Bayesian Optimization based Dempster-Shafer fusion for brain-robot cooperation to navigate a mobile robot

One critical problem in brain-robot cooperation is to efficiently fuse human and machine to enhance the overall performance. In this paper, a Bayesian optimization based Dempster-Shafer (BO-DS) method was designed to integrate decisions from both human brain and machine. Decisions on every step of robot’s movement were made by a brain-computer interface (BCI) model and an intelligent machine (IM) model. The BO-DS estimated basic probability assignments (BPAs) of the two models and assigned exponential weights to them. The optimal weights were selected using a Bayesian optimization approach. The BO-DS fused the weighted BPAs based on D-S theory to make the final decision. The experimental results showed that compared with only using the BCI model (84.9 %), the proposed BO-DS achieved higher average accuracy of 89.01 % in offline validation.