This study presents a simulator framework to replicate joint dislocation that can be used to evaluate the contributions of various joint stabilizers. Compared to conventional axial joint configurations, biologically inspired open joints allow for a large range of motion with fewer rotational components and no singularities. Despite these advantages, open joints have few practical applications due to joint dislocation. To facilitate the development of bio-inspired open joints, our study evaluates the performance of biomimetic joint stabilizers involving (1) the muscle actuators as the active stabilizer, and (2) the skeletal socket and the ligaments as the passive stabilizers. Those stabilizers exert appropriate constraint forces during joint motion to prevent dislocation. The convex decomposition of the skeletal socket enabled the simulation of the dislocation behavior. The kinematic simulation determines the ligaments' maximum length depending on the desired joint motion. The designed ligaments were suggested to prevent excessive dislocation in the simulation even when the motor output was off. The comparative simulation analysis also indicated that socket depth, active stabilizer, and passive stabilizer had different contributions for dislocation prevention and recovery. Future work includes the experimental verification of the proposed open hip joint architecture.
While snake robots offer exceptional potential for exploration in confined and unstructured environments, their capacity for active adaptation is currently hindered by a lack of robust terrain classification. Inspired by the mechanosensory perception of biological snakes, this study proposes a proprioceptive terrain classification framework relying solely on a single uniaxial accelerometer. To capture the coupled robot-terrain interaction dynamics from the complex, non-stationary signals inherent in floating-base locomotion, we introduce the Band-Limited Spectral Magnitude (BLSM) feature, which quantifies vibratory energy within specific frequency bands. The framework was validated across four canonical terrains (concrete, gravel, grass, and sand) using two distinct locomotion modes with varying physical impact mechanisms: vertical undulation and sidewinding. However, because sidewinding generates dominant shear forces with diminished normal impacts, it introduces classification ambiguity on yielding, low-stiffness terrains. To resolve this, we implemented a hierarchical classification strategy that first categorizes bulk terrain stiffness before finely differentiating terrains within the low-stiffness group. This approach achieved classification accuracies exceeding 98.8\% for vertical undulation and up to 92.5\% for sidewinding. Notably, data-driven optimization reveals that the most effective frequency bands for terrain identification closely align with the peak sensitivity range of biological snake mechanoreceptors. By ensuring high reliability through a single scalar feature, this work establishes a practical, computationally efficient baseline for real-time, terrain-adaptive locomotion control in field applications.
Underwater soft robots (USRs) operate in highly dynamic, unstructured, and sensor-limited environments where fluid-structure interaction, compliance, and nonlinear actuation significantly complicate closed-loop control compared with both rigid underwater robots and terrestrial soft robots. As a result, classical underwater control techniques and conventional soft robot control approaches require substantial adaptation to address challenges such as uncertain hydrodynamics, limited onboard sensing, energy constraints, and slow actuation dynamics. This review provides a focused and critical synthesis of recent control strategies developed specifically for USRs. Literature published between 2019 and 2026 (till April) was systematically screened and analyzed, and dominant approaches were categorized into data-driven methods (reinforcement learning and neural networks), model-based methods (sliding mode control, model predictive control and proportional-integral-derivative), and hybrid strategies. For each category we compare representative controllers in terms of actuator modality, control objectives and operational environment. Key limitations are highlighted including model inaccuracy, sensing constraints and scalability, followed by prioritized research directions such as controller actuator co design, data efficient learning and robust control under severe environmental uncertainty. By offering a decision-oriented perspective rather than a cumulative survey, this review aims to guide the selection and development of control strategies tailored to the unique requirements of USRs.
Achieving human-like compliant manipulation remains a fundamental challenge in robot hands due to the difficulty of realizing biomechanical compatibility, compliant interaction, and dexterous operation. To address this, we propose a human-compatible anthropomorphic dexterous hand inspired by the anatomical structure and coordinated motion of the human hand. The design integrates anatomical morphology, joint coordination, and compliant manipulation through a rigid-soft synergistic architecture with a tendon-driven mechanism. Flexible thermoplastic polyurethane elastomers and cables emulate human ligaments and tendons, while rigid linkage mechanisms reproduce the skeletal support and motion transmission functions of the phalanges. A hierarchical modular architecture with separated and nested skeleton-shell structures enables compact integration of the palm and fingers. The proposed hand possesses 16 degrees of freedom (DOFs), including a biomimetic 4-DOF thumb that reproduces thumb opposition and coordinated manipulation. Synchronous coupled control of the interphalangeal and metacarpophalangeal joints enables independent control of 7 DOFs. Kinematic simulations and finite element analysis verify the motion feasibility and structural reliability of the design. Furthermore, a human-inspired neural control strategy is introduced to achieve compliant motion control and adaptive regulation, enhancing compliant interaction and human-machine compatibility. Prototype experiments demonstrate stable grasping performance, compliant interaction, and effective manipulation across tasks of varying complexity and precision. This work establishes an integrated biomechanical and neural-inspired design framework for anthropomorphic dexterous hands, providing a promising approach for human-compatible compliant manipulation and next-generation robotic hands.
Manta rays exhibit highly agile and complex swimming patterns through undulatory pectoral fin flapping, inspiring the development of efficient and controllable swimming robots. However, integrating high propulsion efficiency,maneuverability, and untethered operation into a single robotic platform remains a grand challenge. Here, we propose a bioinspired bistable flapping soft swimming robot that mimics manta ray motion to address this issue. The flapping wings employ pre- stretched elastomeric matrix composites (EMC) to achieve bistable shapes, enabling reconfigurable mono/bistable transitions and multimodal swimming behaviors through simple frequency modulation of fluidic actuation. An analytical model is employed to conduct a sensitivity analysis, identifying key parameters that influence the bistability and shapes of the actuators. Computational Fluid Dynamics (CFD) simulations show a peak instantaneous thrust of up to 17.2 N. Experimental parametric studies are performed with dynamic tests to assess the impact of critical parameters on swimmer performance and to optimize the robot's design. An untethered swimming robot is integrated with a microfluidic pressure modulation control system for remote operation and multi-modal manoeuvrability. By allowing real-time modulation of swimming modes, the robot demonstrates the ability to navigate complex trajectories and avoid obstacles in unstructured aquatic environments.
