A Virtual Motion Camouflage Approach for Cooperative Trajectory Planning of Multiple UCAVs

Abstract

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.

Unmanned aerial vehicle trajectory planning by an integrated algorithm in a complex obstacle environment

“…In this simulation, the efficiency of the integrated algorithm and traditional optimal control method by using individual LPM as an example is provided. In order to utilize the optimal control problem to solve the trajectory planning in obstacle-rich environment, the p-norm method 29 is applied by ignoring the accuracy when describing the obstacles approximately. For the problem of describing the constraints of obstacles, the cubed-shaped obstacles are expressed by the following equation where h represents the obstacle constraints, and the definitions of other parameters are the same with Gu et al.…”

“…For the problem of describing the constraints of obstacles, the cubed-shaped obstacles are expressed by the following equation where h represents the obstacle constraints, and the definitions of other parameters are the same with Gu et al. 29 The p-norm is defined as follows …”

“…where h represents the obstacle constraints, and the definitions of other parameters are the same with Gu et al 29 The p-norm is defined as follows It is worth noting that the p-norm method can describe the simple-shaped obstacles such as circles, rectangles and ellipses etc. Therefore, in order to utilize the p-norm method, three rectangles-shaped obstacles are used to implement the simulation.…”

“…In Xu and Gareth, 27 the VMC method is applied to solve the trajectory planning problem for 2D circle-shaped obstacle. In Gu et al., 29 the VMC method is utilized to solve the cooperative trajectory planning with obstacle avoidance. The obstacles are described by the p-norm method similarly.…”

A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

“…To demonstrate the efficiency of the proposed method, we compare this method with other existing methods. There are several methods to estimate the flight paths of UAVs, such as the straight line from the initial positions to the goal positions (straight line as the initial solution, SI) [35], the B-spline curves-based method (B-spline curves as the initial solution, BI) [36], and the vertical section-based method (VI) [7]. Our proposed method is defined as CI.…”