Time-jerk optimal trajectory planning of hydraulic robotic excavator

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.

Automated summary

This paper investigates the optimal trajectory planning of a hydraulic robotic excavator using the Sequential Quadratic Programming (SQP) algorithm, considering a trade-off between time and acceleration. By utilizing cubic splines for interpolation in joint space, the optimal angle curves for each joint are obtained and the excavator's optimal time-jerk trajectory planning is achieved. Experimental results demonstrate that the SQP method is more efficient in solving the optimal solution under the same weight, resulting in smoother excavating trajectories and improved stability and efficiency in autonomous operation.