Research on global trajectory planning for UAV based on the information interaction and aging mechanism Wolfpack algorithm

Abstract

The planning of trajectories for multi-unmanned aerial vehicles (UAVs) has been a topic of intensive research in both military and civilian contexts. It is a crucial aspect of the overall intelligence capabilities of UAV formation systems. In order to enhance the capability of multi-UAVs autonomous trajectory planning and to facilitate attainment of optimal paths in mountainous environments, this paper proposes an information interaction and aging mechanism Wolfpack Algorithm (IIAM-WPA). Firstly, a mission environment model is established using digital elevation modelling technology to simulate the real mountainous environment. Secondly, a trajectory planning model is established by comprehensively considering the terrain, threats and formation security factors. Meanwhile, in order to comprehensively evaluate the planning results, a new composite objective function is proposed. The proposed IIAM-WPA method is finally employed to identify the optimal paths for multiple UAVs. The key improvements to the method are as follows: the initialization effect is enhanced by the Chebyshev chaotic mapping in initialization phase, thereby accelerating the convergence of the population. Furthermore, the aging mechanism of wolves is incorporated into the model to enhance the efficiency of wolf search. Meanwhile, communication between populations is augmented during the encirclement phase, which serves to enhance population diversity. Finally, a selective mutation mechanism is introduced to rescue the population from the local optimum trap. In order to ascertain the effectiveness of the proposed algorithm, the simulation results of UAV trajectory planning under different mission scenarios are presented and compared with various optimization techniques. The simulation results demonstrate that the maximum improvement rate of the proposed algorithm is 96.73% and 4.2% in single UAV and multi-UAV planning tasks, respectively. This further verifies the planning accuracy and efficiency of the IIAM-WPA method and effectively proves the effectiveness of the method in solving UAV trajectory planning problems.