Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2602.15309

OSCAR: An Ovipositor-Inspired Self-Propelling Capsule Robot for Colonoscopy

Mostafa A. Atalla·Anand S. Sekar·Remi van Starkenburg·David J. Jager·Aimée Sakes·Michaël Wiertlewski·Paul Breedveld
View original source ↗Read source document ↗Export metadata ↓

Abstract

Self-propelling robotic capsules eliminate shaft looping of conventional colonoscopy, reducing patient discomfort. However, reliably moving within the slippery, viscoelastic environment of the colon remains a significant challenge. We present OSCAR, an ovipositor-inspired self-propelling capsule robot that translates the transport strategy of parasitic wasps into a propulsion mechanism for colonoscopy. OSCAR mechanically encodes the ovipositor-inspired motion pattern through a spring-loaded cam system that drives twelve circumferential sliders in a coordinated, phase-shifted sequence. By tuning the motion profile to maximize the retract phase relative to the advance phase, the capsule creates a controlled friction anisotropy at the interface that generates net forward thrust. We developed an analytical model incorporating a Kelvin-Voigt formulation to capture the viscoelastic stick--slip interactions between the sliders and the tissue, linking the asymmetry between advance and retract phase durations to mean thrust, and slider-reversal synchronization to thrust stability. Comprehensive force characterization experiments in ex-vivo porcine colon revealed a mean steady-state traction force of 0.85 N, closely matching the model. Furthermore, experiments confirmed that thrust generation is speed-independent and scales linearly with the phase asymmetry, in agreement with theoretical predictions, underscoring the capsule's predictable performance and scalability. In locomotion validation experiments, OSCAR demonstrated robust performance, achieving an average speed of 3.08 mm/s, a velocity sufficient to match the cecal intubation times of conventional colonoscopy. By coupling phase-encoded friction anisotropy with a predictive model, OSCAR delivers controllable thrust generation at low normal loads, enabling safer and more robust self-propelling locomotion for robotic capsule colonoscopy.

Explore related subjects

cs.ROphysics.med-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mostafa A. Atalla, Anand S. Sekar, Remi van Starkenburg, David J. Jager, Aimée Sakes, Michaël Wiertlewski, Paul Breedveld. 2026-02-17. OSCAR: An Ovipositor-Inspired Self-Propelling Capsule Robot for Colonoscopy. https://arxiv.org/abs/2602.15309

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

J-PARSE: Jacobian-based Projection Algorithm for Resolving Singularities Effectively in Inverse Kinematic Control of Serial Manipulators

J-PARSE is an algorithm for smooth first-order inverse kinematic control of a serial manipulator near kinematic singularities. The commanded end-effector velocity is interpreted component-wise, according to the available mobility in each dimension of the task space. First, a substitute ''Safety'' Jacobian matrix is created, keeping the aspect ratio of the manipulability ellipsoid above a threshold value. The desired velocity is then projected onto non-singular and singular directions, and the latter projection scaled down by a factor informed by the altered mobility. A right-inverse of the non-singular Safety Jacobian is applied to the modified command. In the absence of joint limits and collisions, this ensures safe transition into and out of low-mobility configurations, guaranteeing locally stable reaching behavior towards target poses within, on the boundary of, and outside the workspace. The behavior is further guaranteed to be locally asymptotically stable if the starting and target configurations are not exactly singular, even if they are nearly singular. Velocity control with J-PARSE is benchmarked against approaches from the literature, illustrating its use of a single tuning parameter to simultaneously achieve high reaching accuracy and stable behavior. Applications in teleoperation, servoing, and learning are demonstrated. Videos and code are available at https://jparse-manip.github.io/.

cs.RO↗
arXiv2026

Spatiotemporal Calibration of Doppler Velocity Logs for Underwater Robots

The calibration of extrinsic parameters and clock offsets between sensors for high-accuracy performance in underwater SLAM systems remains insufficiently explored. Existing methods for Doppler Velocity Log (DVL) calibration are either constrained to specific sensor configurations or rely on oversimplified assumptions, and none jointly estimate translational extrinsics and time offsets. We propose a Unified Iterative Calibration (UIC) framework for general DVL sensor setups, formulated as a Maximum A Posteriori (MAP) estimation with a Gaussian Process (GP) motion prior for high-fidelity motion interpolation. UIC alternates between efficient GP-based motion state updates and gradient-based calibration variable updates, supported by a provably statistically consistent sequential initialization scheme. The proposed UIC can be applied to IMU, cameras and other modalities as co-sensors. We release an open-source DVL-camera calibration toolbox. Beyond underwater applications, several aspects of UIC-such as the integration of GP priors for MAP-based calibration and the design of provably reliable initialization procedures-are broadly applicable to other multi-sensor calibration problems. Finally, simulations and real-world tests validate our approach.

cs.RO↗
arXiv2026

HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models

Vision-Language-Action (VLA) models inherit rich world knowledge from vision-language backbones and acquire executable skills via action demonstrations. However, existing evaluations largely focus on action execution success, leaving action policies loosely coupled with visual-linguistic semantics. This decoupling exposes a systematic vulnerability whereby correct action execution may induce unsafe outcomes under semantic risk. To expose this vulnerability, we introduce HazardArena, a benchmark designed to evaluate semantic safety in VLAs under controlled yet risk-bearing contexts. HazardArena is constructed from safe/unsafe twin scenarios that share matched objects, layouts, and action requirements, differing only in the semantic context that determines whether an action is unsafe. We find that VLA models trained exclusively on safe scenarios often fail to behave safely when evaluated in their corresponding unsafe counterparts. HazardArena includes over 2,000 assets and 40 risk-sensitive tasks spanning 7 real-world risk categories grounded in established robotic safety standards. To mitigate this vulnerability, we propose a training-free Safety Option Layer that constrains action execution using semantic attributes or a vision-language judge, substantially reducing unsafe behaviors with minimal impact on task performance. We hope that HazardArena highlights the need to rethink how semantic safety is evaluated and enforced in VLAs as they scale toward real-world deployment. Code released at https://github.com/HazardArena-Team/HazardArena ; updated code availability information.

cs.RO↗