In attempting to build neurorobotic systems based on flying animals, engineers have come to rely on existing firmware and simulation tools designed for miniature aerial vehicles (MAVs). Although they provide a valuable platform for the collection of data for Deep Learning and related AI approaches, such tools are deliberately designed to be general (supporting air, ground, and water vehicles) and feature-rich. The sheer amount of code required to support such broad capabilities can make it a daunting task to adapt these tools to building neurorobotic systems for flight. In this paper we present a complementary pair of simple, object-oriented software tools (multirotor flight-control firmware and simulation platform), each consisting of a core of a few thousand lines of C++ code, that we offer as a candidate solution to this challenge. By providing a minimalist application programming interface (API) for sensors and PID controllers, our software tools make it relatively painless for engineers to prototype neuromorphic approaches to MAV sensing and navigation. We conclude our discussion by presenting a simple PID controller we built using the popular Nengo neural simulator in conjunction with our flight-simulation platform.

在尝试建立基于飞行动物的神经机器人系统时，工程师们开始依赖为微型飞行器（MAV）设计的现有固件和仿真工具。尽管它们为深度学习和相关AI方法的数据收集提供了宝贵的平台，但这些工具还是经过精心设计的，具有通用性（支持空中，地面和水上交通工具）且功能丰富。支持如此广泛的功能所需的代码量之多，使这些工具适应构建飞行的神经机器人系统成为一项艰巨的任务。在本文中，我们提出了一对互补的简单的，面向对象的软件工具（多旋翼飞行控制固件和仿真平台），每个工具都包含数千行C ++代码的核心，我们可以以此为候选解决方案挑战。通过为传感器和PID控制器提供一个极简的应用程序编程接口（API），我们的软件工具使工程师能够相对轻松地设计出用于MAV感应和导航的神经形态方法的原型。通过介绍一个简单的PID控制器来结束我们的讨论，该PID控制器是使用流行的Nengo神经模拟器与我们的飞行模拟平台一起构建的。