Learning how to avoid obstacles: A numerical investigation for maneuvering of self-propelled fish based on deep reinforcement learning,International Journal for Numerical Methods in Fluids

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Learning how to avoid obstacles: A numerical investigation for maneuvering of self-propelled fish based on deep reinforcement learning
International Journal for Numerical Methods in Fluids ( IF 1.7 ) Pub Date : 2021-06-29 , DOI: 10.1002/fld.5025
Lang Yan ₁ , Xinghua Chang ₂ , Nianhua Wang ₃ , Runyu Tian ₄ , Laiping Zhang ₂ , Wei Liu ₁

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The self-propelled fish maneuvering for avoiding obstacles under intelligent control is investigated by numerical simulation. The NACA0012 airfoil is adopted as the two-dimensional fish model. To achieve autonomous cruising of the fish model in a complex environment with obstacles, a hydrodynamics/kinematics coupling simulation method is developed with artificial intelligence (AI) control based on deep reinforcement learning (DRL). The Navier–Stokes (NS) equations in the arbitrary Lagrangian–Eulerian (ALE) framework are solved by the dual-time stepping approach, which is coupled with the kinematics equations in an implicit strong coupling way. Moreover, the moving mesh based on radial basis function and overset grid technology is taken to achieve a wide range of maneuvering. DRL is introduced into the coupling simulation platform for intelligent control of obstacle avoidance when the self-propelled fish swimming. Three cases are tested to validate the novel approach, including the fish model maneuvering to avoid a single obstacle and double or multiple obstacles. The results indicate that the fish model can avoid obstacles in a complex environment under intelligent control. This work illustrates the possibility of producing navigation algorithms by DRL and brings potential applications of bionic robotic swarms in engineering.

中文翻译：

学习如何避开障碍物：基于深度强化学习的自航鱼机动数值研究

通过数值模拟研究了智能控制下自航鱼避障机动。二维鱼模型采用NACA0012翼型。为实现鱼类模型在复杂的障碍环境中自主巡航，基于深度强化学习（DRL）的人工智能（AI）控制，开发了一种流体动力学/运动学耦合仿真方法。任意拉格朗日-欧拉 (ALE) 框架中的 Navier-Stokes (NS) 方程通过双时间步进方法求解，该方法以隐式强耦合方式与运动学方程耦合。此外，采用基于径向基函数的动网格和重叠网格技术，实现了大范围的机动。将DRL引入耦合仿真平台，实现自航鱼游动避障智能控制。测试了三个案例来验证新方法，包括鱼模型机动避开单个障碍物和双重或多个障碍物。结果表明，该鱼模型在智能控制下能够在复杂环境中避开障碍物。这项工作说明了通过 DRL 生成导航算法的可能性，并带来了仿生机器人群在工程中的潜在应用。结果表明，该鱼模型在智能控制下能够在复杂环境中避开障碍物。这项工作说明了通过 DRL 生成导航算法的可能性，并带来了仿生机器人群在工程中的潜在应用。结果表明，该鱼模型在智能控制下能够在复杂环境中避开障碍物。这项工作说明了通过 DRL 生成导航算法的可能性，并带来了仿生机器人群在工程中的潜在应用。

更新日期：2021-09-06

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