A sampling-based planning method for dynamic optimization problems

Date

2023-9

Author

Aguş, Ege Uğur

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This thesis investigates a new sampling-based planning method for efficiently solving complex dynamic optimization problems that other sampling-based planning meth- ods do not provide optimal solutions. Proposed sampling-based planning method achieves this by using a new search method, studied in this thesis, possible optimal paths search, POPS. Possible optimal paths search is a search method that finds all paths that are optimal for at least one set of weights of the edges or nodes of a graph. Proposed sampling-based planning method uses the basics of the POPS to find alter- native paths from an initial state to goal state. The proposed sampling-based planning method is designed to operate in real-time. It provides optimality by finding the paths from initial state to goal state with the minimum number of state changes. Outputs of the proposed method are the inputs for any discrete optimization methods which found in the literature. This way complex dynamic optimization problems can be solved efficiently.

Subject Keywords

Dynamic optimization, Search algorithms, Sampling-based planning, Discrete optimization, Planning algorithms

URI

https://hdl.handle.net/11511/105343

Collections

Graduate School of Natural and Applied Sciences, Thesis