At present, the studies on multi-team antagonistic games(MTAGs) are still in the early stage, because this complicated problem involves not only incompleteness of information and conflict of interests, but also selection of antagonistic targets.Therefore, based on the previous researches, a new framework is proposed in this paper, which is dynamic multi-team antagonistic games with incomplete information(DMTAGII) model.For this model, the corresponding concept of perfect Bayesian Nash equilibrium(PBNE) is established and the existence of PBNE is also proved. Besides, an interactive iteration algorithm is introduced according to the idea of the best response for solving the equilibrium. Then, the scenario of multiple unmanned aerial vehicles(UAVs) against multiple military targets is studied to solve the problems of tactical decision making based on the DMTAGII model. In the process of modeling, the specific expressions of strategy, status and payoff functions of the games are considered, and the strategy is coded to match the structure of genetic algorithm so that the PBNE can be solved by combining the genetic algorithm and the interactive iteration algorithm.Finally, through the simulation the feasibility and effectiveness of the DMTAGII model are verified. Meanwhile, the calculated equilibrium strategies are also found to be realistic, which can provide certain references for improving the autonomous ability of UAV systems.
The optimal path planning for fixed-wing unmanned aerial vehicles(UAVs) in multi-target surveillance tasks(MTST) in the presence of wind is concerned.To take into account the minimal turning radius of UAVs,the Dubins model is used to approximate the dynamics of UAVs.Based on the assumption,the path planning problem of UAVs in MTST can be formulated as a Dubins traveling salesman problem(DTSP).By considering its prohibitively high computational cost,the Dubins paths under terminal heading relaxation are introduced,which leads to significant reduction of the optimization scale and difficulty of the whole problem.Meanwhile,in view of the impact of wind on UAVs' paths,the notion of virtual target is proposed.The application of the idea successfully converts the Dubins path planning problem from an initial configuration to a target in wind into a problem of finding the minimal root of a transcendental equation.Then,the Dubins tour is derived by using differential evolution(DE) algorithm which employs random-key encoding technique to optimize the visiting sequence of waypoints.Finally,the effectiveness and efficiency of the proposed algorithm are demonstrated through computational experiments.Numerical results exhibit that the proposed algorithm can produce high quality solutions to the problem.
