School of Biomedical Engineering, Capital Medical University, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing 100069, China.
School of Biomedical Engineering, Capital Medical University, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing 100069, China.
Comput Methods Programs Biomed. 2022 Dec;227:107202. doi: 10.1016/j.cmpb.2022.107202. Epub 2022 Oct 26.
Safety and accuracy are essential for path planning in a surgical navigation system. In this paper, an improved path planning algorithm is proposed to increase the autonomous level of spine surgery robots for higher safety and accuracy. Firstly, the dynamic gravitational constant and piecewise repulsion function are adopted to improve the traditional Artificial Potential Field algorithm to solve the common issues of path planning, including local minimum, unable to reach the target near obstacles. To better control the pose of the end-effector in an operation space, the positions of the two endpoints of the end-effector are further constrained. Secondly, an improved Primal-Dual Neural Network with multiple constraints is proposed to minimize the joint angular velocity norm. The multiple constraints are formulated according to the planned path, the obstacle avoidance of the robot and the joint limits. Moreover, a real-time planned velocity scheme is applied to prevent the accumulation of position errors. The simulation results of the pedicle screw implantation demonstrate that the robot can find the collision-free trajectory and arrive at the target position in various complicated situations. More specifically, the error between two endpoints of the end-effector and the target pose is below 0.1 mm in reaching the surgical tool pose, while the maximum position error is around 0.05 mm when performing the planned path. Moreover, two experiments are conducted in the real-world to verify the proposed algorithm is effective in practice.
安全性和准确性对于手术导航系统中的路径规划至关重要。本文提出了一种改进的路径规划算法，以提高脊柱手术机器人的自主性水平，从而实现更高的安全性和准确性。首先，采用动态引力常数和分段斥力函数改进传统的人工势场算法，解决路径规划中常见的局部最小值和无法到达障碍物附近目标等问题。为了更好地控制操作空间中末端执行器的姿态，进一步约束末端执行器的两个端点的位置。其次，提出了一种具有多个约束的改进的对偶神经网络，以最小化关节角速度范数。多个约束根据规划路径、机器人的避障和关节限制来制定。此外，应用实时规划速度方案以防止位置误差的累积。经椎弓根螺钉植入的仿真结果表明，机器人可以在各种复杂情况下找到无碰撞轨迹并到达目标位置。更具体地说，当到达手术工具位置时，末端执行器的两个端点和目标姿态之间的误差低于 0.1 毫米，而在执行规划路径时，最大位置误差约为 0.05 毫米。此外，在实际环境中进行了两项实验，验证了所提出算法的有效性。
