Jun 2026· Industrial robot· 0 citations· 21 references
Abstract
Traditional neck-shoulder rehabilitation devices exhibit limited interaction fidelity due to rigid mechanical structures and imprecise multi-axis synchronization when interfacing with nonlinear human soft tissue. This study aims to develop a dual-motor neck-shoulder massage robot featuring kinematic optimization and a hierarchical adaptive control architecture to achieve compliant force tracking and high-precision motion coordination.
A crank-rocker mechanism, synthesized via kinematic optimization, generates biomimetic trajectories, complemented by a scissor-lift module for active depth adjustment. Within the control framework, an extended Kalman filter fuses multi-modal sensor data for real-time contact state estimation. An incremental Fuzzy PID controller accommodates the nonlinear stiffness of muscle tissue to ensure active compliance. Concurrently, a Robust Adaptive Cross-Coupling Synchronization (RACCS) algorithm regulates dual-motor coordination under variable loads.
Validation with 45 subjects demonstrates a 30% tracking error reduction compared to open-loop baselines on the same hardware. Compliant force control precision is maintained within a 0.2 N margin, yielding over 90% target area coverage across the cervical and shoulder regions. Stability analysis confirms the robustness of the RACCS algorithm against heterogeneous load disturbances.
This study contributes a kinematically optimized crank rocker and scissor lift mechanism together with a hierarchical adaptive control architecture for distributed dual motor systems. The integrated design manages nonlinear soft tissue impedance and offers a scalable platform for precise cervical fatigue relief in healthy subjects.
This paper presents design, modeling, and experimental validation of a self-reconfigurable parallel ankle rehabilitation platform. The proposed system employs a three-limb parallel mechanism with mechanically reconfigurable joints, enabling seamless transition between static mobilization and dynamic gait-oriented operation while preserving alignment with the anatomical ankle joint center. A position-level inverse kinematics formulation is developed to directly compute actuator lengths from desired platform orientation, eliminating drift associated with velocity-integration approaches. The corresponding Jacobian matrix is derived to describe the velocity relationship and is evaluated over the rehabilitation workspace to ensure nonsingular operation. A closed-loop control framework based on Jacobian mapping and Lyapunov-guided design is implemented for stable motion tracking. Simulation results demonstrate accurate tracking of multi-axis trajectories with limited cross-axis coupling. Experimental validation on a physical prototype confirms the approach’s feasibility, demonstrating stable trajectory tracking and setpoint regulation under practical conditions, with minor deviations attributable to actuator and mechanical nonlinearities. The results highlight the potential of the proposed self-reconfigurable parallel architecture for anatomically consistent, adaptive ankle rehabilitation in both clinical and home-based settings.
Abdulaziz Alrais, Kun Wang, Jian S. Dai et al.· Design for Augmented Humanit...· 0 citations
Achieving safe physical interaction on the human back is challenging due to respiratory rhythms, complex topography, and varying tissue stiffness. To enable compliant force tracking within commercial closed position-control robot architectures, this paper presents an adaptive variable damping admittance control framework driven by multi-dimensional force sensor feedback. A stiffness-free admittance model is constructed to eliminate steady-state tracking errors, integrated with a nonlinear adaptive damping law that sensitively responds to real-time force sensor measurements. This mechanism rapidly dissipates dynamic impact energy during contacts while maintaining low impedance during steady state. Validated via a high-fidelity MATLAB R2024b-CoppeliaSim co-simulation platform replicating Traditional Chinese Medicine (TCM) manipulations, the proposed sensor-driven strategy significantly improves force tracking fidelity over traditional fixed-parameter control. Quantitative results demonstrate that across all complex therapeutic waveforms, the root mean square error (RMSE) remains below 0.42 N, the mean absolute error (MAE) is within 0.32 N, and the squared correlation coefficient (r2) exceeds 0.97. These findings confirm the high efficiency and clinical potential of the proposed framework.
Jingbo Xu, Chong Ren, Xiangjie Kong et al.· Italian National Conference...· 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, Jiongxu Chen, Teng Wang et al.· 0 citations
Robots working in unstructured or partially unobservable environments must combine accurate motion with physical compliance that can passively correct contact misalignment. Soft robots provide this compliance but have struggled to precisely control their tip compliance and position. This paper presents a robot architecture designed around that control problem: a 7-link arm whose six articulated joints provide twelve independently driven revolute axes, each actuated by an antagonistic pair of pneumatic muscles, so that every axis can simultaneously change its angle and linearly adjust its stiffness. The rigid articulated backbone makes the tip compliance and position of the arm predictable enough to be commanded quantitatively in real time. The robot employs a unified iterative inverse-kinematics and inverse-compliance controller to achieve simultaneous, quantitative control of both compliance and position. The task-space compliance and kinematics models and the control law are derived and verified on both the physical arm and a matched simulation. Simulation is then used to study how the same framework extends to other arm morphologies. Finally, the arm demonstrates tasks that have been difficult for both rigid and soft arms: rejecting disturbances while writing on a moving whiteboard, and passively correcting hidden misalignment during a key-insertion and drawer-opening task. That these tasks succeed under so straightforward a controller is evidence for the advantage of this algorithm-informed structural design.
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
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.