Purpose The purpose of this paper is to develop a distributed task allocation method for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) based on the consensus algorithm and the online cooperative strategy. Design/methodology/approach In this paper, the allocation process is conducted in a distributed framework. The cooperative task allocation problem is proposed with constraints and uncertainties in a real mission. The algorithm based on the consensus algorithm and the online cooperative strategy is proposed for this problem. The local chain communication mode is adopted to restrict the bandwidth of the communication link among the UAVs, and two simulation tests are given to test the optimality and rapidity of the proposed algorithm. Findings This method can handle both continuous and discrete uncertainties in the mission space, and the proposed algorithm can obtain a feasible solution in allowable time. Research limitations/implications This study is only applied to the case that the total number of the UAVs is less than 15. Practical implications This study is expected to be practical for a real mission with uncertain targets. Originality/value The proposed algorithm can go beyond previous works that only deal with continuous uncertainties, and the Bayesian theorem is adopted for estimation of the target.

Purpose -The purpose of this paper is to study the closed-loop guidance algorithm for launch vehicles in an atmospheric ascent phase and present a numerical trajectory reconstruction algorithm to satisfy the real-time requirement of generating the guidance commands. Design/methodology/approach -An optimal control model for an atmospheric ascent guidance system is established directly; following that, the detailed process for necessary conditions of the optimal control problem is re-derived based on the calculus of variations. As a result, the trajectory optimization problem can be reduced to a root-finding problem of algebraic equations based on the finite element method (FEM). To obtain an accurate solution, the Newton method is introduced to solve the roots in a guidance update cycle. Findings -The presented approach can accurately and efficiently solve the trajectory optimization problems. A moderate number of unknowns can yield a good optimal solution, which is well suited for the open-loop guidance. To meet the requirements of the rapidity and accuracy for the close-loop guidance, the fewer number of unknowns is artificially chosen to reduce the calculation time, and the on-board trajectory planning strategy can increase the precision of the optimal solution along with the decrease of time-to-go. Practical implications -The closed-loop guidance algorithm based on an FEM can be found in this paper, which can solve the optimal ascent guidance problems for launch vehicles in the atmospheric flight phase rapidly, accurately and efficiently. Originality/value -This paper re-derives the necessary conditions of the optimal solution in a different way compared to the previous work, and the closed-loop guidance algorithm combined with the FEM is also a new thought for the optimal atmospheric ascent guidance problems.

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Most existing distributed state estimation approaches assume that the position of agent in multi-agent system (MAS) is precisely known. However, in target tracking applications, the position of agent is noisy, which makes traditional distributed state estimation methods suffer from accuracy reduction. This paper focuses on developing a novel hybrid consensus-based variational Bayesian filter (HCVB) for target tracking with position uncertainty in nonlinear MAS. To improve the nonlinear approximation accuracy, high-degree cubature information filter framework is used. To cope with agent position uncertainty, the measurement update is derived by using variational Bayesian (VB) method to approximate the joint posterior distribution of the target state and agent position, which can estimate the state and agent position jointly. To decrease the required consensus iterations, a hybrid consensus method is proposed, which can achieve global consistency of all agents with a few consensus iterations. Simulations on UAV tracking in MAS verify the superiority of the proposed method.