6,194 research outputs found
Modelling mitral valvular dynamics–current trend and future directions
Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed
Informative Path Planning for Active Field Mapping under Localization Uncertainty
Information gathering algorithms play a key role in unlocking the potential
of robots for efficient data collection in a wide range of applications.
However, most existing strategies neglect the fundamental problem of the robot
pose uncertainty, which is an implicit requirement for creating robust,
high-quality maps. To address this issue, we introduce an informative planning
framework for active mapping that explicitly accounts for the pose uncertainty
in both the mapping and planning tasks. Our strategy exploits a Gaussian
Process (GP) model to capture a target environmental field given the
uncertainty on its inputs. For planning, we formulate a new utility function
that couples the localization and field mapping objectives in GP-based mapping
scenarios in a principled way, without relying on any manually tuned
parameters. Extensive simulations show that our approach outperforms existing
strategies, with reductions in mean pose uncertainty and map error. We also
present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation
Letters (and IEEE International Conference on Robotics and Automation
Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space
Existing work in multi-agent collision prediction and avoidance typically
assumes discrete-time trajectories with Gaussian uncertainty or that are
completely deterministic. We propose an approach that allows detection of
collisions even between continuous, stochastic trajectories with the only
restriction that means and variances can be computed. To this end, we employ
probabilistic bounds to derive criterion functions whose negative sign provably
is indicative of probable collisions. For criterion functions that are
Lipschitz, an algorithm is provided to rapidly find negative values or prove
their absence. We propose an iterative policy-search approach that avoids prior
discretisations and yields collision-free trajectories with adjustably high
certainty. We test our method with both fixed-priority and auction-based
protocols for coordinating the iterative planning process. Results are provided
in collision-avoidance simulations of feedback controlled plants.Comment: This preprint is an extended version of a conference paper that is to
appear in \textit{Proceedings of the 13th International Conference on
Autonomous Agents and Multiagent Systems (AAMAS 2014)
Motion Planning under Uncertainty for Autonomous Navigation of Mobile Robots and UAVs
This thesis presents a reliable and efficient motion planning approach based on state lattices
for the autonomous navigation of mobile robots and UAVs. The proposal retrieves optimal
paths in terms of safety and traversal time, and deals with the kinematic constraints and the
motion and sensing uncertainty at planning time. The efficiency is improved by a novel
graduated fidelity state lattice which adapts to the obstacles in the map and the
maneuverability of the robot, and by a new multi-resolution heuristic which reduces the
computational complexity. The motion planner also includes a novel method to reliably
estimate the probability of collision of the paths considering the uncertainty in heading and
the robot dimensions
- …