Research On UAV Trajectory Planning Method Based On Improved Artificial Bee Colony Algorithm

Unmanned aerial vehicles(UAVs)are now becoming widely used in both military and civilian applications due to their advantages such as being easy to train operators and avoiding pilot casualties during missions.The ability to obtain a feasible and safe trajectory for a UAV mission affects the quality of its mission execution.Track planning is an NP-hard optimization problem with many objectives and constraints,and in complex operational environments,more track points need to be set to satisfy the quality of the planned trajectory,but it also brings difficulties to planning an UAV track,i.e.,the algorithm needs to solve the great challenge of increasing convergence difficulty due to the increase of solution dimensions,and it also needs to avoid the algorithm search into local optimum as much as possible.In this study,we systematically investigate the UAV trajectory planning problem in high-dimensional complex environments and design an efficient improved artificial bee colony algorithm for UAV trajectory planning.The main research contents and results are as follows:A complex 3D flight environment model is established.This study uses mathematical functions to model the terrain in a 3D environment,and introduces threat areas such as anti-aircraft guns and no-fly zones to construct complex scenarios;constructs relative flight altitude and range cost functions;and constructs UAV climb angle and yaw constraints.The collision threat,antiaircraft gun threat and manoeuvre violation constraint are quantified as cost functions,and the algorithm is constructed to optimise the objective function.A single UAV trajectory planning method based on improved multidimensional perturbation artificial swarm is proposed.Based on the artificial swarm algorithm,this study introduces two multidimensional perturbation strategies,propensity selection and random selection,to balance the exploitation and exploration capabilities,and adopts an elite individual search-based strategy to improve the optimisation efficiency.The MDP-ABC algorithm is simulated and validated for four scenarios with the number of waypoints set to 10,20,30,40 in a complex scene compared to seven compared algorithms.The simulation results show that the introduced strategy can effectively improve the performance of MDP-ABC.The proposed MDP-ABC planner has outstanding capability in planning UAV 3D trajectories in complex environments.A double precision multi-UAV trajectory planning method approach based on ant colony and artificial bee colony is proposed.The inter-UAV collision constraint and cost function are constructed for the multi-UAV multitasking problem using an ant colony-based,MDP-ABC double-stage search method to improve the quality of the initial solution of the algorithm solution and increase the efficiency of the algorithm search.Simulation analysis reveals that this approach has more efficient search efficiency when planning the multi-UAV multi-task problem.