Multiple unmanned aerial vehicle (Multi-UAV) cooperative area search is an important and effective means of intelligence acquisition and disaster rescue. Search path planning is a critical factor to improve multi-UAV ...
详细信息
Multiple unmanned aerial vehicle (Multi-UAV) cooperative area search is an important and effective means of intelligence acquisition and disaster rescue. Search path planning is a critical factor to improve multi-UAV search performance. Aiming at the search inefficiency resulting from insufficient cooperation between UAVs in existing researches, we present a novel distributed real-time search path planning method based on distributed model predictive control (DMPC) framework. Firstly, we formulate the overall search objective function in finite time domain, considering not only repeated searches, but also maintenance of connectivity and collision avoidance between UAVs. Secondly, we decompose the overall search objective function to establish a distributed constrained optimization problem (DCOP) model, so that all UAVs optimize the overall search objective by interacting with neighbors. Thirdly, aiming at the problem of falling into the local optima in existing algorithms, distributed stochastic algorithm based on enhanced genetic algorithm (DSA-EGA) is proposed to solve the established DCOP model. We design a point crossover operator and introduce anytime local search (ALS) framework that stores the global optimal solution explored. Finally, the simulation results of different benchmark problems demonstrate that the proposed DSA-EGA outperforms other state-of-the-art algorithms in terms of the quality of solution. The simulation results of cooperative area search problems illustrate that the established DCOP model improves the search efficiency by 7.7%, and DSA-EGA improves the search efficiency by 4.3% at least. In addition, we also verify that our method has high scalability.
In this paper, a distributed constrained optimization problem for discrete -time multi -agent systems with timevarying directed graphs is studied and a distributed projection subgradient algorithm is proposed. Further...
详细信息
In this paper, a distributed constrained optimization problem for discrete -time multi -agent systems with timevarying directed graphs is studied and a distributed projection subgradient algorithm is proposed. Furthermore, we investigate the scenario involving time delays in agent interactions and use an augmented matrix method to deal with delays. Specifically, we introduce virtual nodes along with delay edges to effectively transform the system with delays into an equivalent augmented system. It is shown that for our considered systems the agents' estimates obtained by this algorithm converge to the same optimal solution. Finally, simulations examples are given to verify the effectiveness of the proposed algorithm.
暂无评论