基于粒子群优化算法的绳驱动连续体机器人轨迹规划

为提高绳驱动连续体机器人运动的平滑性和稳定性，在关节空间和笛卡尔空间研究了基于样条函数和粒子群算法的轨迹规划问题。首先，采用双参数局部指数积公式建立连续体机器人的运动学模型；其次，根据牛顿-拉夫森迭代方法进行逆运动学求解；最后，基于自适应惯性权重的粒子群时间最优化算法结合五次B样条函数，分别实现了连续体机器人在关节空间和笛卡尔空间的轨迹规划。仿真结果表明：在相同的条件下，两种方法均可得到连续体机器人末端的连续轨迹，速度均小于10 mm/s，加速度均小于20 mm/s²；基于关节空间规划出的关节位移、速度、加速度曲线更为平滑，关节空间规划用时9.219 3 s，笛卡尔空间规划用时10.604 6 s。基于粒子群优化算法的绳驱动连续体机器人轨迹规划研究，提高了连续体机器人的运动性能，可为绳驱动连续体机器人的位姿规划提供参考。

Trajectory planning of a cable-driven continuum robot based on particle swarm optimization algorithm

In order to improve the smoothness and stability of the motion of the cable-driven continuum robot, the trajectory planning methods based on spline function and particle swarm optimization algorithm were proposed for the cable-driven continuum robot in its joint space and Cartesian space respectively. Firstly, the kinematic model was established by applying the local product-of-exponential(POE) formula with two parameters. Secondly, the inverse kinematics was solved by Newton Raphson iterative method. Finally, the particle swarm time optimization algorithm based on adaptive inertia weight combing with the quintic B-spline function was used to realize the trajectory planning of the continuum robot in joint space and Cartesian space respectively. The simulation results show that continuous trajectories can be obtained both in joint space and Cartesian space under the same conditions, the obtained velocities are less than 10 mm/s, and the accelerations are less than 20 mm/s2.The joint displacement, velocity and acceleration curves are smoother in joint space, which takes 9.219 3 s, while it takes 10.604 6 s in Cartisian space. The research on trajectory planning of a cable-driven continuum robot based on particle swarm optimization algorithm improves the kinematic performance of the continuum robot and provides references for pose planning of the cable-driven continuum robots.