Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-8· 0 citations· 18 references
Abstract
Robotic systems operating in unstructured environments face a conflicting requirement: they must be manoeuvrable to easily navigate tight and unstructured environments, yet stiff enough to perform precise, heavy-duty tasks. Tendon-driven hyper-redundant manipulators offer a compelling solution due to their lightweight and slender designs. In this work, a novel stiffness-adjusting strategy for a tendon-driven manipulator is presented. It goes beyond complex antagonistic drives, joint-level added components, and passive high-pretension schemes. This approach utilises a single active tensioning system to modulate a global reference tension, providing on-demand stiffness adjustment on the robot. Through a systematic experimental campaign involving varying payloads and tension levels, the behaviour of a five-degree-of-freedom prototype is characterised using high-precision motion capture. The results demonstrate that this mechanically simple input can amplify tip stiffness by a factor of three to four. Furthermore, the presented compliance model validates the efficiency of the global tensioning strategy in modulating the system’s overall stiffness.
Supportive continuum robots (SCRs) enhance the load-bearing capability of an operative continuum robot by mechanically coupling it with a supportive arm. However, their passive stiffness is determined by the mechanical configuration and cannot be adjusted online for varying payloads or interaction forces. Active stiffness control is therefore needed to regulate the load response and maintain positioning accuracy. Meanwhile, the closed-chain structure introduces kinematic constraints that complicate task-space regulation and stiffness control. This paper presents an active task-space stiffness control framework for a tendon-driven SCR. An existing geometric variable strain model describes the closed-chain dynamics, which are projected onto the constraint-consistent motion subspace. A projected sliding mode controller regulates the operative arm tip while preserving the constraints, and closed-loop stability is established through Lyapunov analysis. After position regulation, active apparent stiffness is introduced through a virtual Cartesian spring based on position-error feedback to shape the force--displacement response. The framework is evaluated in simulation and experimentally validated under prescribed external loads and different desired configurations. Results show that increasing the commanded stiffness gain reduces load-induced tip deflection and increases apparent directional stiffness, thereby improving load resistance and positioning robustness under external loading.
Passive stiffness modulation is essential for compliant robotic systems in dynamic and uncertain environments, where rigid actuators or constant stiffness designs are often insufficient to ensure safety and adaptability. This paper presents the design, modeling, optimization, and experimental validation of a Load-Dependent Contact-Aided Compliant Joint (LCCJ) that passively modulates stiffness in response to external torque. An integrated design pipeline is established, which bridges a high-fidelity analytical framework with a physically-motivated optimization strategy. The framework combines a chained pseudo-rigid-body model (CPRBM) with Karush-Kuhn-Tucker (KKT) conditions to describe the complex beam-boundary interactions. Using the distinct deformation regimes of the mechanism, the pipeline employs a two-stage optimization strategy to precisely map the desired stiffness modulation back to the physical geometric parameters. Simulation results demonstrate that, across the benchmark and optimized-design validation cases, the model predictions agree closely with finite element analysis (FEA), with maximum relative errors in the torque–deformation response of 4.02% and 3.18% in the pre- and post-contact regions, respectively. Experimental validation confirms the effectiveness of the proposed design; compared to the FEA predictions, the experimental results exhibit relative errors below 2.6% in the pre-contact region and 5.22%–6.21% in the post-contact region. The LCCJ offers a compact and monolithic solution for passive stiffness modulation in compliant joint applications.
Zhenjie Shi, Yanjun Liu, Yiran Wang et al.· Journal of Mechanisms and Ro...· 0 citations
ABSTRACT Enhancing contact compliance and task flexibility is essential for expanding the real‐world use of robotic systems. This paper presents a modular collaborative robot system that combines antagonistic actuation with hybrid soft–rigid variable‐stiffness components, including a modular variable‐stiffness bending joint (mvsBJ), a modular variable‐stiffness rotational joint (mvsRJ), and a modular variable‐stiffness link (mvsL), supported by an integrated control framework. Experimental characterization shows that the mvsBJ achieves a 55.96° bending range and nearly threefold stiffness variation, from 9.16 to 24.72 Nm/rad, through fluidic pressure adjustment. The mvsRJ achieves 145° bidirectional rotation. The platform is benchmarked across repeated trials in healthcare assistance and industrial support scenarios. In an assistive feeding task, a 3‐DoF RBBL configuration operates in a low‐stiffness mode, maintaining bounded force‐tracking variation with mean error drift below 0.16 N. Collision tests across 40–60 mm/s show peak interaction forces increasing predictably from 3.93 to 5.38 N. In an industrial chamfering task, the 2‐DoF RB configuration achieves pressurized rigidity comparable to a conventional mechanical workbench. These results demonstrate plug‐and‐play reconfigurability with reliable adjustable compliance, providing a novel architecture for diverse physical human–robot collaboration applications.
