Review of research on path planning and control methods of flexible steerable needle puncture robot

Figures & data

Figure 1. Unicycle Kinematics Model. axyz is the inertial coordinate system; u₁ represents the feed movement of the flexible steerable needle; u₂ represents the rotation movement of the flexible steerable needle; a₁y₁z₁ is the coordinate system of the needle tip at t₁, a₂y₂z₂ is the coordinate system of the needle tip at t₂; o₁ is the arc trajectory of the flexible steerable needle at t₁, the radius is r; o₁ is the arc trajectory of the flexible steerable needle at t₂, the radius is r; ω_x represents the rotational angular velocity of the needle tip along the x-axis of the inertial coordinate system.

Figure 2. Elastic changes in various path forms. (a) RR path form; (b) RL path form; (c) LR path form.

Figure 3. Path optimization using “Markov” decision-making process. (a) Before decision; (b) After decision.

Figure 4. Path optimization using reachable probability density function. (a) Reachable probability density function; (b) Path optimization result.

Figure 5. Puncture planning of three-dimensional obstacle environment. (a) Stop-turn; (b) Spiral line.

Figure 6. The strategy of needle tip reaching multiple targets. (a) 2D environment adjustment strategy; (b) 3D environment adjustment strategy.

Table 1. Path planning algorithm based on numerical calculation.

Classification	Methods	Advantages and disadvantages
2D path planning	Markov decision algorithm proposed by R. Alterovitz et al. [19–21]	Advantages: It can calculate the optimal turning point when the flexible steerable needle puncture (FSNP) the target point with the greatest probability, and choose the best puncture path.
		Disadvantages: Only applicable to the ‘s’ shaped trajectory curve connected by multiple arcs in two-dimensional plane, and can only be planned offline.
	Lyapunov energy optimization algorithm proposed by Sadati et al. [27]	Advantages: It can solve the puncture path of the flexible steerable needle in the uncertain environment, and can perform online planning tasks.
		Disadvantages: Because the algorithm is too complex, and the calculation time is too long, so the real-time performance of online planning is not good.
	Multi-objective optimization algorithm proposed by Bobrenkov, Huo Benyan et al. [32,34]	Advantages: It can carry out online planning (good real-time performance), and the path planning has high precision and little trauma to soft tissues.
		Disadvantages: The operation process of the path planning is particularly complicated, and it is prone to operation errors.
	Improved markov decision algorithm proposed by Tan et al. [35]	Advantages: It is possible to find the trajectory that has the greatest chance of avoiding obstacles and successfully inserting into the target when considering the contact deformation between the various organs during the puncture process, as well as the complex and uncertain force.
		Disadvantages: This method is only suitable for single arc path and it can only be planned offline in a simulation environment.
3D path planning	Single wheel model proposed by Park et al. [22–25]	Advantages: It can perform path planning well in a complex environment with multiple obstacles.
		Disadvantages: The generated path curve is not smooth enough, and the needle feeding accuracy is low, and can only be planned offline.
	Improved single wheel model proposed by Duindam et al. [26]	Advantages: The accuracy of path planning is high, the calculation is simple and time-saving.
		Disadvantages: The generated path curve is not smooth enough, and it can only be planned offline in a simulation environment.
	Probabilistic path algorithm based on fuzzy logic proposed by Lee et al. [30,31]	Advantages: The accuracy of path planning is high, and the generated path curve is smoother.
		Disadvantages: The algorithm is complex, the amount of calculation is large, and it can only be planned offline
	Multi-objective optimization algorithm based on single point insertion proposed by O. Bobrenkov et al. [32]	Advantages: The path planned by it can reduce the complex force between the needle and the tissue, can realize multi-target path planning, and has little trauma to the patient.
		Disadvantages: In the path planning process, the positioning accuracy of the target point is poor, and it can only be planned offline.
	Observable markov decision algorithm proposed by Sun et al. [33]	Advantage: It can combine the movement of the needle and the uncertain factors of the system state perception, and find the optimal puncture trajectory, and can carry out online planning.
		Disadvantages: The amount of data to be processed is large, the calculation time is long, and the real-time path planning process is prone to errors and harms the patient.
	Multi-objective dynamic weighting algorithm based on PSO proposed by Huo Benyan et al. [34]	Advantages: The path planning has high accuracy, can realize the path planning of multiple target points, and has good obstacle avoidance and less trauma to the patient.
		Disadvantages: The amount of calculation is large, the efficiency of path planning is low, and offline planning can only be performed.

Figure 7. RRTs algorithm proposed by Xu. (a) Front view; (b) Top view.

