Abstract
Route planning is the core component of unmanned combat aerial vehicle (UCAV) systems and the premise for implementation of airborne reconnaissance, surveillance, combat, and other tasks. The purpose is to find the optimal flight route under certain constraints, and its essence is a multi-constrained global optimisation problem. This paper presents a modified symbiotic organisms search algorithm based on the simplex method (SMSOS) to solve the UCAV path planning problem. In addition to the flight environment for the fixed threat area, this paper tested the flight environment of the randomly generated threat area because of the complexity of the actual battlefield threat area. After many simulation tests, it was concluded that SMSOS can find the shortest flight path while avoiding the threat areas. The experimental results show that SMSOS has faster convergence speed, higher precision, and stronger robustness than the other main swarm intelligence algorithms for solving the UCAV flight planning problem.
Published Version
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