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A Task Allocation Approach of Multi-Heterogeneous Robot System for Elderly Care

Authors :
Donghui Zhao
Chenhao Yang
Tianqi Zhang
Junyou Yang
Yokoi Hiroshi
Source :
Machines, Vol 10, Iss 8, p 622 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Roboticized nursing technology is a significant means to implement efficient elderly care and improve their welfare. Introducing multi-heterogeneous robot systems (MHRS) and sensor networks into a smart home is a promising approach to improve the safety and acceptability of elderly care services in daily life. Among them, the energy consumption and task planning of MHRS determine nursing safety, which is particularly important in the real nursing process. Therefore, we established a novel smart home for elderly care based on seven heterogeneous nursing robots, and proposed a multi-robot task allocation (MRTA) algorithm, considering execution time and energy consumption. The whole system efficiency makes up for the functional limitations and service continuity of traditional MHRS. To realize efficiently conducted multitasks, we established an architecture with centralized task allocation center, robot alliance layer and distributed execution layer for the MHRS. The self-organizing architecture contributes to overall task allocation, communication and adaptive cooperative control between different robots. Then, to clearly describe the continuous nursing process with multiple simultaneous demands and emergency tasks, we modeled the whole nursing process with continuity, multi-priority, and interpretability. A novel MRTA algorithm with a dynamic bidding mechanism was proposed. Comprehensive experiments showed that the proposed algorithm could effectively solve the three key problems of multi-priority tasks, multi-robot safe and adaptive cooperation, and emergency task call in the scene of elderly care. The proposed architecture regarding the smart home could be applied in nursing centers, hospitals, and other places for elderly care.

Details

Language :
English
ISSN :
20751702
Volume :
10
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Machines
Publication Type :
Academic Journal
Accession number :
edsdoj.fe7ae4f42066483f9a155683ca944524
Document Type :
article
Full Text :
https://doi.org/10.3390/machines10080622