An Optimal Allocation and Scheduling Method in Human-Multi-Robot Precision Agriculture Settings. Lippi, M., Gallou, J., Gasparri, A., & Marino, A. 2023. ![An Optimal Allocation and Scheduling Method in Human-Multi-Robot Precision Agriculture Settings [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Employing teams of robots to offer services to human operators enables the latter to reduce their physical workload. In this paper, we focus on the problem of optimally allocating and scheduling the robot tasks in order to serve human operators. We formulate a Mixed-Integer Linear Programming problem that aims to minimize the human waiting time and the energy spent by the robots, while ensuring that any velocity constraints of the robots are fulfilled and the task ordering is correct. In addition, we propose an online re-allocation strategy that takes into account the possibility of changing human parameters over time. This strategy determines whether a new optimal solution must be computed. We validate the proposed framework in a simulated precision agriculture setting composed of two robots and four human operators for a harvesting application.
@conference{
11580_106723,
author = {Lippi, Martina and Gallou, Jorand and Gasparri, Andrea and Marino, Alessandro},
title = {An Optimal Allocation and Scheduling Method in Human-Multi-Robot Precision Agriculture Settings},
year = {2023},
publisher = {IEEE},
booktitle = {31st Mediterranean Conference on Control and Automation, MED 2022},
abstract = {Employing teams of robots to offer services to human operators enables the latter to reduce their physical workload. In this paper, we focus on the problem of optimally allocating and scheduling the robot tasks in order to serve human operators. We formulate a Mixed-Integer Linear Programming problem that aims to minimize the human waiting time and the energy spent by the robots, while ensuring that any velocity constraints of the robots are fulfilled and the task ordering is correct. In addition, we propose an online re-allocation strategy that takes into account the possibility of changing human parameters over time. This strategy determines whether a new optimal solution must be computed. We validate the proposed framework in a simulated precision agriculture setting composed of two robots and four human operators for a harvesting application.},
keywords = {Human-robot collaboration, Optimal Allocation, Precision Agriculture},
url = {https://ieeexplore.ieee.org/abstract/document/10185899/authors},
doi = {10.1109/med59994.2023.10185899},
isbn = {979-8-3503-1544-8},
pages = {541--546}
}
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