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research-article

A SEQUENTIAL SAMPLING ALGORITHM FOR MUTLI-STAGE STATIC COVERAGE PROBLEMS

[+] Author and Article Information
Binbin Zhang

MAD LAB, Mechanical and Aeronautical Engineering, University at Buffalo, SUNY, Buffalo, NY 14260
bzhang25@buffalo.edu

Jida Huang

Industrial and Systems Engineering, University at Buffalo, SUNY, Buffalo, NY 14260
jidahuan@buffalo.edu

Rahul Rai

MAD LAB, Mechanical and Aeronautical Engineering, University at Buffalo, SUNY, Buffalo, NY 14260
rahulrai@buffalo.edu

Hemanth Manjunatha

Mechanical and Aeronautical Engineering, University at Buffalo, SUNY, Buffalo, NY 14260
hemanthm@buffalo.edu

1Corresponding author.

ASME doi:10.1115/1.4039901 History: Received November 03, 2017; Revised April 04, 2018

Abstract

In many system-engineering problems such as surveillance, environmental monitoring, and cooperative task performance, it is critical to allocate limited resources within a restricted area optimally. Static coverage problem (SCP) is an important class of the resource allocation problem. SCP focuses on covering an area of interest so that the activities in that area can be detected with high probabilities. In many practical settings, primarily due to financial constraints, a system designer has to allocate resources in multiple stages. In each stage, the system designer can assign a fixed number of resources, i.e., agents. In the multi-stage formulation, agent locations for the next stage are dependent on previous stage agent locations. Such multi-stage static coverage problems are non-trivial to solve. In this paper, we propose a robust and efficient sequential sampling algorithm to solve the multi-stage static coverage problem in the presence of Resource Intensity Allocation Maps (RIAMs) distribution functions that abstract the event that we want to detect/monitor in a given area. The agent's location in the successive stage is determined by formulating it as an optimization problem. Three different objective functions have been developed and proposed in this paper (1) L2 difference, (2) Sequential Minimum Energy Design (SMED) and (3) the weighted L2 and SMED. Pattern search, an efficient heuristic algorithm has been used as optimization algorithm to arrive at the solutions for the formulated optimization problems.

Copyright (c) 2018 by ASME
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