Brain-computer interface for human-multirobot strategic consensus with a differential world model

In a distributed multi-robot system, the world model maintained by each robot is inconsistent due to measurement errors from onboard sensors, which will produce different and even incorrect strategies. In this paper, we propose an advanced interaction approach for human-multirobot strategic consensus. First, an opinion dynamics model is used to find the consistent multi-robot strategy, which is not necessarily the best choice due to the inaccurate world model. When the human receives the strategy from the robots, he/she can accept or reject it and reselect the strategy via a brain-computer interface (BCI). Of course, human judgment may be incorrect, and the BCI has false detections. Thus, the robots do not directly accept the human strategy but add it to the opinion dynamics model as a new node and recalculate the final consistent strategy. In addition, we developed a custom-designed simulation system based on the Robot Operating System and Gazebo to realize and evaluate the human-multirobot interaction. The extensive simulation results show that the proposed approach can significantly improve the correct rate of strategy selection compared with robot-only or human-only control, as well as the traditional human-robot interaction methods and other strategic consensus models.