%0 Journal Article
%T A Virtual Motion Camouflage Approach for Cooperative Trajectory Planning of Multiple UCAVs
%A Xueqiang Gu
%A Lincheng Shen
%A Jing Chen
%A Yu Zhang
%A Wanpeng Zhang
%J Mathematical Problems in Engineering
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/748974
%X This paper investigates cooperative trajectory planning of multiple unmanned combat aerial vehicles (multi-UCAV) in performing autonomous cooperative air-to-ground target attack missions. By integrating an approximate allowable attack region model, several constraint models, and a multicriterion objective function, the problem is formulated as a cooperative trajectory optimal control problem (CTOCP). Then, a virtual motion camouflage (VMC) for cooperative trajectory planning of multi-UCAV, combining with the differential flatness theory, Gauss pseudospectral method (GPM), and nonlinear programming, is designed to solve the CTOCP. In particular, the notion of the virtual time is introduced to the VMC problem formulation to handle the temporal cooperative constraints. The simulation experiments validate that the CTOCP can be effectively solved by the cooperative trajectory planning algorithm based on VMC which integrates the spatial and temporal constraints on the trajectory level, and the comparative experiments illustrate that VMC based algorithm is more efficient than GPM based direct collocation method in tackling the CTOCP. 1. Introduction Nowadays, it is an active research area to perform autonomous cooperative air-to-ground target attack (CA/GTA) missions using multiple unmanned combat aerial vehicles (multi-UCAV) [1]. However, compared with single UCAV planning and coordinated formation control problems [2], new technical challenges in the CA/GTA missions are emerging. The cooperative trajectory planning is one of the key challenging technologies, due to its high dimensionality, severe equality and inequality constraints involved, and the requirement of spatial-temporal cooperation of multi-UCAV. Recently, various algorithms have been developed to solve this cooperative trajectory planning problem [3, 4], including artificial neural network methods [5], sample-based planning methods [6], maneuver automation (MA) [7], and optimal control methods. There is no doubt that the optimal control theory is the most natural framework for this type of problem with dynamic constraints [8]. However, the rapid solution to optimal control problems (OCPs) for complicated nonlinear systems, such as UCAVs, is a challenging task [9]. Analytical solutions are seldom available or even possible. As a result, one usually resorts to numerical techniques [3]. The techniques can be classified into two general types, namely, indirect and direct methods. Indirect methods [10] solve the OCPs by formulating the first-order optimality conditions, applying PontryaginĄ¯s Maximum
%U http://www.hindawi.com/journals/mpe/2014/748974/