高保真多无人机仿真器，支持深度强化学习与控制算法测试。
MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning
- 基于MuJoCo引擎，可模拟任意数量纳米无人机的物理行为。
- 支持多种动力学模型和观测方式，适配不同控制任务需求。
- 兼容PettingZoo，适合多智能体强化学习研究者快速上手。
机器人仿真器是现代空中机器人研究的核心，既用于开发新控制算法，也为深度强化学习提供训练数据。现有四轴飞行器学习环境常在物理精度、多智能体支持与深度强化学习所需吞吐量之间权衡。本文提出MuJoCo-Drones-Gym，一个开源的Gymnasium兼容多无人机环境，基于MuJoCo物理引擎构建。该环境支持任意数量的Bitcraze Crazyflie 2.x纳米四轴飞行器，并提供模块化API，可选择（一）物理模型（刚体MuJoCo、Python显式动力学，或地面效应、桨叶阻力、机间下洗流等子集），（二）动作接口（单电机转速、归一化总推力、速度设定点或PID航点指令），（三）观测空间（运动状态向量、RGB/深度/分割摄像头、邻近关系信息）。通过PettingZoo ParallelEnv包装器，可直接用于多智能体强化学习；配套七种任务环境——悬停、速度跟踪、多机悬停、航点导航、编队飞行、穿越门禁、通用多智能体模板——展示其功能广度。文中阐述环境设计、底层动力学与四轴飞行器建模方法，并通过控制与学习示例验证其有效性，相较于同类gym-pybullet-drones项目，利用MuJoCo更优的接触处理、渲染能力和并行性优势。
原文摘要 · Abstract (English)
Robotic simulators are a cornerstone of modern research in aerial robotics, serving both as a vehicle for the development of new control algorithms and as the data source for training reinforcement learning (RL) policies. Yet, existing quadcopter learning environments often face a trade-off between physical fidelity, multi-agent support, and the throughput required by modern deep RL pipelines. In this paper, we present MuJoCo-Drones-Gym, an open-source Gymnasium-compatible multi-drone environment built on top of the MuJoCo physics engine. MuJoCo-Drones-Gym supports an arbitrary number of Bitcraze Crazyflie 2.x nano-quadcopters and exposes a modular API for selecting (i)~the physics model (rigid-body MuJoCo, explicit Python dynamics, or any subset of ground effect, blade drag, and inter-drone downwash), (ii)~the action interface (per-motor RPMs, collective normalized thrust, velocity setpoints, or PID waypoint commands), and (iii)~the observation space (kinematic state vectors, RGB / depth / segmentation cameras, or neighbourhood adjacency information). A PettingZoo ParallelEnv wrapper enables drop-in multi-agent reinforcement learning, while a suite of seven task environments, hover, velocity tracking, multi-drone hover, waypoint navigation, formation flight, gate racing, and a generic multi-agent template, demonstrates the breadth of the interface. We describe the environment design, the underlying physics and quadcopter dynamics, and illustrate its use through control and learning examples that mirror those of the closely related gym-pybullet-drones project, while taking advantage of MuJoCo's improved contact handling, rendering, and parallelizability.
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