Utilizing Hyperchaos in Memristive Dynamical Systems to Design Self-Randomizing UAV Surveillance Routes

This paper presents a novel method to generate pseudo-random yet deterministic flight trajectories for secure UAV surveillance using simulations of a hyperchaotic memristive coupled neural network model. The hyperchaotic dynamics of this memristive system produce long-term unpredictable 2D/3D way-point sequences while maintaining full determinism. Simulation results validate that trajectories computed from the chaotic solutions maintain randomness over extended durations, ensuring unpredictability and thorough coverage of patrol areas. Critically, patrol routes repeat identically on each mission but appear random, improving security without onboard computational complexity. The findings suggest this approach offers a potential means of defining navigation pathways through deterministic chaos for repetitive UAV surveillance applications, providing proof-of-concept and assessing trajectory qualities through numerical simulations.