An Energy-Saving Path Planning Method for Autonomous Trucks in Underground Mining Areas

With the advancement of green mining initiatives, the high energy consumption and emissions in underground mining transportation have become critical challenges restricting sustainable development in the industry. To optimize energy use and support green transitions in mining operations, this study proposes an energy-saving path planning method based on an improved Lagrangian relaxation algorithm. The method optimizes transportation cost, time, and energy-saving objectives, enhancing the efficiency of multi-trucks collaborative operations. Experimental results demonstrate that the proposed approach significantly reduces energy consumption in complex multi-trucks transportation scenarios, achieving an average energy-saving rate of 6.12%, and outperforms traditional solvers such as CPLEX and Gurobi in computational efficiency and planning performance. These findings provide essential theoretical support and practical guidance for advancing energy savings and green energy transitions in mining transportation.