Yinglei Zhu

Wheel-legged robots combine the advantages of both wheeled robots and legged robots, offering versatile locomotion capabilities with excellent stability on challenging terrains and high efficiency on flat surfaces. However, existing wheel-legged robots typically have limited hip joint mobility compared to humans, while hip joint plays a crucial role in locomotion. In this paper, we introduce Whleaper, a novel 10-degree-of-freedom (DOF) bipedal wheeled robot, with 3 DOFs at the hip of each leg. Its humanoid joint design enables adaptable motion in complex scenarios, ensuring stability and flexibility. This paper introduces the details of Whleaper, with a focus on innovative mechanical design, control algorithms and system implementation. Firstly, stability stems from the increased DOFs at the hip, which expand the range of possible postures and improve the robot’s foot-ground contact. Secondly, the extra DOFs also augment its mobility. During walking or sliding, more complex movements can be adopted to execute obstacle avoidance tasks. Thirdly, we utilize two control algorithms to implement multimodal motion for walking and sliding. By controlling specific DOFs of the robot, we conducted a series of simulations and practical experiments, demonstrating that a high-DOF hip joint design can effectively enhance the stability and flexibility of wheel-legged robots. Whleaper shows its capability to perform actions such as squatting, obstacle avoidance sliding, and rapid turning in real-world scenarios.

The development of high-performance bionic legged robots can benefit from the continued advancements in various actuation methods, such as artificial muscles. This work presents a musculoskeletal bionic leg driven by fluidic elastomer actuators (FEAs), showcasing their potential as artificial muscles for legged robots. Our approach integrates three key innovations: First, we established a mechanics model using thin plate theory to optimize the bellows shell structure of the FEAs, achieving high force output while maintaining inherent compliance. Second, we developed a lightweight embedded optoelectronic sensing system that enables closed-loop control without significantly increasing mass. Third, we designed a two-joint leg in the sagittal plane that utilizes a bionic configuration incorporating both monoarticular and biarticular FEAs. The leg demonstrated robust performance across various tasks including extreme positional movements, load-bearing squats supporting up to 2.45 times its body weight, vertical jumping with 147 mm ground clearance, and stable walking. Notably, our embedded sensing system successfully detected ground contact states without additional foot sensors, enabling reliable gait control while minimizing complexity and weight. The experimental results validate both the mechanical capabilities of the optimized FEAs and their controllability through embedded sensing, laying a foundation for developing full legged robots with muscle-like actuation.

Dynamic modeling and control are critical for unleashing soft robots’ potential, yet remain challenging due to their complex constitutive behaviors and real-world operating conditions. Bio-inspired musculoskeletal robots, which integrate rigid skeletons with soft actuators, combine high load-bearing capacity with inherent flexibility. Although actuation dynamics have been studied through experimental methods and surrogate models, accurate and effective modeling and simulation remain a significant challenge, especially for large-scale hybrid rigid–soft robots with continuously distributed mass, kinematic loops, and diverse motion modes. To address these challenges, we propose EquiMus, an energy-equivalent dynamic modeling framework and MuJoCo-based simulation for musculoskeletal rigid–soft hybrid robots with linear elastic actuators. The equivalence and effectiveness of the proposed approach are validated and examined through both simulations and real-world experiments on a bionic robotic leg. EquiMus further demonstrates its utility for downstream tasks, including controller design and learning-based control strategies.