Shared Control Based on a Brain-Computer Interface for Human-Multirobot Cooperation
Wei Dai, Yaru Liu, Huimin Lu, Zongtan Zhou, Zhiqiang Zhen
Abstract
Currently, distributed multi-robot systems (MRSs) can meet the requirements of various tasks in complex environments. Nevertheless, the inevitable disadvantages of robot sensor errors, communication delays, and obstructive environmental factors hinder the operation of MRSs. Therefore, a shared control approach supported by human intention has emerged, relying on human experience and knowledge to improve cooperation. With firefighting tasks as the application background, this letter considers a brain-computer interface as the means of input for human intention and proposes a layered shared control framework suitable for human-multirobot cooperation. In the upper layer of the framework, an intention field model is used to construct the shared intention of the human and robots, and in the lower layer, a policy-blending model is employed to fuse the various velocity components for shared intention, formation control, and obstacle avoidance. Ultimately, the proposed human-multirobot shared control framework effectively avoids the inherent disadvantages of a single control source (either the human or robots), enabling the robot team to efficiently, safely and flexibly complete all tasks. In addition, we have designed and performed both simulated and practical experiments to fully illustrate the effectiveness and operability of the proposed shared control framework.