Enhanced Artificial Potential Field for Planning the Safe Return of Wartime Aircraft

In the wartime scenario of large-scale air combat, the battlefield situation is dynamically changing, and damaged aircraft often face issues such as limited maneuverability and fuel shortages. Relying on fixed corridors for return flights is no longer feasible. To quickly address wartime demands, it is essential to determine alternative routes for damaged aircraft in real-time, which must not only avoid multi-source threats but also adapt to their performance constraints. This paper improves the traditional artificial potential field algorithm by adopting a two-tier strategy that combines the greedy algorithm with the artificial potential field algorithm. First, to address limitations of the traditional APF method, relative distance is introduced to solve target unreachability, adjustment force is introduced to break local minimum traps, a dynamic potential field reconstruction strategy is introduced to traverse dense obstacles, and a memory-based resultant force is introduced to suppress oscillations. Subsequently, the greedy algorithm optimizes the initial path planned by the APF to shorten the distance. However, when sudden or moving threats are encountered and their influence range is entered, the fast-computing APF algorithm is used for real-time avoidance. The simulation results confirm that the improved algorithm effectively resolves the above issues, with fast planning, smooth, high-quality paths, and guaranteed safe return of aircraft.