Due to the fact that intelligent algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) are susceptible to local optima and the efficiency of solving an optimal solution is low when solving the optimal trajectory, this paper uses the Sequential Quadratic Programming (SQP) algorithm for the optimal trajectory planning of a hydraulic robotic excavator. To achieve high efficiency and stationarity during the operation of the hydraulic robotic excavator, the trade-off between the time and jerk is considered. Cubic splines were used to interpolate in joint space, and the optimal time-jerk trajectory was obtained using the SQP with joint angular velocity, angular acceleration, and jerk as constraints. The optimal angle curves of each joint were obtained, and the optimal time-jerk trajectory planning of the excavator was realized. Experimental results show that the SQP method under the same weight is more efficient in solving the optimal solution and the optimal excavating trajectory is smoother, and each joint can reach the target point with smaller angular velocity, and acceleration change, which avoids the impact of each joint during operation and conserves working time. Finally, the excavator autonomous operation becomes more stable and efficient.

由于粒子群优化（PSO）和差分进化（DE）等智能算法在求解最优轨迹时易受局部最优影响，求解最优解的效率较低，本文采用序列二次规划（Sequential Quadratic Programming） SQP) 算法用于液压机器人挖掘机的最佳轨迹规划。为了在液压机器人挖掘机的运行过程中实现高效率和平稳性，需要考虑时间和加加速度之间的权衡。使用三次样条在关节空间进行插值，以关节角速度、角加速度和加加速度为约束条件，使用SQP得到最优的时加加速度轨迹。得到各关节的最佳角度曲线，并实现了挖掘机的最佳时加轨迹规划。实验结果表明，相同权重下的SQP方法求解最优解的效率更高，最优挖掘轨迹更平滑，每个关节都能以更小的角速度和加速度变化到达目标点，避免了每个关节的影响。在操作过程中连接并节省工作时间。最后，挖掘机自主运行变得更加稳定和高效。