In this study, we present a comparative analysis of various trajectory optimization algorithms for Unmanned Aerial Vehicles (UAVs) navigating complex environments. The performance of the proposed FOPID-TID based HAOAROA (Hybrid Archimedes Optimization Algorithm-Rider Optimization Algorithm) is evaluated against traditional methods such as A*, JPS, Bezier, and L-BSGF algorithms. The FOPID-TID based HAOAROA approach integrates the advantages of fractional-order control with hybrid optimization techniques to improve UAV trajectory planning. Simulation results indicate that the proposed method carries significantly better performance than the traditional algorithms with respect to trajectory length, smoothness, and overall stability. Remarkably, the FOPID-TID based HAOAROA yields a 10% reduced trajectory length that is smoother than traditional methods while also being more computationally efficient. By using fractional-order parameters, the dynamic response becomes better and better in more challenging environments. This shows that disturbance rejection and control precision using the FOPID-TID based HAOAROA are much superior to the original two subroutines. The applications presented in this study allow future growth in UAV control system improvements and provide proof of concept of hybrid optimization in improving the performance of UAVs in dynamic, complex environments.
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Basil et al. (2025) studied this question.