MuJoCo-Drones-Gym: 用于控制和强化学习的GPU加速多无人机模拟器
MuJoCo-Drones-Gym: A GPU-Accelerated Multi-Drone Simulator for Control and Reinforcement Learning
AI总结：
提出基于MuJoCo物理引擎的GPU加速多无人机模拟器MuJoCo-Drones-Gym，支持任意数量Crazyflie 2.x纳米四旋翼，提供模块化物理模型、动作接口和观测空间，集成PettingZoo多智能体强化学习，涵盖悬停、速度跟踪等七种任务环境。
摘要：
机器人模拟器是现代空中机器人研究的基石，既作为新控制算法开发的工具，也作为训练强化学习策略的数据源。然而，现有的四旋翼学习环境通常在物理保真度、多智能体支持和现代深度强化学习管道所需吞吐量之间面临权衡。本文提出MuJoCo-Drones-Gym，一个基于MuJoCo物理引擎构建的开源Gymnasium兼容多无人机环境。MuJoCo-Drones-Gym支持任意数量的Bitcraze Crazyflie 2.x纳米四旋翼，并暴露模块化API用于选择：(i)物理模型（刚体MuJoCo、显式Python动力学，或地面效应、桨叶阻力和无人机间下洗流的任意子集），(ii)动作接口（每电机RPM、集体归一化推力、速度设定点或PID航点命令），以及(iii)观测空间（运动状态向量、RGB/深度/分割相机或邻域邻接信息）。PettingZoo ParallelEnv封装支持即插即用的多智能体强化学习，而一套七种任务环境——悬停、速度跟踪、多无人机悬停、航点导航、编队飞行、门赛竞速和通用多智能体模板——展示了接口的广度。我们描述了环境设计、底层物理和四旋翼动力学，并通过与密切相关项目gym-pybullet-drones相似的控制和学习示例说明其使用，同时利用MuJoCo改进的接触处理、渲染和并行化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
