article · Scientific Reports
Unmanned aerial vehicles operating in complex settings require efficient trajectory planning and precise control. This research introduces a hybrid navigation strategy combining a fractional-order proportional-integral-derivative tilt-integral-derivative controller with a hybrid Archimedes and rider optimisation algorithm. Evaluated against conventional methods including A star, jump point search, Bezier, and L-BSGF algorithms, the hybrid scheme demonstrated superior path planning capabilities. Simulation findings reveal that the proposed approach shortens trajectory lengths by ten percent while generating smoother flight paths and operating with greater computational efficiency. Incorporating fractional-order parameters also enhanced dynamic responses, disturbance rejection, and control precision under challenging flight conditions. The results deliver a proof of concept that hybrid optimisation techniques can enhance the stability, accuracy, and operational performance of autonomous aerial vehicles navigating dynamic spaces.
Autonomous drones frequently face intricate obstacles and external disturbances that make navigation difficult and energy-intensive. By decreasing trajectory length and smoothing flight paths, improved control systems allow aerial vehicles to manoeuvre through demanding spaces more reliably. Stronger disturbance rejection and precise steering are essential for ensuring that autonomous systems complete missions safely without losing control or wasting computational power.
The method could be used by developers of autonomous flight control software, unmanned aircraft manufacturers, and robotics teams operating in complex environments. Because the performance gains have been established through simulation rather than physical flight trials, this technology represents an early-stage proof of concept that requires hardware integration and physical testing before reaching commercial readiness.
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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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DOI: 10.1038/s41598-025-86803-4
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