Bridging the Gap to Bionic Motion: Challenges in Legged Robot Limb Unit Design, Modeling, and Control

Abstract

Motivated by the agility of animal and human locomotion, highly dynamic bionic legged robots have been extensively applied across various domains. Legged robotics represents a multidisciplinary field that integrates manufacturing, materials science, electronics, and biology, and other disciplines. Among its core subsystems, the lower limbs are particularly critical, necessitating the integration of structural optimization, advanced modeling techniques, and sophisticated control strategies to fully exploit robots' dynamic performance potential. This paper presents a comprehensive review of recent developments in the structural design of single-legged robots and systematically summarizes prevailing modeling approaches and control strategies. Key challenges and potential future directions are also discussed, serving as a reference for the future application of state-of-the-art manufacturing and control methodologies in legged robotic systems.

Fig. 1.

Application scenarios of legged robots in diverse environments.

Fig. 2.

Categorization and application domains of telescopic SLRs .

Fig. 3.

Categorization and application domains of articulated SLRs.

Fig. 4.

Characteristics of 4 types of articulated SLR.

Fig. 5.

Overview of modeling approaches for SLRs: (1a) 1-DoF SLIP model, (1b) A-SLIP model, (1c) three-mass SLIP model, (1d) D-SLIP model, (1e) active-SLIP model, (1f) MD-SLIP model, (1g) U-SLIP model, (1h) R-SLIP model, (1i) W-SLIP model, (1j) W-LS-LIP model, (1k) Seipel’s SLIP model, and (1l) 3D-SLIP model; (2a) Berkemeier’s model, (2b) Roozing’s model, (2c) VDC model, (2d) closed-chain VDC model, (2e) SLSK model, (2f) AKF model, (2g) U-articulated model, (2h) He’s model, and (2i) Chen’s model.

Fig. 6.

Equivalent modeling process of SLRs [151].

Fig. 7.

Overview of model-based and model-free control strategies for SLRs.

Fig. 8.

Decoupled SLIP control strategy frameworks. (A) HFC method. (B) Obstacle-clearing SLIP control method. (C) Extended SLIP control method. (D) 3D-HFC method.

Fig. 9.

Zero moment point control strategy frameworks. (A) ZMP-based global dynamic balance control method. (B) QP optimization control method.

Fig. 10.

Virtual model control strategy frameworks. (A) MARCO-Hopper II control method. (B) Hopping virtual model control method. (C) Parameter adaptive VMC method. (D) Adaptive learning algorithm VMC method.

Fig. 11.

Model predictive control strategy frameworks. (A) Energy-efficient hydraulic pump MPC method. (B) Learning-based MPC energy management method.

Fig. 12.

Reinforcement learning control strategy frameworks. (A) End-to-end RL-based continuous jumping control method. (B) Guided reinforcement learning control method. (C) Four RL algorithms control method. (D) Hybrid DDPG control method.

Fig. 13.

Future research directions and challenges for SLRs.