Turning behavior modeling for the heading control of an articulated micro-tunneling robot

The performance of a microtunneling robot to be driven along a projected line is strongly dependent on the skill of the operator. The heading control of the driving machine is the key to achieving an autonomous navigation system. This paper presents a design scheme for models to describe driving machine turning behavior of an articulated micro-tunneling robot. Two types of model are introduced based on the offline analysis of a driving data obtained at an actual construction site: a model with a head motion constraint and a model with a rear end motion constraint. Simulation results are provided, in which a Kalman filter is applied to the models to verify the online state estimation performance obtained using the driving data, and the two models are compared in terms of their estimation and prediction errors.