A method for preventing accidents due to human action slip utilizing HMM-based Dempster-Shafer theory

Abstract

We propose a method for preventing hazardous accidents due to operator's action slip in their use of skill-assist, which has been already introduced as a power assist device in automobile assembly processes. First, we show that hidden Markov model (HMM) can be used to detect human erroneous operation from data sequences of an operator's hand motion trajectory, but that a problem arises in direct application of HMM to human error detection when trajectory is not in alignment with any of pretaught trajectories. However, HMM-based Dempster-Shafer theory proposed in the study allows the system to select their safety- or productivity- oriented robot control policy, solves the detection problem, and performs secure accident prevention, Second, a workability improvement process which comprises a state-policy and a teach data renewal sub-processes allows for optimal reconstruction of the policy determinant state space and HMMs. Finally, experimental results verify our overall proposal, and demonstrate the effectiveness of the workability improvement process.