Search for: All records

Abstract This paper presents the design and validation of a wearable shoulder exoskeleton robot intended to serve as a platform for assistive controllers that can mitigate the risk of musculoskeletal disorders seen in workers. The design features a four-bar mechanism that moves the exoskeleton’s center of mass from the upper shoulders to the user’s torso, dual-purpose gravity compensation mechanism located inside the four-bar’s linkages that supports the full gravitational loading from the exoskeleton with partial user’s arm weight compensation, and a novel 6 degree-of-freedom (DoF) compliant misalignment compensation mechanism located between the end effector and the user’s arm to allow shoulder translation while maintaining control of the arm’s direction. Simulations show the four-bar design lowers the center of mass by$$ 11 $$ cm and the kinematic chain can follow the motion of common upper arm trajectories. Experimental tests show the gravity compensation mechanism compensates gravitational loading within$$ \pm 0.5 $$ Nm over the range of shoulder motion and the misalignment compensation mechanism has the desired 6 DoF stiffness characteristics and range of motion to adjust for shoulder center translation. Finally, a workspace admittance controller was implemented and evaluated showing the system is capable of accurately reproducing simulated impedance behavior with transparent low-impedance human operation. 

This paper presents a biomechanics‐based, user‐adaptive variable impedance controller designed to enhance the performance of coupled human–robot systems during motion. The controller integrates the biomechanical characteristics of human limbs and dynamically adjusts the robotic impedance parameters—specifically damping, stiffness, and equilibrium trajectory—based on real‐time estimations of the user's intent and direction of motion. The primary goal is to minimize the energy expenditure of the coupled human–robot system while maintaining system passivity. To address uncertainties in human behavior and noisy observations, the controller employs Bayesian optimization combined with a Gaussian process. To validate the proposed approach, human experiments are conducted using a standard robotic arm manipulator. The results demonstrate that the controller eliminates the need for manual parameter tuning, a process that is typically time‐consuming. A comparative analysis against two variable impedance controllers without user‐adaptive parameter adjustments reveal significant benefits, with the controller improving combined performance metrics—such as accuracy, speed, user effort, and smoothness—by over 13%. Notably, all participants in the study preferred the optimized controller over the alternatives. These findings highlight the effectiveness of the biomechanics‐based, user‐adaptive variable impedance control approach and its potential to enhance physical human–robot interaction in various applications that involve repetitive or continuous motion.