Title: UAV path planning in presence of occlusions as noisy combinatorial multi-objective optimisation

Authors: Aishwaryaprajna; Thia Kirubarajan; Ratnasingham Tharmarasa; Jonathan E. Rowe

Addresses: School of Computer Science, University of Birmingham, Birmingham, UK ' Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada ' Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada ' School of Computer Science, University of Birmingham, Birmingham, UK; The Alan Turing Institute, London, UK

Abstract: A realistic noisy combinatorial problem on surveillance by unmanned aerial vehicle (UAV) in presence of weather factors is defined. The presence of cloud coverage is considered as a posterior Gaussian noise in the visibility region of the UAV. Recent studies indicate that recombination-based search mechanisms are helpful in solving noisy combinatorial problems. The search strategy of univariate marginal distribution algorithm that includes only selection and recombination, which has a close association with genepool crossover, proves to be beneficial in solving constrained and multi-objective combinatorial problems in presence of noise. This paper proposes a solution methodology based on multi-objective UMDA (moUMDA) with diversification mechanisms for the multi-objective problem of UAV surveillance. To obtain a well-spread set of Pareto optimal solutions, relevant diversification mechanisms are important. Numerical simulations show that moUMDA with and without K-means clustering provides better quality solutions and a more diverse Pareto optimal set than NSGA-II in solving this noisy problem.

Keywords: noisy combinatorial optimisation; posterior additive noise; UAV path planning; multi-objective optimisation; clustering.

DOI: 10.1504/IJBIC.2023.132789

International Journal of Bio-Inspired Computation, 2023 Vol.21 No.4, pp.209 - 217

Received: 01 Dec 2021
Accepted: 28 Jan 2023
Published online: 09 Aug 2023 *

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