Learning how to avoid obstacles: A numerical investigation for maneuvering of self‐propelled fish based on deep reinforcement learning

摘要

Abstract 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.