| MULTI-OBJECTIVE VELOCITY TRAJECTORY OPTIMIZATION METHOD
FOR AUTONOMOUS MINING VEHICLES |
| Shuwei Pei , Jue Yang |
| University of Science and Technology Beijing |
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| ABSTRACT |
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Autonomous mining transportation is an intelligent traffic control system that can provide better economics
than traditional transportation systems. The velocity trajectory of a manned vehicle depends on the driver’s driving style. Still,
it can be optimized utilizing mathematical methods under autonomous driving conditions. This paper takes fuel and electric
mining vehicles with a load capacity of 50 tons as the subject. It contributes a multi-objective optimization approach
considering time, energy consumption, and battery lifetime. The dynamic programming (DP) algorithm is used to solve the
optimal velocity trajectory with different optimization objectives under two types of mining condition simulation. The
trajectories optimized by the single objective, energy consumption, usually adopt the pulse-and-gliding (PnG) approach
frequently, which causes the battery capacity loss and increases the travel time. Hence, a multi-objective optimization
approach is proposed. For electric vehicles, trajectories optimized by the multi-objective approach can decrease the battery
capacity loss by 22.01 % and the time consumption by 41.28 %, leading to a 42.12 % increment in energy consumption.
For fuel vehicles, it can decrease the time consumption by 32.54 %, leading to a 7.68 % increment in energy consumption.
This velocity trajectory is smoother with less fluctuation. It can better meet the requirements of mining transportation and has
a particular reference value for optimizing autonomous transportation costs in closed areas. |
| Key Words:
Velocity trajectory, Energy consumption, Transportation efficiency, Multi-objective optimization |
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Corresponding Author.
Jue Yang , Email. 
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