Figure 8. Patil’s improved RRT algorithm. (a) Front view; (b) Side view.

Figure 9. Planning strategy based on risk level. (a) Test 1; (b) Test 2.

Table 2. Path planning based on search algorithm.

Classification	Methods	Advantages and disadvantages
Two-dimensional planning	PRM algorithm represented by Alterovitz, Caborni et al. [41,42]	Advantages: The path planning has high accuracy, can ensure the safety of the operation when planning the path, and can perform online planning.
		Disadvantages: The amount of data to be processed is large and the work efficiency is low.
	RRT algorithm represented by J. Gustaaf, Xiong and Li Xia et al. [46,47,50]	Advantages: It can be combined with medical images for closed-loop feedback to correct the path in real time. The path planning has high accuracy, good algorithm interaction, and be able to plan online.
		Disadvantages: The operation process of real-time path planning is complicated, the planning time is long, errors are prone to occur and cause trauma to the patient.
	AI algorithms represented by N. Sadati, M. Bernardes et al. [38,43,44]	Advantages: The path planning has high accuracy and can be planned online.
		Disadvantages: The amount of data that needs to be calculated in the path planning process is large, and the real-time performance is not good.
Three-dimensional planning	RRT algorithm represented by Xu and S. Patil et al. [36,37,39,40]	Advantages: The calculation time is short, the path planning efficiency is high, the algorithm interaction is good, and online planning can be carried out.
		Disadvantages: The accuracy of path planning is low, and it needs to be combined with other algorithms

Figure 10. Path planning based on artificial potential field method.

Table 3. Path planning based on other algorithms.

Classification	Methods	Advantages and disadvantages
Two-dimensional planning	The inverse solution represented by V. Duindam et al. [51]	Advantages: The path planning of this method has high accuracy and good reliability.
		Disadvantages: This algorithm is only suitable for planning in a characteristic environment, and it can only be able to planned offline.
	The geometric illustration method represented by Hu Xiaotong and Zhou Li et al. [45–57]	Advantages: The solution process of path planning is simple and the amount of calculation is small.
		Disadvantages: The accuracy of path planning is poor, and it can only be planned offline.
Three-dimensional planning	Artificial potential field method represented by Wang, Jiang and Li et al. [52–54,58,59]	Advantages: The path planning of this method has high accuracy, and the algorithm interaction is good.
		Disadvantages: The amount of calculation is large, and it is easy to fall into a dead zone during the solution process, and it can only be able to planned offline.
	Data induction method represented by N. Hamze, C. Rossa et al. [60,61].	Advantages: The path planning has high accuracy and good reliability, and it can be used for both offline planning and online planning.
		Disadvantages: The amount of calculation is large, and the required hardware cost is high, and prone to delay.

Figure 11. RSPR 6-DOF parallel robot control system.

Figure 12. CT image overlay system for flexible steerable needle insertion process. (a) Overall system figure; (b) CT image identified by localization of bone biopsy.

Figure 13. MRI-based FSNP equipment.

Figure 14. Three-dimensional ultrasound guided robot puncture needle placement system. (a) Experimental institution; (b) 3D ultrasonic interface.

Figure 15. Diagram of closed-loop ultrasound image control system with updated stiffness.

Figure 16. Comparative analysis. (a) Open loop; (b) Closed loop; (c) Closed loop with updated stiffness.

Table 4. Performance comparison of various ways of navigation.

