Motion Predicting of Autonomous Tracked Vehicles with Online Slip Model Identification

Abstract

Precise understanding of the mobility is essential for high performance autonomous tracked vehicles in challenging circumstances, though the complex track/terrain interaction is difficult to model. A slip model based on the instantaneous centers of rotation (ICRs) of treads is presented and identified to predict the motion of the vehicle in a short term. Unlike many research studies estimating current ICRs locations using velocity measurements for feedback controllers, we focus on predicting the forward trajectories by estimating ICRs locations using position measurements. ICRs locations are parameterized over both tracks rolling speeds and the kinematic parameters are estimated in real time using an extended Kalman filter (EKF) without requiring prior knowledge of terrain parameters. Simulation results verify that the proposed algorithm performs better than the traditional method when the pose measuring frequencies are low. Experiments are conducted on a tracked vehicle with a weight of 13.6 tons. Results demonstrate that the predicted position and heading errors are reduced by about 75% and the reduction of pose errors is over 24% in the absence of the real-time kinematic global positioning system (RTK GPS).