Robotic Ultrasound Systems (RUSS) provide a promising solution to reduce operator dependency, alleviate physical strain, and meet the growing demand for ultrasound procedures. However, their clinical applicability remains limited by their inability to adapt to dynamic patient movements and tissue deformations during scans. This work introduces a novel framework that leverages Laplacian Trajectory Editing (LTE) for real-time adaptation of scan trajectories in response to both rigid and non-rigid patient movements. it integrates a RGB-D camera to capture surface point clouds, which are processed to estimate displacements between consecutive frames. These displacements define anchor points for LTE-based trajectory adaptations, ensuring smooth motion while preserving local trajectory properties. This approach is validated through experiments spanning rigid phantom movements, generalization across differently shaped phantoms, and non-rigid human arm motion. Adaptation accuracy is quantified by comparing adapted trajectories to a ground-truth reference, with root mean squared errors averaging 0.026 0.012 m in non-rigid scenarios. Real-time trajectory adaptation is achieved, with an average LTE adaptation processing time of 373 ms per trial. Furthermore, our implementation achieved low tracking errors across all conditions while maintaining a high success rate in diverse movement scenarios. These results demonstrate the feasibility of LTE for real-time trajectory adaptation in ultrasound scanning, offering a pathway to more autonomous and clinically viable RUSS implementations.

In this letter, we investigate whether classical function allocation—the principle of assigning tasks to either a human or a machine—holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. This study empirically tests the applicability of Fitts' List within physical Human-Robot Collaboration, by conducting a user study (N=26, within-subject design) to evaluate four distinct allocations of position/force control between human and robot in an abstract blending task. We hypothesize that the function in which humans control the position achieves better performance and receives higher user ratings. When allocating position control to the human and force control to the robot, compared to the opposite case, we observed a significant improvement in preventing overblending. This was also perceived better in terms of physical demand and overall system acceptance, while participants experienced greater autonomy, more engagement and less frustration. An interesting insight was that the supervisory role (when the robot controls both position and force) was rated second best in terms of subjective acceptance. Another surprising insight was that if position control was delegated to the robot, the participants perceived much lower autonomy than when the force control was delegated to the robot. These findings empirically support applying Fitts' principles to static function allocation for physical collaboration, while also revealing important nuanced user experience trade-offs, particularly regarding perceived autonomy when delegating position control.

This paper presents a multi-modal dynamic workspace re-indexing method for addressing operator ergonomics and workspace limitations. The proposed method has two interactive modes: pose-to-pose mode, which is active when the operator is within an ergonomic workspace of comfortable arm postures, and ergonomic workspace drift mode, which activates after the operator makes an excursion beyond the boundaries of the ergonomic workspace when trying to reach more distant targets with the remote robot. In the ergonomic workspace drift mode, the operator temporarily stays slightly outside these boundaries, while the offset between the local and remote workspace drifts with a velocity proportional to the excursion distance. This dynamically re-indexes the remote workspace toward the distant target, and the operator can remain in a comfortable posture while the remote robot moves toward the intended target where the task is. To construct the ergonomic workspace, we employed the Rapid Upper Limb Assessment method. To validate the proposed method, we conducted experiments on a teleoperation setup involving a Force Dimension Sigma7 haptic device controlling a Kuka LBR iiwa robotic arm. The results show that the proposed controller successfully addresses workspace limitations by dynamically reindexing the follower's workspace towards target objects, while maintaining good operator ergonomics.

Dit artikel beschrijft de vorderingen in het Brightsky-project, waarin de potentie voor robotondersteuning wordt onderzocht, met en voor vakmensen bij KLM Engine Services die daar reparatiewerk uitvoeren. Door de samenwerking met vakmensen centraal te stellen, wordt er onderzocht hoe robotondersteuning niet alleen fysiek werk kan verlichten, maar ook kan bijdragen aan een betekenisvolle werkervaring. Hiermee wordt gepoogd een brug te slaan tussen de focus van human factors zoals we die kennen als discipline, en een meer holistische benadering die diepe kennis van vakmensen, innovatie-experts, robotici, ontwerpers, psychologen en organisatiewetenschappers aanwendt.

In this paper, we propose a closed-loop force sensor based nested admittance/impedance control strategy to actively estimate and minimize the effects of geometric misalignment that naturally occur during assembly tasks with compliant robots. The method allows the robot to be used with a stiff impedance control setting, which is beneficial for free air motion performance, yet allows to adjust for large misalignment errors between parts that need be assembled.