Wenlong Gaozhang, Yue Li, Jia-Lei Shi et al.· Advancement of science· 0 citations
Underactuated tendon-driven hands offer compact actuation and passive compliance, but tendon elongation under restoring-spring loading introduces configuration-dependent joint deviations. This paper presents H-PAC, a modular 6-actuator, 15-DoF robotic hand with a control-oriented modeling and implementation framework. A sparse analytical actuator-joint model is derived from the tendon-routing geometry, and a mechanics-based compensation model is developed to account for tendon-elasticity-induced joint errors. The proposed method is implemented in a hierarchical architecture: a host computer performs workspace-constrained posture mapping and compensation, while an ESP32 generates synchronized commands for six position-controlled servos. The same control parameters and execution strategy are used across all tasks without task-specific retuning. Monotonic servo-sweep experiments show that the compensation substantially improves joint-angle prediction. The MAE of the index DIP joint decreases from 1.15 degrees to 0.18 degrees, and all nine evaluated joints achieve an MAE below 0.23 degrees. Representative postures and grasping configurations are further executed using the same control pipeline without external joint or force sensing in the control loop. The results demonstrate a practical approach to improving posture reproducibility in compact underactuated robotic end-effectors.
Tengfei Yan, Jiong-Xu Chen, Teng Wang et al.· 0 citations
Soft robots excel at safe and adaptive interaction but are often constrained by limited load capacity and manipulation precision. Antagonistic soft actuators offer a promising route to address these challenges by coupling actuation with variable stiffness, inspired by biological systems such as elephant trunks and octopus tentacles. However, existing control strategies largely treat stiffness as a passive mechanical property, whereas those that incorporate stiffness regulation typically rely on analytical models, limiting their deployment in soft robots with difficult-to-model dynamics. Here, we introduce a muscle coactivation-inspired, data-driven control strategy that explicitly integrates motion control with stiffness modulation, enabling precise and robust control without reliance on analytical models or system-specific tuning. The robot requires only minutes of learning from simple motions to acquire diverse functionalities, including grasping, stiffness sensing, liquid pumping, and reconfigurable bridging. By explicitly harnessing stiffness regulation, this work establishes a general and scalable control paradigm that expands the functional scope of antagonistic soft robots beyond passive compliance.
Jie Pan, Tian-Yu Chen, Jun-Wei Li et al.· Science Advances· 0 citations
Slender robots are essential for search-and-rescue operations in disaster sites. While actively articulated slender robots can achieve follow-the-leader motions suitable for confined spaces, they are difficult to miniaturize and lighten. Conversely, soft robots with passive joints offer simpler structure but often suffer from low durability and poor self-weight compensation. This study proposes a novel design approach for slender robots utilizing passive joints with preload torque. By designing a monotonic torque distribution, stable push-driven insertion is achieved through environmental contact. This mechanism enables sequential unilateral bending, where bending propagates from the tip to the base without buckling, facilitating wall-following propulsion without the need for active control. A static model was formulated to derive the necessary conditions for this motion, demonstrating that preload torque must increase monotonically from the tip to the base. Power-law and exponential distributions were found to satisfy these requirements within specific parameter ranges. Quasi-static simulations in both single- and multiple-contact environments validated these theoretical predictions. Furthermore, prototype experiments demonstrated contact-based propulsion, confirming that a power-law torque distribution capable of compensating for self-weight ensures stable performance. The proposed method reduces actuator requirements while maintaining high operational stability, offering a promising approach for exploring confined spaces.
Hiro Nakamura, M. Watanabe, K. Tadakuma et al.· IEEE Robotics and Automation...· 0 citations
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