A Shared Control Framework for Human-Multirobot Foraging With Brain-Computer Interface

• Published in: IEEE robotics and automation letters Vol. 6; no. 4; pp. 6305 - 6312
• Main Authors: Dai, Wei, Liu, Yaru, Lu, Huimin, Zheng, Zhiqiang, Zhou, Zongtan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain-computer interfaces; brain-machine interfaces; Collaboration; Drones; Human engineering; Human performance; Human-computer interface; human-robot collaboration; Input devices; Multi-robot systems; Multiple robots; Robot sensing systems; Robots; Task analysis
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3092290

With the rapid development of multi-robot systems (MRSs), they can be widely used to perform various tasks in typical environments. However, the inevitable disadvantages of onboard sensor errors, communication delays, and underspecified environmental factors seriously affect the operation of MRSs. Therefore, this letter considers a shared control framework suitable for human-multirobot foraging with a brain-computer interface (BCI) as a means of allowing a human operator to express opinions, permitting the robots to rely on human experience and knowledge to improve cooperation. An opinion dynamics model is used to find the consensus opinion of the MRS, which, however, is likely not accurate due to the biased nature of the available environmental information. When the human operator learns the opinion of the robots, he/she can then either accept it or reject it and express his/her own opinion via the BCI. Of course, this human judgment may also be incorrect, or the BCI may suffer from false detections. Thus, the MRS does not directly follow the human operator's opinion; instead, it is added to the opinion dynamics model as a new node to generate the final consensus opinion. Extensive simulation results show that the proposed framework can markedly improve the efficiency of foraging compared with robot-only or human-only performance and traditional human-robot interaction methods.