Real-time decentralized cooperative robust trajectory planning for multiple UCAVs air-to-ground target attack

Abstract

Planning cooperative trajectories in real time is of great importance for multiple unmanned combat aerial vehicles (multi-UCAV) in performing autonomous time-critical cooperative air-to-ground target attack missions. The models of the vehicle, constraints, and a multi-criteria objective function are set up, and then the problem is formulated as a decentralized cooperative receding horizon optimal control problem. An elaborate framework for provable effective decentralized cooperative trajectory planning in two-degree-of-freedom decentralized receding horizon control is presented to provide the robustness and adaptivity for trajectory planning in dynamic and uncertain environments. Furthermore, a two-stage iterative optimization algorithm is developed to solve the problem. The novel algorithm integrates the differential flatness theory to generate trajectories, and the coordination variable-based strategy to coordinate the arrival time of multi-UCAV online, which can improve the computational efficiency and achieve the cooperative objective. The results of numerical simulations show that the proposed approach is feasible and well suited for the real-time implementation in dynamic and uncertain environment.

Robust aircraft control based on UDE theory

“…The mathematical representation of the discrete gust as given in Roskam, 39 Karimi and Pourtakdoust 40 and Gu et al. 41 is The influence of the vertical wind field to the aircraft motion is reflected in the change of angle of attack of the aircraft and is given as 42,43 where and V m = 60 m/s represents the gust amplitude, d m = 120 m is the gust length, x is the distance traveled, Vwind is the resultant wind velocity in the body axis frame and V is the speed of the level flight. The start time of the discrete wind gust is at 5 s. In addition to this wind disturbance an external disturbance of 10sin(t)deg/s is also considered in all the channels.…”

A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

“…Hence reasonable estimations of the costs of potential trajectories are required. When the trajectory costs are obtained, the task assignment problem could be modeled as a multi-base multi-traveling salesman problem (MBMTSP) by using graph theory [15]. The entry points, task points and exit points are collectively referred to as nodes 1 2…”

“…Yoshiaki Kuwata et al propose a distributed receding horizon mixed integer linear programming method for multiple UAVs to generate trajectories [14]. Gu Xueqiang et al propose a distributed receding horizon planning method [15], without considering the task assignment problem. Intelligent algorithms such as the genetic algorithm have been proposed to solve the multi-UAV trajectory planning problem [16,17].…”

Cooperative Task Assignment and Track Planning For Multi-UAV Attack Mobile Targets

“…However, the optimization of the results cannot be guaranteed while avoiding conflicts between UAVs. Classical centralized algorithms include Hungarian algorithm [3], genetic algorithm [9], particle swarm optimization algorithm [10], ant colony algorithm [11]and state space priority algorithm [12]. This kind of algorithm has the ability to find the global optimal solution, it is suitable for solving small and medium-sized problems.…”