Ponencia
Predictive Receding-Horizon Multi-Robot Task Allocation with Moving Tasks*
Autor/es | García Martín, Javier
Hanif, Muhammad Hatanaka, Takeshi Maestre Torreblanca, José María Camacho, Eduardo F. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2022 |
Fecha de depósito | 2023-03-03 |
Publicado en |
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Resumen | This paper addresses a multi-robot task allocation (MRTA) towards moving tasks and presents a novel computationally efficient predictive allocation algorithm that requires solving a linear program (LP) problem. Following ... This paper addresses a multi-robot task allocation (MRTA) towards moving tasks and presents a novel computationally efficient predictive allocation algorithm that requires solving a linear program (LP) problem. Following the receding horizon control policy, the present algorithm repeats the optimization of future task assignments within an allocation horizon while predicting the evolution of the system. The online optimization is formulated so that the assignment problem is reduced exactly to an LP. The algorithm is also compared with other traditional methods, namely, the greedy approach and a genetic algorithm (GA). Our results show that the algorithm herein proposed outperforms the greedy approach for small prediction horizons and has significantly lower computational load than GA. |
Cita | García Martín, J., Hanif, M., Hatanaka, T., Maestre Torreblanca, J.M. y Camacho, E.F. (2022). Predictive Receding-Horizon Multi-Robot Task Allocation with Moving Tasks*. En European Control Conference (ECC) (2030-2035), Londres, Reino Unido: IEEE (Institute of Electrical and Electronics Engineers). |