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本文引用的文献

1
Solving nonlinear equality constrained multiobjective optimization problems using neural networks.利用神经网络解决非线性等式约束多目标优化问题。
IEEE Trans Neural Netw Learn Syst. 2015 Oct;26(10):2500-20. doi: 10.1109/TNNLS.2015.2388511. Epub 2015 Jan 30.

一种基于分解协调方法的自主无人机有效且安全的轨迹规划

Effective and Safe Trajectory Planning for an Autonomous UAV Using a Decomposition-Coordination Method.

作者信息

Nizar Imane, Jaafar Adil, Hidila Zineb, Barki Mohamed, Illoussamen El Hossein, Mestari Mohammed

机构信息

Laboratory SSDIA, École Normale Supérieure de l'Enseignement Technique (ENSET) Mohammedia 20800, University Hessan II, Casablanca, Morocco.

出版信息

J Intell Robot Syst. 2021;103(3):50. doi: 10.1007/s10846-021-01467-2. Epub 2021 Oct 27.

DOI:10.1007/s10846-021-01467-2
PMID:34720405
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8549418/
Abstract

In this paper, we present a Decomposition Coordination (DC) method applied to solve the problem of safe trajectory planning for autonomous Unmanned Aerial Vehicle (UAV) in a dynamic environment. The purpose of this study is to make the UAV more reactive in the environment and ensure the safety and optimality of the computed trajectory. In this implementation, we begin by selecting a dynamic model of a fixed-arms quadrotor UAV. Then, we define our multi-objective optimization problem, which we convert afterward into a scalar optimization problem (SOP). The SOP is subdivided after that into smaller sub-problems, which will be treated in parallel and in a reasonable time. The DC principle employed in our method allows us to treat non-linearity at the local level. The coordination between the two levels is achieved after that through the Lagrange multipliers. Making use of the DC method, we can compute the optimal trajectory from the UAV's current position to a final target practically in real-time. In this approach, we suppose that the environment is totally supervised by a Ground Control Unit (GCU). To ensure the safety of the trajectory, we consider a wireless communication network over which the UAV may communicate with the GCU and get the necessary information about environmental changes, allowing for successful collision avoidance during the flight until the intended goal is safely attained. The analysis of the DC algorithm's stability and convergence, as well as the simulation results, are provided to demonstrate the advantages of our method and validate its potential.

摘要

在本文中,我们提出一种分解协调(DC)方法,用于解决动态环境下自主无人机(UAV)的安全轨迹规划问题。本研究的目的是使无人机在环境中更具反应能力,并确保计算出的轨迹的安全性和最优性。在这个实现过程中,我们首先选择一个固定臂四旋翼无人机的动态模型。然后,我们定义我们的多目标优化问题,随后将其转化为一个标量优化问题(SOP)。之后,SOP被细分为更小的子问题,这些子问题将在合理的时间内并行处理。我们方法中采用的DC原理使我们能够在局部层面处理非线性问题。之后,通过拉格朗日乘数实现两个层面之间的协调。利用DC方法,我们几乎可以实时地从无人机的当前位置计算出到最终目标的最优轨迹。在这种方法中,我们假设环境完全由地面控制单元(GCU)监控。为确保轨迹的安全性,我们考虑一个无线通信网络,无人机可以通过该网络与GCU通信,并获取有关环境变化的必要信息,从而在飞行过程中成功避免碰撞,直到安全到达预定目标。提供了DC算法的稳定性和收敛性分析以及仿真结果,以证明我们方法的优点并验证其潜力。