A Threat-Based Unmanned Vehicle Cluster Mission Planning Model

In response to the problems of large computational load in path planning, inflexibility in strike strategies, and a wide range of selectable strike locations in multi-unmanned vehicle cluster strikes, this paper proposes a threat-based unmanned vehicle cluster mission planning model. This model is an improved two-layer model combining Greedy Tabu Search and A* algorithm. The initial solution is generated by the greedy algorithm according to different strategies, then iteratively optimized by the tabu search algorithm. The route for each unmanned vehicle to reach the target strike area is calculated through the improved A* algorithm. And the evaluation of the plan is done based on weighted strike time calculated from parameters such as target threat level. While considering the strike range and reducing the number of path planning, thus decreasing calculation load, this model achieves a convergence rate that is 60% faster than traditional tabu algorithms. The effectiveness and speed of this model are verified using 20 strike targets and three unmanned vehicles as an example, compared with traditional distance-priority and threat-priority strategies, the required time is reduced by 13.2% and 21.8%, respectively, and the speed of threat reduction is increased by 15.2% and 27.9%, respectively. This verifies the effectiveness and speed of this model. The research results show that in the planning of unmanned vehicle cluster strike missions, decision-makers can use this model to adjust the weight value of the target threat level, thereby flexibly and quickly formulating the optimal comprehensive strike scheme weighted based on the target threat level.