Rolling trajectory generation for autonomous forklifts based on task time constraint
As the demand for operational efficiency in intelligent factories and logistics systems continues to increase, trajectory planning for autonomous forklifts has become a critical technological challenge. In this paper, we propose a Task-Time Constrained Trajectory Rolling Planning (TTRP) approach tha...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-03-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132251330142 |
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| Summary: | As the demand for operational efficiency in intelligent factories and logistics systems continues to increase, trajectory planning for autonomous forklifts has become a critical technological challenge. In this paper, we propose a Task-Time Constrained Trajectory Rolling Planning (TTRP) approach that ensures timely task completion despite unforeseen circumstances. The TTRP method calculates time intervals between waypoints based on task time constraints and the forklift’s kinematic and dynamic models. By employing cubic spline interpolation, it replans the remaining trajectory after unplanned stops, ensuring smooth transitions and adherence to task time constraints. We validated the TTRP method using a ROS-based Gazebo simulation environment. Simulation results indicate that TTRP significantly improves trajectory smoothness and stability compared to traditional quintic polynomial interpolation methods. Specifically, TTRP reduces maximum jerk by up to 89.7%, decreases the standard deviation of acceleration by 88.1%, and lowers maximum speed variation by 61%. These findings demonstrate that TTRP efficiently generates smooth, feasible trajectories that meet the kinematic, dynamic, and task time requirements of autonomous forklifts in intelligent factory environments. |
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| ISSN: | 1687-8140 |