X-ray	Advantages	It is suitable for guiding the flexible steerable needle to puncture the soft tissue in the lumbar joint (bone), and the imaging effect in this aspect is very clear [75]. It can guide flexible steerable needle to puncture the calibration target, and its operation is simple, convenient, and economical.
	Disadvantages	The images of the metal needles in different depths of the tissue overlap and hide each other; Sometimes it takes multiple times to take X-rays from multiple angles to see clearly, and it is easy to lose sight of the tumor when it is too small. It has ionizing radiation to the human body, and too long operation time will cause higher radiation dose. It is mainly used for real-time puncture guidance on a 2D plane; although it can perform 3D reconstruction to obtain graphics, it is not conducive to real-time guided puncture of the flexible steerable needle [76].
B-mode ultrasound	Advantages	It is usually used for for transrectal guided flexible steerable needle puncture (FSNP) of soft tissues of the prostate [77–79], and it is also suitable for guiding flexible steerable needles to puncture soft tissues in the abdominal cavity (Hollow organs; It refers to organs that contains a lot of space inside the organs). Economical, safe and easy to operate, easy to observe.
	Disadvantages	It is usually used for fast and real-time guidance on a 2D plane. Although it can also be used for 3D image guidance, its puncture positioning effect is not as good as CT and MRI, and its real-time performance is not as good as for 2D plane guidance. The image scanning accuracy is poor. Although filtering algorithms are generally used to improve the image scanning accuracy, it is easy to cause image distortion, resulting in inaccurate positioning judgments, and difficult to meet the requirements of complex puncture operations.
CT	Advantages	It is mainly used to guide flexible steerable needles for soft tissue puncture of thoracic and lung [80,81]; Meanwhile, it also includes guided puncture of other substantial organs. It can use 3D reconstruction to guide the flexible steerable needle for spatial positioning (including: plane and depth). Compared with X-ray, it can use 3D images to guide the flexible steerable needle in real time; It can see small tumors that are difficult to see by X-ray (such as the posterior heart area, beside the spine), and the image scanning speed is faster than MRI.
	Disadvantages	It has a poor display effect on the lesion tissue of the empty organs and is not conducive to puncture guidance. The CT image is prone to artifacts in the environment with metal needles, which makes the metal flexible steerable needles prone to identification errors during the puncture process according to the path. It will have ionizing radiation to the human body, and it is easy to cause harm to the human body after long working.
MRI	Advantages	It is mainly used to guide the flexible steerable needle to puncture the soft tissues of the head and breast, and it is also suitable for the soft tissues of the urinary glands such as the prostate [82–84]. It has the ability to accurately distinguish soft tissue, and obtain volumetric image of multi-directional and multi-level without 3D reconstruction; It has good real-time operability. It has no ionizing radiation and is harmless to patients and doctors.
	Disadvantages	Complex equipment, difficult operation, high cost. The magnetic rheometer in MRI equipment often has distortion and magnetic field inhomogeneity, and there is also susceptibility artifact during the guided puncture process, which affects its effect of the puncture positioning. There is an audible noise of about 80 decibels in the scanning chamber, and long-term operation can easily cause claustrophobia to the patient.

Figure 17. Needle tail puncture control block diagram.

Figure 18. Force/position hybrid control block diagram.

Figure 19. Control block diagram of rotation feedback compensation based on torsion deformation model.

Figure 20. Model of needle body of flexible steerable needle reconstructed by FBG sensor.

Figure 21. Collaborative puncture robot.

Figure 22. Diagram of magnetic navigation FSNP (a) Device; (b) Process 1; (c) Process 2.

Figure 23. Pre-bent coaxial needle tube.

Figure 24. Pre-curved solid needle. (a) bevel tip needle; (b) Pre-curved needle.

Figure 25. Flexible steerable needle with bevel tip and multiple chain needles.

Figure 26. Flexible puncture needle with mechanical clutch device.

Sadati N, Torabi M. Adaptive 2D-path optimization of steerable bevel-tip needles in uncertain model of brain tissue. World Congress on Computer Science and Information Engineering. IEEE Computer Society; 2009. p. 254–260.

 Google Scholar

Bobrenkov O, Lee J, Park W. A new Geometry-Based plan for inserting flexible needles to reach multiple targets. Robotica. 2014;32(6):985–1004.

 Web of Science ®Google Scholar

Huo B, Zhao X, Han J, Xu  , et al. Path-tracking control of bevel-tip needles using model predictive control. IEEE 14th International Workshop on Advanced Motion Control; 2016; Auckland, New Zealand. p. 197–202.

 Google Scholar

Tan X, Yu P, Lim K, et al. Robust path planning for flexible needle insertion using markov decision processes. Int J Cars. 2018;13(9):1439–1451.

 Google Scholar

Duindam V, Alterovitz R, Sastry S, et al. Screw-Based motion planning for Bevel-Tip flexible needles in 3D environments with obstacles. IEEE International Conference on Robotics and Automation; May 19–23; Pasadena, CA: Institute of Electrical and Electronics Engineers Inc; 2008. p. 2483–2488.

 Google Scholar

Lee J, Park W. A Probability-Based path planning method using fuzzy logic. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014); Sep 14–18; Chicago, IL: Institute of Electrical and Electronics Engineers Inc; 2014. p. 2978–2984.

 Google Scholar

Lee J, Park W. Insertion planning for steerable flexible needles reaching multiple planar targets. New Horizon. 2015;40:2377–2383.

 Google Scholar

Sun W, Alterovitz R. Motion planning under uncertainty for medical needle steering using optimization in belief space. IEEE International Conference on Robotics and Automation; 2014; Hongkong, China.

