An improved path planning algorithm based on artificial potential field and primal-dual neural network for surgical robot

Puncture path planning algorithm of flexible needle in percutaneous nephrolithotomy with a hybrid NSGA optimizer

BACKGROUND

Percutaneous nephrolithotomy is the standard treatment for large or irregularly shaped kidney stones, especially staghorn calculus. However, establishing a precise and safe puncture is challenging and requires extensive training for the surgeon. Navigation surgery is a commonly employed technique that facilitates the puncture through generating a path before surgery. One critical challenge for navigation is skin-kidney path planning due to the complex anatomical deconstruction of the kidney as well as the irregular shape of kidney stones.

METHOD

In this paper, we propose a hybrid strategy puncture path planning algorithm, where we follow a 2-step flow path that considers the selection of puncture renal calyces and planning of a B-spine curve path. We imitate the decision-making process of the clinician in selecting the puncture calyx based on the projective area of the calculi to be cleared. We summarize subjective judgment and clinical experience during puncture, where parametric optimization indicators are proposed to realize the optimization of puncture path.

RESULTS

An optimal frontier consisting of puncture pathways focused on different puncture factors can be generated from the proposed algorithm, where the physician can choose the path that works best under real circumstances. Results in 2D simulation show that the planned pathway is similar to that planned by a urologist.

CONCLUSIONS

The proposed 2-Step hybrid strategy reaches a balance on both optimal effect and efficiency. This automatic planning method based on the long axis section of the kidney can quickly and autonomously provide physicians with a series of optimized puncture paths, and provide auxiliary guidance for clinicians, especially young physicians. Nevertheless, the proposed method shows considerable potential in percutaneous nephrolithotomy surgical demonstration and teaching, and can integrated into robotic surgical navigation system.

A robotic system for transthoracic puncture of pulmonary nodules based on gated respiratory compensation

BACKGROUND AND OBJECTIVE

With the urgent demands for rapid and precise localization of pulmonary nodules in procedures such as transthoracic puncture biopsy and thoracoscopic surgery, many surgical navigation and robotic systems are applied in the clinical practice of thoracic operation. However, current available positioning methods have certain limitations, including high radiation exposure, large errors from respiratory, complicated and time-consuming procedures, etc. METHODS: To address these issues, a preoperative computed tomography (CT) image-guided robotic system for transthoracic puncture was proposed in this study. Firstly, an algorithm for puncture path planning based on constraints from clinical knowledge was developed. This algorithm enables the calculation of Pareto optimal solutions for multiple clinical targets concerning puncture angle, puncture length, and distance from hazardous areas. Secondly, to eradicate intraoperative radiation exposure, a fast registration method based on preoperative CT and gated respiration compensation was proposed. The registration process could be completed by the direct selection of points on the skin near the sternum using a hand-held probe. Gating detection and joint optimization algorithms are then performed on the collected point cloud data to compensate for errors from respiratory motion. Thirdly, to enhance accuracy and intraoperative safety, the puncture guide was utilized as an end effector to restrict the movement of the optically tracked needle, then risky actions with patient contact would be strictly limited.

RESULTS

The proposed system was evaluated through phantom experiments on our custom-designed simulation test platform for patient respiratory motion to assess its accuracy and feasibility. The results demonstrated an average target point error (TPE) of 2.46 ± 0.68 mm and an angle error (AE) of 1.49 ± 0.45° for the robotic system.

CONCLUSIONS

In conclusion, our proposed system ensures accuracy, surgical efficiency, and safety while also reducing needle insertions and radiation exposure in transthoracic puncture procedures, thus offering substantial potential for clinical application.

Needle-tissue interaction model based needle path planning method

BACKGROUND AND OBJECTIVE

In needle insertion procedure, needle deflection and target movement will affect targeting accuracy. Existing planning algorithms rely on predetermined interaction force and parameters, which increase the targeting error for the patient-specific difference. In this paper, we proposed a needle-tissue interaction model based needle path planning method with patient-specific parameter identification algorithm, which is able to use iteration learning control and interaction model predicted information to improve targeting accuracy with the consideration of patient-specific differences.

METHODS

A 3D needle-tissue interaction deformation model has been constructed using local constraint method. The model, termed as the full computation model, predicts the needle-tissue interaction force using a Kriging-based model as well as the target movement and needle deflection simultaneously only requiring patient specific parameters. Needle paths without incorporating deformation, which is called static path, are generated by rapidly-exploring random trees algorithm first. Then, the needle-tissue interaction deformation model can calculate force and deformation of the static path and iterative learning control can correct the targeting error of moved target. In addition, the intraoperative parameter identification algorithm is proposed to identify patient-specific parameter. Simulations are carried out to verify the full computation model and needle path planning. A testbed is constructed and experiments are designed to validate the proposed method using phantom with common lesion-size obstacle markers and target markers. The deformation of tissue and needle are captured through charge coupled device camera.

RESULTS

Simulation results indicated the full computation model can simulate the needle-tissue interaction process and the proposed method can achieve needle path planning incorporating tissue deformation. Experiment results indicated the tissue deformation and needle deflection agree between model prediction and experiments. The proposed path planning method can reduce targeting error from maximum of 3.89 mm without incorporating deformation to less than 1 mm in 4 phantom experiments.

CONCLUSIONS

The full computation model based needle path planning is verified to be effective by experiments. The planning accuracy is improved based on the deformation predicted by full computation model and the desired accuracy is achieved.

A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots

Secure and reliable active debris removal methods are crucial for maintaining the stability of the space environment. Continuum robots, with their hyper-redundant degrees of freedom, offer the ability to capture targets of varying sizes and shapes through whole-arm grasping, making them well-suited for active debris removal missions. This paper proposes a pre-grasping motion planning method for continuum robots based on an improved artificial potential field to restrict the movement area of the grasping target and prevent its escape during the pre-grasping phase. The analysis of the grasping workspace ensures that the target is within the workspace when starting the pre-grasping motion planning by dividing the continuum robot into delivery and grasping segments. An improved artificial potential field is proposed to guide the continuum robot in surrounding the target and creating a grasping area. Specifically, the improved artificial potential field consists of a spatial rotating potential field, an attractive potential field incorporating position and posture potential fields, and a repulsive potential field. The simulation results demonstrate the effectiveness of the proposed method. A comparison of motion planning results between methods that disregard and consider the posture potential field shows that the inclusion of the posture potential field improves the performance of pre-grasping motion planning for spatial targets, achieving a success rate of up to 97.8%.