Abstract Animals learn to master their capabilities by trial and error, and with out having any knowledge about their dynamics model and mathematical or physical rules. They use their maximum capabilities in an optimized way. This is the result of millions of years of evolution where the best of different possibilities are kept, and makes us rethink How does the nature perform things?, particularly when natural systems outperform our rigid systems. In this study, inspired by the nature, we developed an innovative algorithm by enhancing an existing reinforcement learning algorithm (proximal policy optimization (PPO)). Our algorithm is capable of learning to control a quad-rotor drone in order to fly. This new algorithm called Bio-inspired Flight Controller (BFC) does not use any conventional controller such as PID or MPC to control the quad-rotor drone. The goal of BFC is to completely replace the conventional controller with a controller that acts in a similar way to the animals where they learn to control their movements. It is capable of stabilizing a quad-copter in a desired point, and following way points. We implemented our algorithm in an AscTec Hummingbird quad-copter simulated in Gazebo, and tested it using different scenarios to fully measure its capabilities.

中文翻译：

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用于四旋翼无人机的创新仿生飞行控制器：四旋翼无人机使用强化学习学习飞行

摘要 动物通过反复试验学习掌握自己的能力，而无需了解其动力学模型和数学或物理规则。他们以优化的方式使用他们的最大能力。这是数百万年进化的结果，其中保留了最好的不同可能性，并让我们重新思考大自然是如何运作的？特别是当自然系统胜过我们严格的系统时。在这项研究中，受大自然的启发，我们通过增强现有的强化学习算法（近端策略优化 (PPO)）开发了一种创新算法。我们的算法能够学习控制四旋翼无人机以进行飞行。这种称为仿生飞行控制器 (BFC) 的新算法不使用任何传统控制器（如 PID 或 MPC）来控制四旋翼无人机。BFC 的目标是用一个控制器来完全取代传统的控制器，该控制器的行为方式与动物学习控制其运动的方式类似。它能够将四轴飞行器稳定在所需的点，并跟随航路点。我们在 Gazebo 中模拟的 AscTec Hummingbird 四轴飞行器中实现了我们的算法，并使用不同的场景对其进行了测试以全面测量其功能。