Synergistic Terrain-Adaptive Morphing and Trajectory Tracking in a Transformable-Wheeled Robot

Abstract

Transformable-wheeled robots exhibit efficient locomotion and obstacle negotiation through mode transformation, which underpins the development of the multimodal robot MTABot—a previously validated platform. However, existing literature primarily focuses on structural design, leaving autonomous mode transitions across varying terrains as a significant challenge. This paper presents a unified terrain-adaptive morphing and trajectory tracking approach for MTABot, utilizing the Nonlinear Model Predictive Control (NMPC) framework. This method eliminates the need for environmental recognition or prior training. Specifically, a segmented kinematic model for the transformable wheel has been developed, ensuring the feasibility of motion in both rolling and climbing modes. Additionally, a virtual ground attachment constraint is proposed to guide adaptive morphing for overcoming single or small obstacles. An online weight adjustment method for NMPC is introduced to synchronize wheel motion and overcome continuous large obstacles. Comprehensive experiments in multi-terrain composite scenarios and various obstacle-crossing tests validated the effectiveness of the proposed approach.