“…However, this method does not achieve path tracking, making it challenging to implement closed-loop motion control during operations. Zhao et al [ 102 ] proposed an intraoperative flexible-needle steering path in a dynamic environment based on APF and curve fitting algorithms, optimized several feasible derived paths, and compared its results with those of the RRT algorithm in 3D simulation experiments, as presented in Fig. 10 (b).…”
Section: Path Planning Methods and Resultsmentioning
confidence: 99%
“… Fig. 10 Path planning based on APF algorithm: (a) the path planned by Zhang et al [ 77 ]; (b) the path planned by Zhao et al [ 102 ]. …”
Section: Path Planning Methods and Resultsmentioning
confidence: 99%
“… Disadvantages: It requires big data support, large computational volume, and high hardware cost. Three-dimensional path planning APF method represented by Zhang, Jiang, Liu, and Zhao et al [ 77 , [99] , [100] , [101] , [102] ] Advantages : The path planning of this method has high positioning accuracy and safety, and the working principle of the algorithm is simple and easy to understand. Disadvantages : It is computationally intensive and inefficient, and sometimes, the path cannot be planned, and path planning cannot be planned in real time during surgery.…”
Section: Path Planning Methods and Resultsmentioning
“…However, this method does not achieve path tracking, making it challenging to implement closed-loop motion control during operations. Zhao et al [ 102 ] proposed an intraoperative flexible-needle steering path in a dynamic environment based on APF and curve fitting algorithms, optimized several feasible derived paths, and compared its results with those of the RRT algorithm in 3D simulation experiments, as presented in Fig. 10 (b).…”
Section: Path Planning Methods and Resultsmentioning
confidence: 99%
“… Fig. 10 Path planning based on APF algorithm: (a) the path planned by Zhang et al [ 77 ]; (b) the path planned by Zhao et al [ 102 ]. …”
Section: Path Planning Methods and Resultsmentioning
confidence: 99%
“… Disadvantages: It requires big data support, large computational volume, and high hardware cost. Three-dimensional path planning APF method represented by Zhang, Jiang, Liu, and Zhao et al [ 77 , [99] , [100] , [101] , [102] ] Advantages : The path planning of this method has high positioning accuracy and safety, and the working principle of the algorithm is simple and easy to understand. Disadvantages : It is computationally intensive and inefficient, and sometimes, the path cannot be planned, and path planning cannot be planned in real time during surgery.…”
Section: Path Planning Methods and Resultsmentioning
“… 180 Then, a path planning and tracking control method for flexible needle steering in dynamic environment was proposed based on the artificial potential field algorithm and curve fitting algorithm. 181 …”
Cancer, with high morbidity and high mortality, is one of the major burdens threatening human health globally. Intervention procedures via percutaneous puncture have been widely used by physicians due to its minimally invasive surgical approach. However, traditional manual puncture intervention depends on personal experience and faces challenges in terms of precisely puncture, learning-curve, safety and efficacy. The development of puncture interventional surgery robotic (PISR) systems could alleviate the aforementioned problems to a certain extent. This paper attempts to review the current status and prospective of PISR systems for thoracic and abdominal application. In this review, the key technologies related to the robotics, including spatial registration, positioning navigation, puncture guidance feedback, respiratory motion compensation, and motion control, are discussed in detail.
“…In terms of the APF-based path planning algorithm, which is easy for the agent to be stuck in local minima, Zhao et al. [ 6 ] proposed a 2-dimensional (2D) steerable needle path planner using a modified APF algorithm in a planar environment with only several obstacles. Optimization-based algorithms, especially the particle swarm optimization algorithm, and evolutionary algorithm have been adopted a lot.…”
The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain; with proper path planning, it can also minimize the potential damage by setting constraints and optimizing the insertion path. Recently, reinforcement learning (RL)-based path planning algorithm has shown promising results in neurosurgery, but because of the trial and error mechanism, it can be computationally expensive and insecure with low training efficiency. In this paper, we propose a heuristically accelerated deep Q network (DQN) algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment. Furthermore, a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm. Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms. Tests showed promising results of our algorithm in saving over 50 training episodes, calculating path lengths of 0.35 after normalization, which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm, respectively. Moreover, the maximum curvature during planning is reduced to 0.046 from 0.139 mm
−1
using the proposed algorithm compared to DQN.
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