Concept and Strategies: Equivalent Predictive Control and Handle Point Control for Bipedal-Vehicle Transformable Robots Under Various Disturbances

Bipedal-vehicle transformable robots (BVTRs), equipped with driving wheels, combine the flexibility of bipedal locomotion with the speed of wheeled movement. However, maintaining balance across different formations under various external disturbances remains a significant challenge due to uncertain disturbance types and dynamic shifts between formations. To address these challenges, this paper introduces the concept of Equivalent Predictive Control (EPC), which models all disturbances as unified virtual wrenches and integrates them directly into the robot’s predictive control model, treated as an inertia-varying single rigid body. By anticipating the future impact of disturbances, EPC enhances stability and enables simultaneous handling of various disturbances. To address the challenge of dynamic changes, contact variations, and shifting constraints during formation transitions, we propose Handle Point Control (HPC). HPC simplifies multi-task tracking by reducing joint space control to a set of virtual target points, called ‘handle points’, such as knees, hips, and shoulders. This method facilitates real-time formation switching by tracking different handle points. Experiments on the BVTR platform BHR8-2 validate the effectiveness of the proposed control strategies. Note to Practitioners—This paper addresses two critical challenges for applying BVTRs in real-world industrial scenarios: 1) managing various external disturbances, and 2) overcoming the complexities associated with changing dynamics and contact situations during formation transitions. The proposed EPC strategy unifies all disturbance types and integrates them into the robot’s dynamic model, enhancing stability and adaptability across formations. The HPC method simplifies control by focusing on tracking key handle points, allowing smooth, real-time formation transitions without needing multiple optimization schemes. These methods can also be applied to other robotic systems that face similar control challenges.