Multi-objective optimization of electric shovel excavation trajectories using ore distribution perception and reinforcement learning

AbstractSafe and efficient excavation trajectories are essential for autonomous operation of intelligent electric shovels in open-pit mining. However, irregular ore pile distributions, multi-objective requirements, and operational constraints pose a significant challenge to the real-time generation of high-performance trajectories. This paper formulates the excavation trajectory optimization as a Markov decision process and proposes a real-time multi-objective optimization surrogate model based on reinforcement learning, with the objectives of maximizing bucket fill rate, minimizing mass-specific energy consumption, and reducing excavation time. By embedding the solution evolution into reinforcement learning training process, the model achieves a 2.87 s runtime, 84.13% non-dominated solutions, and a hypervolume value of 0.9403, outperforming other multi-objective optimization algorithms. After optimization, an entropy-based decision-making method is designed to objectively select the final excavation trajectory from obtained non-dominated solutions. Simulations and experiments indicate that the surrogate model and decision-making method effectively enable efficient and stable excavation for electric shovels.