Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/153970
Title: OPTIMIZATION ANALYSIS OF PATH PLANNING FOR ADAPTIVE SAMPLING
Authors: HAINING ZHENG
Keywords: mixed integer linear programming
parallel branch and bound algorithm
path planing
adaptive sampling
vortices
Issue Date: 2008
Citation: HAINING ZHENG (2008). OPTIMIZATION ANALYSIS OF PATH PLANNING FOR ADAPTIVE SAMPLING. ScholarBank@NUS Repository.
Abstract: Due to the highly nonlinear and dynamical nature of oceanic phenomena, the predictive capability of various ocean models is relatively limited. A practical method to improve the predictive accuracy is using data assimilation technologies to combine in-situ measured and remotely acquired data with the numerical model and reduce the uncertainty of forecasting. Autonomous Underwater/Surface Vehicles (AUV/ASV) with various sensors are economic and efficient tools for exploring the ocean, however there is energy limitation of such vehicles, thus path planning is required to optimize the efficiency of exploration. In this thesis, first the Mixed Integer Linear Programming (MILP) of path planning for an adaptive sampling problem is reviewed and a Branch and Bound algorithm, which is commonly used to solve the MILP problem, is thoroughly analyzed. Thereafter, the Parallel Branch and Bound algorithm and other computation efficiency improvement methods are proposed and applied to solve the MILP optimization problem using the Xpress-MP package. Numerical tests show significant computation time deduction with the improved algorithm. Furthermore, the new path planning algorithm is implemented for the Singapore Straits coastal zone as an aide of planning sea trials and achieves good results. Beside the objective to reduce uncertainty, path planning algorithm can also be developed to explore ocean regions with physical or biological significance. Thus oceanographic feature exploration around the Monggok Gerita region south of Singapore is briefly discussed and suggestions for future work are given at last.
URI: https://scholarbank.nus.edu.sg/handle/10635/153970
Appears in Collections:Master's Theses (Restricted)

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