Flow-Achieving Online Planning and Dispatching for Continuous Transportation with Autonomous Vehicles
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Automation Science and Engineering, 2022, 19, (1), pp. 457-472
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Flow-Achieving_Online_Planning_and_Dispatching_for_Continuous_Transportation_With_Autonomous_Vehicles.pdf | Published version | 1.86 MB |
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In large-scale industrial applications, goods must be continuously transported between locations, which in the absence of conveyor systems is by a fleet of individual vehicles. This article introduces flow-achieving scheduling tree (FAST), an online dispatching algorithm that allows vehicles to efficiently operate as a team to maximize the system's throughput while meeting a production schedule. A high-performance model is developed for high-fidelity prediction of vehicle interactions and system performance. It is subsequently optimized using a self-tuning variant of Monte Carlo tree search (MCTS) to make agile dispatch decisions in real time. The method is validated using an open-cut mine site and is shown to outperform a commonly used algorithm in this domain. Note to Practitioners - This article was motivated by the problem of dispatching autonomous haul trucks on open-cut mine sites. The proposed method is suited to any industrial transportation system where a continuous stream of goods must be efficiently transported between the load and unload stations by a potentially heterogeneous fleet of automated vehicles. The system makes decisions in real time while reacting to performance variations and disturbances by using a receding horizon approach. Off-the-shelf software commonly used in this domain is based on heuristics with limited ability to optimize, leading to myopic decision making without taking vehicle interactions into account. Here, flow-achieving scheduling tree (FAST) overcomes this by optimizing over possible schedules and thereby implicitly accounting for knock-on effects. Future work will incorporate additional constraints into the optimization process and validate FAST in other industrial domains.
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