The use of heterogeneous human-multi-robot teams enables the combination of complementary skills of these two different types of agents. To have an effective collaboration, it is necessary to define a strategy for allocating and scheduling tasks among them. In this work, we distinguish robots in working robots and service ones: working robots and human operators can perform similar tasks in the environment and both are assisted by service robots. We propose a Mixed-Integer Linear Programming approach that aims to minimize the waiting times of the working agents, the energy consumption of the service robots, and the makespan while ensuring that the velocity constraints of the robots are met and the task ordering is correct. Furthermore, we propose an online updating strategy that tackles changes in the parameters of working agents and adapts the plan accordingly based on a heuristic algorithm. To validate our framework, we analyze a precision agriculture harvesting application with two human operators, two working robots, and two service robots.
Lippi, M., Gallou, J., Palmieri, J., Gasparri, A., Marino, A. (2023). Human-Multi-Robot Task Allocation in Agricultural Settings: a Mixed Integer Linear Programming Approach. In 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (pp.1056-1062). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.1109/RO-MAN57019.2023.10309392].
Human-Multi-Robot Task Allocation in Agricultural Settings: a Mixed Integer Linear Programming Approach
Lippi, M;Gallou, J;Gasparri, A;
2023-01-01
Abstract
The use of heterogeneous human-multi-robot teams enables the combination of complementary skills of these two different types of agents. To have an effective collaboration, it is necessary to define a strategy for allocating and scheduling tasks among them. In this work, we distinguish robots in working robots and service ones: working robots and human operators can perform similar tasks in the environment and both are assisted by service robots. We propose a Mixed-Integer Linear Programming approach that aims to minimize the waiting times of the working agents, the energy consumption of the service robots, and the makespan while ensuring that the velocity constraints of the robots are met and the task ordering is correct. Furthermore, we propose an online updating strategy that tackles changes in the parameters of working agents and adapts the plan accordingly based on a heuristic algorithm. To validate our framework, we analyze a precision agriculture harvesting application with two human operators, two working robots, and two service robots.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
