期刊
2020 AMERICAN CONTROL CONFERENCE (ACC)
卷 -, 期 -, 页码 30-35出版社
IEEE
DOI: 10.23919/acc45564.2020.9147782
关键词
-
资金
- ONR [N0001419-1-2556, N00014-19-1-2266, N00014-16-1-2667]
- NSF [OCE-1559475, CNS-1828678, SAS-1849228]
- NRL [N00173-17-1-G001, N00173-19-P-1412]
- NOAA [NA16NOS0120028]
We analyze a human and multi-robot collaboration system and propose a method to optimally schedule the human attention when a human operator receives collaboration requests from multiple robots at the same time. We formulate the human attention scheduling problem as a binary optimization problem which aims to maximize the overall performance among all the robots, under the constraint that a human has limited attention capacity. We first present the optimal schedule for the human to determine when to collaborate with a robot if there is no contention occurring among robots' collaboration requests. For the moments when contentions occur, we present a contention-resolving Model Predictive Control (MPC) method to dynamically schedule the human attention and determine which robot the human should collaborate with first. The optimal schedule can then be determined using a sampling based approach. The effectiveness of the proposed method is validated through simulation that shows improvements.
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