planning methods in literature.
AbolhassaniNikiPatelRVAyaziF (2010) Minimization of needle deflection in robot-assisted percutaneous therapy. International Journal of Medical Robotics and Computer Assisted Surgery3(2): 140–148.
2.
CaiCXSunCSHanYZhangQH (2020) Clinical flexible needle puncture path planning based on particle swarm optimization. Computer Methods and Programs in Biomedicine193: 105511.
3.
DuindamVAlterovitzRSastrySGoldbergK (2008) Screw-based motion planning for bevel-tip flexible needles in 3D environments with obstacles. In: IEEE International Conference on Robotics and Automation, Pasadena, California, USA, 19–23 May 2008, pp. 2483–2488. New York: IEEE Publishing.
4.
EberhartRKennedyJ (1995) A new optimizer using particle swarm theory. In: 6th IEEE International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4–6 October 1995, pp. 39–43. New York: IEEE Publishing.
5.
GaoZKLiYLYangYX, et al. (2019) A GPSO-optimized convolutional neural networks for EEG-based emotion recognition. Neurocomputing380: 225–235.
6.
HuoBYZhaoXGHanJDXuWL (2014) A control method for puncture with flexible needle based on reachable decision. Control Theory and Applications31(10): 1423–1430.
7.
HuoBYZhaoXGHanJDXuWL (2018) Closed-loop control of bevel-tip needles based on path planning. Robotica36(12): 1857–1873.
8.
HeLQZhaoYYuJL (2020) Calculation and analysis of optimized RBF neural network line loss based on improved PSO. Northeast Electric Power Technology41(2): 55–59.
9.
JiangSLiuXYBaiSYangZY (2010) The potential field-based trajectory planning of needle invasion in soft tissue. Journal of Biomedical Engineering27(4): 790–794.
10.
LiWHuXHWangHC (2018) Two-dimensional cross entropy multi-threshold image segmentation based on BBO algorithm. Application Research of Computers35(9): 2845–2847.
11.
MinhasDSEnghJAMicheleHRiviereCN (2007) Modeling of needle steering via duty-cycled spinning. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 23–26 August 2007, pp. 2756–2759. New York: IEEE Publishing.
12.
QiCYangYQSuiXShiZC (2019) The optimal control synchronization of complex dynamical networks with time-varying delay using PSO. Neurocomputing333: 1–10.
13.
QiWSuHAlivertiA (2020) A smartphone-based adaptive recognition and real-time monitoring system for human activities. IEEE Transactions on Human-Machine Systems50(5): 414–423.
14.
StauntonRC (1989) The design of hexagonal sampling structures for image digitization and their use with local operators. Image and Vision Computing7(3): 162–166.
15.
ShiYHEberhartR (1998) A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation Conference, Alaska, USA, 4–9 May 1998, pp. 69–73. New York: IEEE Publishing.
16.
SuHHuYKarimiHR, et al. (2020) Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results. Neural Networks131: 291–299.
17.
SuHQiWHuY, et al. (2020a) An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators. IEEE Transactions on Industrial Informatics, early access, 9 November 2020. DOI: 10.1109/TII.2020.3036693.
18.
SuHQiWYangCG, et al. (2020b) Deep neural network approach in robot tool dynamics identification for bilateral teleoperation. IEEE Robotics and Automation Letters5(2): 2943–2949.
19.
TianZDLiSJWangYHWangXD (2017) SVM predictive control for calcination zone temperature in lime rotary kiln with improved PSO algorithm. Transactions of the Institute of Measurement and Control40(10): 3134–3146.
20.
WangJJLiXPZhengJJSunD (2014) Dynamic path planning for inserting a steerable needle into a soft tissue. IEEE/ASME Transactions on Mechatronics19(2): 549–558.
21.
WebsterRJCowanNJChirikjianGOkamuraAM (2006) Nonholonomic modeling of needle steering. The International Journal of Robotics Research25(5–6): 509–525.
22.
WebsterRJMemisevicJOkamuraAM (2005) Design considerations for robotic needle steering. In: IEEE International Conference on Robotics and Automation, Barcelona, ES, April 2005, pp. 3588–3594. New York: IEEE Publishing
23.
YuXLZhouYJLiuZLZhaoZM (2019a) An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO. Transactions of the Institute of Measurement and Control41(12): 3331–3339.
24.
YuXZhouXBZhangYM (2019b) Collision-free trajectory generation and tracking for UAVs using Markov decision process in a cluttered environment. Journal of Intelligent and Robotic Systems93(1): 17–32.
25.
ZhangQWWeiYC (2020) Particle swarm optimization with independent adaptive parameter adjustment. Computer Science and Technology14(4): 637–648.
26.
ZhouXBYuXZhangYM, et al. (2020) Trajectory planning and tracking strategy applied to an unmanned ground vehicle in the presence of obstacles. IEEE Transactions on Automation Science and Engineering, early access, 4 August 2020. DOI: 10.1109SE.2020.3010887.
