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Item type:Publication, Access status: with files , A digital twin for robotic post mortem tissue sampling using virtual reality(IEEE, 2025-09-10); ; ; ; Studying tissue samples obtained during autopsies is the gold standard when diagnosing the cause of death and for understanding disease pathophysiology. Recently, the interest in post mortem minimally invasive biopsies has grown which is a less destructive approach in comparison to an open autopsy and reduces the risk of infection. While manual biopsies under ultrasound guidance are more widely performed, robotic post mortem biopsies have been recently proposed. This approach can further reduce the risk of infection for physicians. However, planning of the procedure and control of the robot need to be efficient and usable. We explore a virtual reality setup with a digital twin to realize fully remote planning and control of robotic post mortem biopsies. The setup is evaluated with forensic pathologists in a usability study for three interaction methods. Furthermore, we evaluate clinical feasibility and evaluate the system with three human cadavers. Overall, 132 needle insertions were performed with an off-axis needle placement error of 5.30±3.25 mm. Tissue samples were successfully biopsied and histopathologically verified. Users reported a very intuitive needle placement approach, indicating that the system is a promising, precise, and low-risk alternative to conventional approaches.Publicationtype: Journal Article46 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection With Sparse LiDAR and Large Domain GapsIn this study, we address a gap in existing unsupervised domain adaptation approaches on LiDAR-based 3D object detection, which have predominantly concentrated on adapting between established, high-density autonomous driving datasets. We focus on sparser point clouds, capturing scenarios from different perspectives: not just from vehicles on the road but also from mobile robots on sidewalks, which encounter significantly different environmental conditions and sensor configurations. We introduce Unsupervised Adversarial Domain Adaptation for 3D Object Detection (UADA3D). UADA3D does not depend on pre-trained source models or teacher-student architectures. Instead, it uses an adversarial approach to directly learn domain-invariant features. We demonstrate its efficacy in various adaptation scenarios, showing significant improvements in both self-driving car and mobile robot domains.Publicationtype: Journal Article23 43 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Toward a robust sensor fusion step for 3D object detection on corrupted dataMultimodal sensor fusion methods for 3D object detection have been revolutionizing the autonomous driving research field. Nevertheless, most of these methods heavily rely on dense LiDAR data and accurately calibrated sensors which is often not the case in real-world scenarios. Data from LiDAR and cameras often come misaligned due to the miscalibration, decalibration, or different frequencies of the sensors. Additionally, some parts of the LiDAR data may be occluded and parts of the data may be missing due to hardware malfunction or weather conditions. This work presents a novel fusion step that addresses data corruptions and makes sensor fusion for 3D object detection more robust. Through extensive experiments, we demonstrate that our method performs on par with state-of-the-art approaches on normal data and outperforms them on misaligned data.Publicationtype: Journal Article23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A lightweight simulation model for soft robot's locomotion and its application to trajectory optimizationThis letter extends the piecewise constant curvature modeling approach by energy minimization techniques in order to simulate contact with the environment. The new method enables a fast and sufficiently good estimation of the forward kinematics for soft structures whose behavior is mainly determined by boundary conditions. This enables to predict the configuration of such structures for a given reference input, which is demonstrated on a gecko-inspired soft robot. The newly gained simulation capability is then used to find new patterns for straight as well as curved trotting gait. The existing gait pattern for straight gait could be improved by a factor of 1.4, and a new gait pattern for the rotation on the spot could be discovered.Publicationtype: Journal Article77 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Design and adaptive depth control of a micro diving agent(IEEE, 2017-06-09); ; ; ; This letter presents the depth control of an autonomous micro diving agent called autonomous diving agent (ADA). ADA consists of off-The-shelf components and features open-source hardware and firmware. It can be deployed as a testbed for depth controllers, as well as a mobile sensor platform for research or in industrial tanks.We introduce a control law that is based on the feedback linearization method and enhanced by an adaptive fuzzy algorithm to cope with modeling inaccuracies. The proposed depth controller is computationally light enough to run on ADAs embedded hardware. In experiments performed in a wave tank, the adaptive fuzzy scheme shows the ability to deal with both depth regulation and depth profile tracking. ADA is even able to hold on to dynamic isobars despite external disturbances. We demonstrate that under the influence of waves, ADA describes orbital motions similar to water particles.Publicationtype: Journal Article114