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首页> 外文会议>IEEE Symposium Series on Computational Intelligence >Brain Emotional Learning-Based Path Planning and Intelligent Control Co-Design for Unmanned Aerial Vehicle in Presence of System Uncertainties and Dynamic Environment
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Brain Emotional Learning-Based Path Planning and Intelligent Control Co-Design for Unmanned Aerial Vehicle in Presence of System Uncertainties and Dynamic Environment

机译：基于大脑情感学习的路径规划和智能控制在系统不确定因素和动态环境存在下无人驾驶飞行器

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This paper proposes a novel intelligent path planning and control co-design for Unmanned Aerial Vehicles (UAVs) in the presence of system uncertainties and dynamic environments. In order to simultaneously handle the uncertainties from both the UAV platform itself and from the environment, a novel biologically-inspired approach based on a computational model of emotional learning in mammalian limbic system is adopted. The methodology, known as Brain Emotional Learning (BEL), is implemented in this application for the first time. Making use of the multi-objective properties and the real-time learning capabilities of BEL, the path planning and control co-design are applied in a synthetic UAV path planning scenario, successfully dealing with the challenges caused by system uncertainties and dynamic environments. A Lyapunov analysis demonstrates the convergence of the co-design, and a set of numerical results illustrate the effectiveness of the proposed approach. Furthermore, it is shown that the low computational complexity of the method guarantees its implementation in real-time applications.
机译：本文提出了在存在系统不确定性和动态环境的情况下为无人驾驶飞行器（无人机）的新颖智能路径规划和控制共同设计。为了同时处理无人机平台本身和环境中的不确定性，采用了一种基于哺乳动物肢体系统在情感学习的计算模型的新型生物学激发方法。该方法，称为大脑情绪学习（BEL），首次在本申请中实施。利用贝尔的多目标属性和实时学习功能，在合成的UAV路径规划场景中应用了路径规划和控制共同设计，成功地处理了系统不确定性和动态环境引起的挑战。 Lyapunov分析表明了共同设计的收敛性，并且一组数值结果说明了所提出的方法的有效性。此外，表明该方法的低计算复杂性保证了其在实时应用中的实现。

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