 Google Scholar

Alterovitz R, Patil S, Derbakova A. Rapidly-exploring roadmaps: weighing exploration vs. refinement in optimal motion planning. Proceedings under IEEE International Conference on Robotics and Automation; 2011; Shanghai, China. p. 3706–3716.

 Google Scholar

Caborni C, Ko S, De M, et al. Risk-based path planning for a steerable flexible probe for neurosurgical intervention. IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics; 2012; Rome, Italy. p. 866–871.

 Google Scholar

Gustaaf J, Abayazid M. Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images. Int J Robot Res. 2014;33:1361–1374.

 PubMed Web of Science ®Google Scholar

Xiong J, Li X, Gan Y, et al. Path planning for flexible needle insertion system based on improved rapidly-exploring random tree algorithm. IEEE International Conference on Information and Automation; 2015; Lijiang, China. p. 1545–1550.

 Google Scholar

Li X, Li P, Xiong J. Path planning for flexible needle based on environment properties and random method. Comput Eng Appl. 2017;53:121–125.

 Google Scholar

Sadati N, Torabi M, Vaziri R, et al. Soft-tissue modeling and image-guided control of steerable needles. 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine; 2009; Minneapolis, MN. p. 5122–5125.

 Google Scholar

Bernardes M, Adorno B, Poignet P, et al. Robot-assisted automatic insertion of steerable needles with Closed-Loop imaging feedback and intraoperative trajectory replanning. Mechatronics. 2013;23(6):630–645.

 Web of Science ®Google Scholar

Bernardes M, Adorno B, Borges G, et al. 3D robust online motion planning for steerable needles in dynamic workspaces using Duty-Cycled rotation. J Control Autom Electr Syst. 2014;25(2):216–227.

 Google Scholar

Xu J, Duindam V, Alterovitz R, et al. Motion planning for steerable needles in 3D environments with obstacles using rapidly-exploring random trees and backchaining. 4th IEEE Conference on Automation Science and Engineering; 2008; Washington, DC. p. 41–46.

 Google Scholar

Xu J, Duindam V, Alterovitz R, et al. Planning fireworks trajectories for steerable medical needles to reduce patient trauma. IEEE/RSJ International Conference on Intelligent Robots and Systems; 2009; St. Louis, MO. p. 4517–4522.

 Google Scholar

Patil S, Alterovitz R. Interactive motion planning for steerable needles in 3D environments with obstacles. 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics; 2010; Tokyo, Japan. p. 893–899.

 Google Scholar

Patil S, Burgner J, Webster R, et al. Needle steering in 3-D via rapid replanning. IEEE Trans Robot. 2014;30(4):853–864.

 PubMed Web of Science ®Google Scholar

Duindam V, Xu J, Alterovitz R, et al. 3D motion planning algorithms for steerable needles using inverse kinematics. 8th International Workshop on the Algorithmic Foundations of Robotics; 2008; Guanajuato, Mexico. p. 535–549.

 Google Scholar

Li P, Jiang S, Yang J. A combination method of artificial potential field and improved conjugate gradient for trajectory planning for needle insertion into soft tissue. J Med Biol Eng. 2014;34(2):178–573.

 Google Scholar

Li P, Jiang S, Liang D, et al. Modeling of path planning and needle steering with path tracking in anatomical soft tissues for minimally invasive surgery. Med Eng Phys. 2017;41:35–45.

 PubMed Web of Science ®Google Scholar

Hamze N, Peterlik L, Cotin S, et al. Preoperative trajectory planning for percutaneous procedures in deformable environments. Comput Med Imaging Graph. 2016;47:16–28.

 PubMed Web of Science ®Google Scholar

Rossa C, Lehmann T, Sloboda R, Usmani  , et al. A data-driven soft sensor for needle deflection in heterogeneous tissue using just-in-time modeling. Med Biol Eng Comput. 2017;55(8):1401–1414. Aug.

 PubMed Web of Science ®Google Scholar

Zhang R, Chen S, Ou J, et al. X-ray guided lumbar facet joint intra-articular injection for lumbar facet joint osteoarthritis. Clin Med Eng. 2009;16:16–18.

 Google Scholar

Bossi R, Shepherd W. X-Ray computed tomography for failure analysis investigations. Opt Express. 2021;29(2).

 Google Scholar

Huo X, Yang S, Guo Z. The clinical application study of low-dose CT-guided thoracic puncture biopsy. J Med Imaging. 2020;12:2228–2231.

 Google Scholar

Yang C, Wu Y, Hu Y, et al. Application of bipolar laser positioning navigator in CT-guided percutaneous transthoracic needle biopsy for lung cancer. Chin Clin Oncol. 2020;25:1116–1120.

 Google Scholar