Multi-Strategy Synthesis Based Artificial Bee Colony Algorithm for UAV Path Replanning
Hao Li, Hua Wu · 2023
Route planning for drones is a complex global optimization problem. Considering the different types of constraints in complex environments, the flight route is replanned due to the emergence of emergencies. The artificial bee colony (ABC) algorithm is a calculation method that mimics the honey collecting behavior of bees, proposing an improved artificial bee colony (IABC) algorithm based on multi-strategy synthesis. IABC initializes the population based on the chaotic mapping mechanism to increase the diversity of the initial positions. At the same time, in order to enhance the search capability, a search strategy based on similarity was designed to search the location of nearby honey sources, and provide a candidate flight path for unmanned aerial vehicles (UAVs). Then, the tangential stochastic evolution mechanism is added to introduce some random changes, which enhances the updating ability in emergencies. Genetic algorithm is used for scheduling. Due to unexpected events, changes in mission point position, end point position, obstacle position, and flight speed, the flight path is re-planned to reach the target point. Aiming at the complex three-dimensional environment, a fitness function is established under various constraints, and the generated path meets the requirements of flight.