Limiting the incident NA for efficient wavefront shaping through thin anisotropic scattering media

Wave front shaping holds great potential for high-resolution imaging or light delivery either through or deep inside living tissue. However, one of the biggest barriers that must be overcome to unleash the full potential of wavefront shaping for practical biomedical applications is the fact that wavefront shaping, especially based on iterative feedback, requires lengthy measurements to obtain useful correction of the output wavefront. As biological tissues are inherently dynamic, the short decorrelation time sets a limit on the achievable wavefront shaping enhancement. Here we show that for wavefront shaping in thin anisotropic scattering media such as biological tissues, we can optimize the wavefront shaping quality by simply limiting the numerical aperture (NA) of the incident wavefront. Using the same number of controlled modes, and therefore the same wavefront measurement time, we demonstrate that the wavefront shaped focus peak to background ratio can be increased by a factor of 2.1 while the energy delivery throughput can be increased by a factor of 8.9 through 710 mu m thick brain tissue by just limiting the incident NA. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

Overcoming the penetration depth limit in optical microscopy: Adaptive optics and wavefront shaping

Despite the unique advantages of optical microscopy for molecular specific high resolution imaging of living structure in both space and time, current applications are mostly limited to research settings. This is due to the aberrations and multiple scattering that is induced by the inhomogeneous refractive boundaries that are inherent to biological systems. However, recent developments in adaptive optics and wavefront shaping have shown that high resolution optical imaging is not fundamentally limited only to the observation of single cells, but can be significantly enhanced to realize deep tissue imaging. To provide insight into how these two closely related fields can expand the limits of bio imaging, we review the recent progresses in their performance and applicable range of studies as well as potential future research directions to push the limits of deep tissue imaging

Efficient Environment Representation for Mobile Robot Path Planning using CVT-PRM with Halton Sampling

A centroidal Voronoi tessellation-based probabilistic roadmap (CVT-PRM) and its construction method for mobile robot path planning are introduced. The CVT-PRM efficiently encoded the entire unoccupied region of the environment by autonomously rearranging the positions of nodes via CVT and Halton sampling. Simulation results verified that the CVT-PRM can encode the entire unoccupied region efficiently, using evenly distributed nodes.X1132sciescopu

Incremental hierarchical roadmap construction for efficient path planning

This paper proposes a hierarchical roadmap (HRM) and its construction process to efficiently represent navigable areas in an indoor environment. HRM is adopted to solve the path-planning problems of mobile robots in indoor environments. HRM has a multi-layered graphical structure that enables it to abstract and cover navigable areas using a smaller number of nodes and edges than a probabilistic roadmap. During the incremental process of constructing HRM, information on navigable areas is abstracted using a sonar gridmap when the mobile robot navigates an unexplored area. The HRM-based planner efficiently searches for paths to answer queries by reducing the search space size using the multi-layered graphical structure. The benefits of the proposed HRM are experimentally verified in real indoor environments.11Nsciescopuskc

Sampling-based retraction method for improving the quality of mobile robot path planning

This paper presents a method for improving the quality of the initial path produced by the probabilistic roadmap (PRM)-based mobile robot path planner. The sampling-based retraction method modifies the initial path to achieve approximate maximum safety by removing unsafe and redundant sections. The updated directions and distances of the waypoints on the initial path are determined by approximately modeling clearances around the initial paths using random samples. The proposed method can control the update speed to induce smooth convergence. The performance of the proposed method was verified by simulation.1165sciescopuskc

Roadmap Coverage Improvement using a Node Rearrangement Method for Mobile Robot Path Planning

This paper proposes a method to efficiently abstract the traversable regions of a bounded two-dimensional environment using the probabilistic roadmap (PRM) to plan the path for a mobile robot. The proposed method uses centroidal Voronoi tessellation to autonomously rearrange the positions of initially randomly generated nodes. The PRM using the rearranged nodes covers most of the traversable regions in the environment and regularly divides them. The rearranged roadmap reduces the search space of a graph search algorithm and helps to promptly answer arbitrary queries in the environment. The mobile robot path planner using the proposed rearranged roadmap was integrated with a local planner that considers the kinematic properties of a mobile robot, and the efficiency and the safety of the paths were verified by simulation. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2012X1122sciescopu

Sensing Range Extension for Short-Baseline Stereo Camera Using Monocular Depth Estimation

This paper proposes a method to extend a sensing range of a short-baseline stereo camera (SBSC). The proposed method combines a stereo depth and a monocular depth estimated by a convolutional neural network-based monocular depth estimation (MDE). To combine a stereo depth and a monocular depth, the proposed method estimates a scale factor of a monocular depth using stereo depth–mono depth pairs and then combines the two depths. Another advantage of the proposed method is that the trained MDE model may be utilized for different environments without retraining. The performance of the proposed method is verified qualitatively and quantitatively using the directly collected and open datasets

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