Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks
Multi-human multi-robot (MH-MR) systems have the ability to combine the potential advantages of robotic systems with those of having humans in the loop. Robotic systems contribute precision performance and long operation on repetitive tasks without tiring, while humans in the loop improve situationa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9170619/ |
| _version_ | 1818351374216200192 |
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| author | Tamzidul Mina Shyam Sundar Kannan Wonse Jo Byung-Cheol Min |
| author_facet | Tamzidul Mina Shyam Sundar Kannan Wonse Jo Byung-Cheol Min |
| author_sort | Tamzidul Mina |
| collection | DOAJ |
| description | Multi-human multi-robot (MH-MR) systems have the ability to combine the potential advantages of robotic systems with those of having humans in the loop. Robotic systems contribute precision performance and long operation on repetitive tasks without tiring, while humans in the loop improve situational awareness and enhance decision-making abilities. A system's ability to adapt allocated workload to changing conditions and the performance of each individual (human and robot) during the mission is vital to maintaining overall system performance. Previous works from literature including market-based and optimization approaches have attempted to address the task/workload allocation problem with focus on maximizing the system output without regarding individual agent conditions, lacking in real-time processing and have mostly focused exclusively on multi-robot systems. Given the variety of possible combination of teams (autonomous robots and human-operated robots: any number of human operators operating any number of robots at a time) and the operational scale of MH-MR systems, development of a generalized framework of workload allocation has been a particularly challenging task. In this article, we present such a framework for independent homogeneous missions, capable of adaptively allocating the system workload in relation to health conditions and work performances of human-operated and autonomous robots in real-time. The framework consists of removable modular function blocks ensuring its applicability to different MH-MR scenarios. A new workload transition function block ensures smooth transition without the workload change having adverse effects on individual agents. The effectiveness and scalability of the system's workload adaptability is validated by experiments applying the proposed framework in a MH-MR patrolling scenario with changing human and robot condition, and failing robots. |
| first_indexed | 2024-12-13T18:36:43Z |
| format | Article |
| id | doaj.art-6a3d03109a8c4336b2f4ad486113d4b3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:36:43Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6a3d03109a8c4336b2f4ad486113d4b32022-12-21T23:35:21ZengIEEEIEEE Access2169-35362020-01-01815269715271210.1109/ACCESS.2020.30176599170619Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous TasksTamzidul Mina0https://orcid.org/0000-0003-4793-2959Shyam Sundar Kannan1https://orcid.org/0000-0002-8099-8434Wonse Jo2https://orcid.org/0000-0002-6904-5878Byung-Cheol Min3https://orcid.org/0000-0001-6458-4365Department of Computer and Information Technology, SMART Laboratory, Purdue University, West Lafayette, IN, USADepartment of Computer and Information Technology, SMART Laboratory, Purdue University, West Lafayette, IN, USADepartment of Computer and Information Technology, SMART Laboratory, Purdue University, West Lafayette, IN, USADepartment of Computer and Information Technology, SMART Laboratory, Purdue University, West Lafayette, IN, USAMulti-human multi-robot (MH-MR) systems have the ability to combine the potential advantages of robotic systems with those of having humans in the loop. Robotic systems contribute precision performance and long operation on repetitive tasks without tiring, while humans in the loop improve situational awareness and enhance decision-making abilities. A system's ability to adapt allocated workload to changing conditions and the performance of each individual (human and robot) during the mission is vital to maintaining overall system performance. Previous works from literature including market-based and optimization approaches have attempted to address the task/workload allocation problem with focus on maximizing the system output without regarding individual agent conditions, lacking in real-time processing and have mostly focused exclusively on multi-robot systems. Given the variety of possible combination of teams (autonomous robots and human-operated robots: any number of human operators operating any number of robots at a time) and the operational scale of MH-MR systems, development of a generalized framework of workload allocation has been a particularly challenging task. In this article, we present such a framework for independent homogeneous missions, capable of adaptively allocating the system workload in relation to health conditions and work performances of human-operated and autonomous robots in real-time. The framework consists of removable modular function blocks ensuring its applicability to different MH-MR scenarios. A new workload transition function block ensures smooth transition without the workload change having adverse effects on individual agents. The effectiveness and scalability of the system's workload adaptability is validated by experiments applying the proposed framework in a MH-MR patrolling scenario with changing human and robot condition, and failing robots.https://ieeexplore.ieee.org/document/9170619/Adaptive workload allocationagent-based systemscognitive human-robot interactionhuman-robot teammulti-robot systemsworkload transition |
| spellingShingle | Tamzidul Mina Shyam Sundar Kannan Wonse Jo Byung-Cheol Min Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks IEEE Access Adaptive workload allocation agent-based systems cognitive human-robot interaction human-robot team multi-robot systems workload transition |
| title | Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks |
| title_full | Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks |
| title_fullStr | Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks |
| title_full_unstemmed | Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks |
| title_short | Adaptive Workload Allocation for Multi-Human Multi-Robot Teams for Independent and Homogeneous Tasks |
| title_sort | adaptive workload allocation for multi human multi robot teams for independent and homogeneous tasks |
| topic | Adaptive workload allocation agent-based systems cognitive human-robot interaction human-robot team multi-robot systems workload transition |
| url | https://ieeexplore.ieee.org/document/9170619/ |
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