Path Planning of an Unmanned Combat Aerial Vehicle with an Extended-Treatment-Approach-Based Immune Plasma Algorithm

This project tackles the hard problem of planning routes for Unmanned Combat Aerial Vehicles (UCAVs) that must fly through very dangerous areas. An optimal path has to balance several competing goals: keep the total distance short, use as little fuel as possible, stay clear of radar and other threats, and obey strict motion limits such as maximum turn and climb angles. To meet these demands, we test a new version of the Immune Plasma Algorithm (IPA) called the Extended Immune Plasma Algorithm (ExtIPA). In ExtIPA the usual plasma‑transfer steps are replaced by a newly designed “extended treatment” method. This change improves the algorithm’s ability to search globally and helps it

avoid getting stuck early in complex problem spaces. We run ExtIPA in many simulated battlefield scenarios that include different threat layouts and terrain obstacles. By carefully adjusting the algorithm’s parameters and comparing its results with those from standard meta‑heuristic planners, we show that ExtIPA can produce routes that are safe, flyable, and fuel‑efficient. The simulation framework we built offers a solid tool for testing guidance, navigation, and control strategies in modern aerospace systems.

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