AI-Driven Blade Alignment for Aerial Vehicles' Rotary Systems using A* Algorithm and Statistical Heuristic

In the aviation domain, precise alignment of helicopter blades is paramount for ensuring optimal performance and safety during flight operations. Manual methods for blade alignment often demand extensive calculations and experienced technicians, resulting in time-consuming processes. This research proposes an innovative AI-based algorithm, integrating the A* algorithm and a statistical heuristic function, to optimise blade alignment in helicopter rotary systems. The algorithm seeks to minimise the standard deviation of blade distances from the ground, captured using high-speed distance sensors. Firstly, the initial blade positions, along with the swashplate turns limitations, are given to the algorithm. Later, by exploring all potential adjustments and selecting the most promising sequence to minimise the standard deviation of blade distances (considering the allowable pitch limits), the algorithm achieves precise blade alignment, enhancing helicopter performance and safety. Subsequently, the algorithm outputs the recommended sequence of adjustments to be made in the swashplate.

To validate the algorithm's efficacy, we conducted comprehensive case studies using MI 17 helicopters as a testbed. The algorithm was assessed under varying scenarios, such as near-perfect alignment, single-blade misalignment in upward and downward directions, and multiple blades in asymmetric positions. The results demonstrate the algorithm's capability to swiftly recommend the precise sequence of adjustments for each control rod nut, effectively minimising blade misalignment and reducing standard deviation. The implications of this research are far-reaching, promising enhanced helicopter performance and safety across diverse application domains. By automating and streamlining the blade alignment process, the algorithm mitigates the reliance on human expertise and manual calculations, ensuring consistent and accurate blade alignment in real-world scenarios.