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

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.

Dimensional Synthesis for Multi-Linkage Robots Based on a Niched Pareto Genetic Algorithm

The dimensional synthesis of multi-linkage robots has great significance for improving flexibility and efficiency. With the increase of the degree of freedom and restrictions on special occasions, the solution of dimensional synthesis becomes complicated and time-consuming. Theory of workspace density function, maneuverability, and energy expenditure had been studied. With high flexibility and low energy consumption as the design goal, the method for dimensional and joint angle synthesis of multi-linkage robots was proposed based on a niched Pareto genetic algorithm. The Pareto solution set has been obtained. The method was verified by two application examples, which is occlusion of the solar salt evaporation pool and the secondary scattering of solid 2,2′-azobis(2,4-dimethylvaleronitrile). Through the application of NPGA (niched Pareto genetic algorithm) compared with KPCA (kernel principal component analysis), it can save 12.37% time in occlusion of one evaporating pool and reduce energy consumption by 3.85%; it can save 9.96% time in scattering of remain materials per barrel and reduce energy consumption by 1.77%. The study reduces the labor intensity of manual workers in the salt making industry, ensures the safe production of dangerous chemicals, and provides new ideas and methods for the dimensional synthesis of multi-linkage robots.