Abstract
As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of 20 s to 10 s after the first phase optimization, and the energy consumption per payload reduced by 13.28% after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.
Highlights
A cable shovel is one of the key equipment in the open-pit mining industry traditionally operated by trained operators [1]
The results indicated that the first phase optimization could adapt to different digging conditions
A kind of multi-objective two-phase optimization method was established for robotic excavation of cable shovels in this research
Summary
A cable shovel is one of the key equipment in the open-pit mining industry traditionally operated by trained operators [1]. Abundant research results indicate that human factors have become a main factor inducing maintenance cost and reliability risk [2,3]. Current shovels with larger bucket capacity bring the benefit of lower digging cost, but higher operational difficulty, which makes the concept of robotic excavation a practical option for effective and steady digging processes. Rational planning of digging trajectory is the base for robotic excavation. Optimized the hoist and crowd speed of a P&H 2100b cable shovel based on the Balovnev model with the objective of minimizing the unit payload energy consumption [4]. Dunbabin M. et al designed an operation assistant system for cable shovels, which can help the driver plan the digging trajectory and predict the bucket fullness [5]. Wei B. et al came up with a three degree of freedom (DOF)
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