Research On Resources Allocation And Formation Trajectories Optimization For Multiple UAVs Cooperation Mission

Mission planning for multiple Unmanned Aerial Vehicles (UAV) cooperative operation is one of the focuses to exert the advantages of cooperation of multiple UAVs. But multi-UAV systems in battle fields have to confront the uncertainty of information, the complexity of mathematical model, and the pressure of computing time. Under the background of multiple UAVs cooperative ground attack mission including multiple UAVs, multiple missions, and multiple targets, this dissertation applies closed-loop control system theory to abstracting of mission planning problem, focuses on the resource allocation problem and formation trajectory optimization problem, builds mathematical model, researches optimization theory, designs algorithm, carries out simulation and experiments.(1) The dynamic resources allocation model for multiple UAVs cooperative mission with partially observable targets states is presented. The basic tasks in multiple UAVs ground attack mission are defined, so as to describe the execution effects, time and resource properties about different tasks. Under the framework of Partially Observable Markov Decision Processes (POMDP), this paper takes the targets set to be struck as a controllable system, the strike tasks as system inputs, the assessment tasks as states feedback, and combines the finity of resources, to build a dynamic system optimization model under partially observable environment. The proposed model reveals inner relationship between strike and assessment tasks, coequally assigns fire and information resources in multiple UAVs system, and preferably describes the execution effects of tasks and information of targets states with time delay and uncertainty during operation process. Compared with traditional static models, the model overcomes the disadvantage of adapting to dynamic targets states changes.(2) The trajectory optimization model for multiple UAVs formation flying with loose structure is suggested. The process of multiple UAVs formation flying is divided into two stages, i.e. formation configuration stage and formation maintaining stage. Then, the UAV kinematics equation is linearized based on the Lie derivative, and the communication topology is described by graph model. On this basis, the optimal control theory is used for reference to build optimization models for two stages. For formation configuration stage, the trajectory optimization model with free terminal constraints is build, so as to reduce over all energy consumption by properly choosing a consensus point. For formation maintaining stage, the local and global index functions are defined respectively to achieve the control objectives about reducing energy consumption, follow reference path, and maintaining formation structure at the same time.(3) Starting from common feature of the resource allocation model and the formation trajectory optimization model, the adaptability of decomposition optimization method for weakly coupled dynamic system is expended, and corresponding theorems with theoretical proof is presented. Relation between cooperative behaviors of multiple UAVs and the coupling of mathematic model is analyzed thoroughly, then the convex optimization theory based decomposition methods are introduced to achieve the decoupling of problems. For the features about networks of distributed computing, the equivalent augmented network model without time delay of networks with time delay is build, and the augmented optimization problem is defined. Then, starting from the convergence of network nodes information transition matrix, the consistency and convergence of distributed asynchronous subgradient algorithm running in networks with local connection and time varying topology, and with time delay is theoretically proofed. For weakly coupled stochastic dynamic programming problem with partially observable system states, the dual function is defined on the basis of POMDP value iteration equation with constraints. Then, under premise of independency of each sub system's initial belief state, the separability of dual function is theoretically proofed. Finally, the problem is decoupled by dual decomposition method, and construction method for subgradient of dual function is given.(4) The dynamic resources allocation algorithm, which combines off-line optimization with on-line decision algorithm, is presented. Dynamic resource assignment model for single target under ideal conditions is build on the basis of infinite horizon POMDP. The model is conversed into non-linear integral programming problem based on properties of optimal stationary policy, and the resolve algorithm is designed. Dynamic resource assignment model for single target under general conditions is build on the basis of finite horizon POMDP. The improved linear support algorithm is proposed, and the controllable property of solution precision of the algorithm is proofed. Resources allocation process for multiple targets is divided into two stages, i.e. off-line optimization stage and on-line decision stage. In off-line optimization stage, the dual decomposition method is used to decompose the problem into multiple POMPD sub-problems for single targets, and costs of resources are used to coordinate expectation resources consumption of sub-problem optimal policies. In on-line decision stage, policies obtained in off-line stage and practical information gathered during tasks execution were took into account, in order to decide tasks to be executed for all targets based on greedy principle. Simulation results indicate that, the proposed method overcomes the disadvantage brought by uncertainty of targets states, and meets the demand of real time decision.(5) The negotiation based distributed multiple UAVs formation trajectory optimization algorithm is presented. For variable coupling in trajectory optimization model of formation configuration stage, and index coupling in trajectory optimization model of formation maintaining stage, the primal decomposition method and indirect decomposition method is used to decouple the models respectively. The properties of master problems in coordination level obtained after decomposition of above models were proofed. On this basis, the construction methods for subgradients of index function in master problem is proposed, and the negotiation based distributed trajectory optimization algorithm is designed. In formation configuration stage, each UAV resolves its own optimal trajectory with given terminal states independently, gets alteration direction of consensus point to its advantage, and then updates and sends the point to its neighbors. After repeatedly negotiations, consensus points proposed by all UAVs consistence and convergent to the optimal solution. In formation maintaining stage, each UAV resolves its own optimal trajectory independently under control of dual variables, then updates and sends dual variables to its neighbors. After repeatedly negotiations, each UAV achieve to maintaining formation geometrical structure while optimize its local index. Simulation and experiments results indicate that, the proposed method has good computational efficiency and optimization capabilities, achieves performance improvements obviously under given index function, and supports multiple UAVs formation trajectory optimization effectively.