Research On Distributed Online Cooperative Mission Planning For Multiple Unmanned Combat Aerial Vehicles In Dynamic Environment

Cooperative mission planning for multiple Unmanned Combat Aerial Vehicles(UCAVs) is one of the key technologies to realize the cooperative operation for multipleUCAVs in complicated battlefield environment and enhance the efficiency ofmulti-UCAVs cooperation. This dissertation focuses on the online mission planningproblem of typical multi-UCAVs SEAD mission in dynamic battle fields, in terms ofmulti-UCAVs cooperative SEAD under the management of manned fighters andmulti-UCAVs independent cooperative SEAD, builds mathematical model, researchesand designs optimization algorithm, and carries out simulation and experiments. Themain work and the creative contribution are as follows:(1) The processes of multi-UCAVs cooperative SEAD mission are analyzed, themodels of elements which are related to the mission executing processes and planningprocesses are built, and the distributed structure of multi-UCAVs cooperative systemsfor two typical SEAD mission modes are presented.According to the processes of multi-UCAVs cooperative SEAD mission, therelated elements are described, the formalized model including UCAV platforms,mission payloads, menace entities are presented based on the characteristics ofmulti-UCAVs cooperative SEAD. Aiming at the two typical modes of multi-UCAVsSEAD, the distributed structures for multi-UCAVs cooperative SEAD under themanagement of manned fighters and multi-UCAVs independent cooperative SEAD arepresented. The multi-UCAVs system based on the presented structures is highly robustand credible, and has sensitive reaction to the environments, flexible ability of systemrebuilding; therefore, it can satisfy the requirement of multi-UCAVs cooperativeoperation in highly dynamic battle fields.(2) Based on the parallel computing technology and the idea of co-evolutionary,the Co-Evolutionary Multi-Ant-Colony-Algorithm (COE-MACA) is proposed under theframework of multi-difference-ant-colony algorithm to solve the cooperation problemswith multiple behavior units.With considerations on the basic principle, algorithm model, characteristics ofoptimization, current situation of ant colony optimization research, and functionalmechanism of multi-ant-colony algorithm, the Co-EvolutionaryMulti-Ant-Colony-Algorithm is proposed under the framework ofmulti-difference-ant-colony algorithm. The algorithm is designed according to thecharacteristics of cooperation problem with multiple behavior units, the parallelcomputing technology is used to guide the design of COE-MACA, and theco-evolutionary strategy is introduced to the evolvement of multiple ant colonies. Themultiple colonies structure, strategies of parallel progress and information exchange, results evaluation based on co-evolutionary mechanism is studied, and the pheromonediffusion mechanism is used to improve the optimization capability of COE-MACA.The simulation of multi-UCAVs cooperative path planning using COE-MACA iscarried out to confirm the efficiency of the proposed algorithm, the experimental resultsindicate that the COE-MACA has good performance in describing the cooperativerelations between multiple behavior units by designing the elements of algorithm, andthe solution can be optimized rapidly through the evolution of ant colony. Compared tothe evolutionary algorithm, the convergence speed of COE-MACA increases at least10%in specific experimental scenario.(3) Based on the framework of Distributed Adaptive Model Predictive Control(DA-MPC), the local optimization model of multi-UCAVs online cooperative pathplanning is presented to solve the online cooperative path planning of multi-UCAVscooperative SEAD under the management of manned fighters, and the distributed onlinecooperative path planning algorithm to solve this model is proposed.The cooperative constraints in multi-UCAVs distributed cooperative online pathplanning are analyzed, and the cost model of multi-UCAVs cooperative path based oncooperative coefficient is presented; based on the distributed model predictive control,the UCAV path terminal penalty function based on the cost field and its generatingalgorithm is proposed. It is proofed that the cost field has only one local minimal pointat the specific target point. Then the local optimization model of multi-UCAVs onlinecooperative path planning is proposed. In this model, the complex optimization problemof multi-UCAVs cooperative system is decomposed into several local optimizationproblems corresponding to each UCAV, which can reduce the complexity of originalplanning problem on the system level. To solve the proposed model, the localoptimization algorithm based on COE-MACA and RHC is presented. The algorithmseparates and optimizes the original planning problem on the levels of system as well astime, which makes multi-UCAVs distributed online path planning easier to realize.Simulation results indicates that the proposed model and algorithm can separates theoriginal problem on different level, reduces the complexity and time cost ofmulti-UCAVs online cooperative path planning, in statistics of the simulation results,the planning time of proposed algorithm is10%of the distributed global optimizationalgorithm, and40%of the centralized local optimization algorithm, and furthermore, theproposed algorithm can balance the weight between time cost and optimized solution,and has good performance in response to dynamic environment such as avoiding suddenmenaces, suppressing sudden menaces, menaces states change, targets change and soon.(4) To solve the online cooperative mission planning problem of multi-UCAVsindependent cooperative SEAD, a local optimization model for multi-UCAVs missioncoordination is proposed based on DMPC, and a distributed local optimization algorithm to solve the local optimization model is presented based on COE-MACA andRHC.Based on the hierarchical control, multi-UCAVs independent cooperative SEADmission planning is divided into mission coordination and path planning. To solvemulti-UCAVs mission coordination problem, the mathematical models of mission/targetevaluation and transfer cost estimation between different missions are studied based onthe grey system theory and weighted oriented graph. The strategies to construct andupdate UCAV planning windows are designed, an mathematical local optimizationmodel is proposed and the distributed local optimization algorithm for multi-UCAVsmission coordination is presented under the framework of COE-MACA and RHC.Simulation results shows that the mentioned algorithm can manage the cooperativeconstraints between different UCAVs and missions effectively, the introduction of RHCdecomposes the original problem to reduce the complexity, and the algorithm caneffectively response to the dynamic event such as target increasing, target cancelled,UCAV failure or damage quickly, so that the mentioned model and algorithm cansatisfy the requirement of multi-UCAVs online mission coordination in dynamicenvironment.