Human–Robot Task Allocation and Scheduling: Boeing 777 Case Study

2021 ◽  
Vol 6 (2) ◽  
pp. 1256-1263
Author(s):  
Andy Ham ◽  
Myoung-Ju Park
Keyword(s):  
Task Allocation ◽  
Task Allocation And Scheduling ◽  
Robot Task

Multi-robot task allocation for exploration

2006 ◽  
Vol 13 (5) ◽  
pp. 548-551 ◽  
Author(s):  
Ping-an Gao ◽  
Zi-xing Cai
Keyword(s):  
Task Allocation ◽  
Robot Task ◽  
Multi Robot

Distributed Hedonic Coalition Formation for Multi-Robot Task Allocation

2021 ◽  
Author(s):  
Ayan Dutta ◽  
Vladimir Ufimtsev ◽  
Tuffa Said ◽  
Inmo Jang ◽  
Roger Eggen
Keyword(s):  
Coalition Formation ◽  
Task Allocation ◽  
Robot Task ◽  
Hedonic Coalition Formation ◽  
Multi Robot

A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments

2021 ◽  
Author(s):  
Ching-Wei Chuang ◽  
Harry H. Cheng
Keyword(s):  
Bayesian Networks ◽  
Task Allocation ◽  
Low Cost ◽  
Dynamic Environments ◽  
Modern World ◽  
Robot System ◽  
Novel Approach ◽  
Robot Systems ◽  
Robot Task ◽  
Multi Robot

Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed various algorithms or approaches to solve MRTA problems in different multi-robot systems, it is still difficult to overcome certain challenges, such as dynamic environments, changeable task information, miscellaneous robot abilities, the dynamic condition of a robot, or uncertainties from sensors or actuators. In this paper, we propose a novel approach to handle MRTA problems with Bayesian Networks (BNs) under these challenging circumstances. Our experiments exhibit that the proposed approach may effectively solve real problems in a search-and-rescue mission in centralized, decentralized, and distributed multi-robot systems with real, low-cost robots in dynamic environments. In the future, we will demonstrate that our approach is trainable and can be utilized in a large-scale, complicated environment. Researchers might be able to apply our approach to other applications to explore its extensibility.

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