Effective Metrics for Multi-Robot Motion-Planning

Atias, Aviel; Solovey, Kiril; Halperin, Dan

doi:10.15607/rss.2017.xiii.022

Cited by 3 publications

(1 citation statement)

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“…(1) Experimental background This experiment detects the application of bayesian improved hidden markov algorithm in low-dose CT images [26][27][28]. In order to verify the feasibility and effectiveness of the improved algorithm, the enhanced mutual information selection function in this paper is designed and tested, and the enhanced tf-idf is used to assign different weights to the selected function.…”

Section: Experimental Settingsmentioning

confidence: 99%

Improved Hidden Markov Algorithm Based on Bayes in Low Dose CT Images

2023

IJMC

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In order to reduce the radiation exposure of patients during CT scan, low-dose CT images were produced, but the disadvantage was that the image quality was reduced. The Bayesian maximum posterior probability estimation (Bayesian MAP) method is an applied statistical method that can estimate the original noise-independent coefficients from the noise-contaminated image detail coefficients. This paper aims to study the application of bayesian based improved hidden markov algorithm in low-dose CT images, which has become the focus of CT research in recent years. In this experiment, hmrf-em, eHMRF algorithm and hmrf-msa-em algorithm were firstly analyzed by mathematical statistics within the experimental scope, and the superiority of this algorithm was compared by looking at different coefficients. The classification and statistical analysis of the re-data statistical method were carried out by using the naive bayesian algorithm and the improved hidden markov algorithm based on bayes. And the use of a single variable method to compare the use of bayesian based improved hidden markov algorithm in the low-dose CT image imaging whether there are different changes, and the degree of change. Experimental data show that the improved hidden markov algorithm based on bayes achieves higher values of Jaccard, Dice and CCR at different noise levels. The improved hidden markov algorithm based on bayes is clearer than the low-dose CT images obtained by the naive bayes algorithm. In various medical images, the improved hidden markov algorithm based on bayes plays an obvious role in changing the resolution of low-dose CT images. Experimental data show that compared with other denoising methods, the peak signal-to-noise ratio of the de-noised image can be improved, the detailed features of the image can be retained better, the visual effect can be improved by 46.78%, and the correct segmentation rate can reach 95.75%.

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Section: Experimental Settingsmentioning

confidence: 99%

Improved Hidden Markov Algorithm Based on Bayes in Low Dose CT Images

2023

IJMC

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Following Surgical Trajectories with Concentric Tube Robots via Nearest-Neighbor Graphs

Niyaz

Kuntz

Salzman

et al. 2020

Springer Proceedings in Advanced Robotics

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Effective metrics for multi-robot motion-planning

Atias

Solovey

Salzman³

et al. 2018

The International Journal of Robotics Research

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We study the effectiveness of metrics for Multi-Robot Motion-Planning (MRMP) when using RRT-style sampling-based planners. These metrics play the crucial role of determining the nearest neighbors of configurations and in that they regulate the connectivity of the underlying roadmaps produced by the planners and other properties like the quality of solution paths. After screening over a dozen different metrics we focus on the five most promising ones-two more traditional metrics, and three novel ones which we propose here, adapted from the domain of shape-matching. In addition to the novel multi-robot metrics, a central contribution of this work are tools to analyze and predict the effectiveness of metrics in the MRMP context. We identify a suite of possible substructures in the configuration space, for which it is fairly easy (i) to define a so-called natural distance, which allows us to predict the performance of a metric. This is done by comparing the distribution of its values for sampled pairs of configurations to the distribution induced by the natural distance; (ii) to define equivalence classes of configurations and test how well a metric covers the different classes. We provide experiments that attest to the ability of our tools to predict the effectiveness of metrics: those metrics that qualify in the analysis yield higher success rate of the planner with fewer vertices in the roadmap. We also show how combining several metrics together leads to better results (success rate and size of roadmap) than using a single metric.

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Effective Metrics for Multi-Robot Motion-Planning

Cited by 3 publications

References 50 publications

Improved Hidden Markov Algorithm Based on Bayes in Low Dose CT Images

Improved Hidden Markov Algorithm Based on Bayes in Low Dose CT Images

Following Surgical Trajectories with Concentric Tube Robots via Nearest-Neighbor Graphs

Effective metrics for multi-robot motion-planning

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