Human-Like Wall-Climbing Planning for Heavy Unmanned Ground Vehicles Using Driver Model and Dynamic Motion Primitives

Wall climbing represents a crucial task in a heavy unmanned ground vehicle (HUGV) working under poor road conditions. Inspired by the human perception-action behavior, this study proposes an effective human-like planning system for solving the wall-climbing problem in the HUGVs. In the proposed model, the Gaussian mixture model (GMM) and hidden semi-Markov model (HSMM) are combined to imitate the stochastic dynamic driving behavior. Based on the decision made by the GMM-HSMM-based driver model, the speed planner uses the dynamic motion primitive (DMP) to generate the expected speed curve. Furthermore, in the climbing-over process, the DMP is employed to predict the motor torque curve. This study considers a driver's perception of acceleration, which strengthens the planning system's adaptability and flexibility. The proposed method is verified by experiments with a real electric tracked HUGV. The experimental results show that the proposed system with duration constraints can maintain a reasonable acceleration rate, balancing driving safety and efficiency in the wall-climbing task. Moreover, the proposed method can adapt well to different road conditions without using a vehicle dynamics model.