Excavation Trajectory Planning for the Large-scale Mining Excavator Based on Polynomial Optimization

In intelligent mining, planning efficient and low-energy excavation trajectories is a key issue. In this paper, the excavation trajectory planning algorithm based on polynomial optimization is designed for the WK-35 large mining excavator. An excavation trajectory description model is established based on the sixth-order polynomial, and the parameters of polynomial are optimized iteratively based on genetic algorithm. Compared to existing algorithms, the optimization object is defined as excavation trajectories, instead of motor speed, improving the trajectory optimization capacity. To improve the accuracy of the dynamic model, the multi-step Newton-Euler method is used instead of the Lagrange method, which can consider factors such as Coriolis force and centripetal force of each component. Simulation results are compared with other planning algorithms and actual mining samples of WK-35. The study shows that, compared to existing algorithms, the designed algorithm can reduce energy consumption in the mining process and improve excavation efficiency.