Potato：一款面向大规模异构群体机器人的数据导向编程3D模拟器
Potato: A Data-Oriented Programming 3D Simulator for Large-Scale Heterogeneous Swarm Robotics
AI总结：
针对现有群体机器人仿真平台无法兼顾真实动力学模型与大规模节点的问题，本文提出基于数据导向编程的3D模拟器Potato，结合多进程与GPU加速的PyTorch实现异构大规模仿真，在5000架以内四旋翼场景下计算速度稳定。
摘要：
具备真实非线性动力学模型的大规模仿真对群体机器人算法开发至关重要。然而现有平台主要基于面向对象编程（OOP）开发，要么采用简单运动学模型以追求大量仿真节点，要么实现真实动力学模型但仿真节点数量有限。本文开发了一款基于数据导向编程（DOP）的模拟器，利用GPU并行计算实现大规模群体机器人仿真。具体而言，我们采用多进程方法仿真异构智能体，并借助搭载GPU的PyTorch仿真大量同构智能体。我们使用非线性四旋翼模型测试了该方法，证明当四旋翼数量少于5000架时，这种DOP方法几乎能保持相同的计算速度。我们还提供了两个示例以展示该平台的功能。
英文摘要：
Large-scale simulation with realistic nonlinear dynamic models is crucial for algorithms development for swarm robotics. However, existing platforms are mainly developed based on Object-Oriented Programming (OOP) and either use simple kinematic models to pursue a large number of simulating nodes or implement realistic dynamic models with limited simulating nodes. In this paper, we develop a simulator based on Data-Oriented Programming (DOP) that utilizes GPU parallel computing to achieve large-scale swarm robotic simulations. Specifically, we use a multi-process approach to simulate heterogeneous agents and leverage PyTorch with GPU to simulate homogeneous agents with a large number. We test our approach using a nonlinear quadrotor model and demonstrate that this DOP approach can maintain almost the same computational speed when quadrotors are less than 5,000. We also provide two examples to present the functionality of the platform.
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。
