UCAVs online collaborative path planning method based on dynamic task allocation

Wei, Zhenglei; Huang, Changqiang; Han, Tong; Dong, Kangsheng; Li, Yintong

doi:10.1109/ccdc.2018.8407253

Cited by 2 publications

(1 citation statement)

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“…In [5], a two-stage method for solving the terrain-following (TF)/terrain-avoidance (TA) path-planning problem for UCAV is presented, where the first stage of planning takes an optimization approach for generating a 2D path on a horizontal plane with no collision with the terrain, and in the second stage of planning, an optimal control approach is adopted to generate a 3D flyable path for the UCAV that is as close as possible to the terrain. In [6], the online collaborative path-planning method based on dynamic task allocation is proposed. However, they always suffer from the weakness of UCAVs' real-time response capability due to the high complexity.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

Zhu

Wang

et al. 2021

Mathematics

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Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks a superior flight path in a complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability to address complex optimization problems. However, different SI algorithms present various performances for UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of different SI algorithms for UCAV path-planning research. In the experiment, twelve algorithms that published in major journals and conference proceedings are surveyed and then applied to UCAV path-planning. Moreover, to demonstrate the performance of different algorithms in further, we design different scales of problem cases for those comparative algorithms. The experimental results show that UCAV can find the safe path to avoid the threats efficiently based on most SI algorithms. In particular, the Spider Monkey Optimization is more effective and robust than other algorithms in handling the UCAV path-planning problem. The analysis from different perspectives contributes to highlight trends and open issues in the field of UCAVs.

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Section: Introductionmentioning

confidence: 99%