Improved Black Hole Algorithm for Efficient Low Observable UCAV Path Planning in Constrained Aerospace

Abstract

An essential task of UAV autonomy is automatic path planning. There are many evolutionary planners for Unmanned Aerial Vehicles (UAVs) that have been developed UAV community. In this paper a comparative study about performance of effective trajectory planners is done. Also an efficient version of black hole methodology has been introduced for single UCAV trajectory planning, and an enhancement is designed to communicate among stars and black hole based on relativity theory principles. By considering UCAV Dynamic properties and environment constraints, Developed path planner based on black hole algorithm can compute feasible and quasi-optimal trajectories for UCAV flight. Our comparison of algorithms shows that IBH generates desired optimal trajectories. Then path planning task of UCAV is performed. Simulations show advantage of IBH methodology.