2 research outputs found
Concurrent Constrained Optimization of Unknown Rewards for Multi-Robot Task Allocation
Task allocation can enable effective coordination of multi-robot teams to
accomplish tasks that are intractable for individual robots. However, existing
approaches to task allocation often assume that task requirements or reward
functions are known and explicitly specified by the user. In this work, we
consider the challenge of forming effective coalitions for a given
heterogeneous multi-robot team when task reward functions are unknown. To this
end, we first formulate a new class of problems, dubbed COncurrent Constrained
Online optimization of Allocation (COCOA). The COCOA problem requires online
optimization of coalitions such that the unknown rewards of all the tasks are
simultaneously maximized using a given multi-robot team with constrained
resources. To address the COCOA problem, we introduce an online optimization
algorithm, named Concurrent Multi-Task Adaptive Bandits (CMTAB), that leverages
and builds upon continuum-armed bandit algorithms. Experiments involving
detailed numerical simulations and a simulated emergency response task reveal
that CMTAB can effectively trade-off exploration and exploitation to
simultaneously and efficiently optimize the unknown task rewards while
respecting the team's resource constraints.Comment: 9 pages, 5 figures, to be published in RSS 202
Image Enhancement & Automatic Detection of Exudates in Diabetic Retinopathy
Diabetic retinopathy (DR) is becoming a global health concern, which causes the loss of vision of most patients with the disease. Due to the vast prevalence of the disease, the automated detection of the DR is needed for quick diagnoses where the progress of the disease is monitored by detection of exudates changes and their classifications in the fundus retina images. Today in the automated system of the disease diagnoses, several image enhancement methods are used on original Fundus images. The primary goal of this thesis is to make a comparison of three of popular enhancement methods of the Mahalanobis Distance (MD), the Histogram Equalization (HE) and the Contrast Limited Adaptive Histogram Equalization (CLAHE). By quantifying the comparison in the aspect of the ability to detect and classify exudates, the best of the three enhancement methods is implemented to detect and classify soft and hard exudates. A graphical user interface is also adopted, with the help of MATLAB. The results showed that the MD enhancement method yielded better results in enhancement of the digital images compared to the HE and the CLAHE. The technique also enabled this study to successfully classify exudates into hard and soft exudates classification. Generally, the research concluded that the method that was suggested yielded the best results regarding the detection of the exudates; its classification and management can be suggested to the doctors and the ophthalmologists