Potato: A Data-Oriented Programming 3D Simulator for Large-Scale Heterogeneous Swarm Robotics

TL;DR

Potato is a GPU-accelerated, data-oriented 3D simulator for large-scale heterogeneous swarm robotics, enabling efficient simulation of nonlinear dynamic models with high node counts.

Contribution

The paper introduces a novel Data-Oriented Programming based simulator leveraging GPU parallelism for large-scale heterogeneous swarm robotics simulation.

Findings

Supports up to 5,000 quadrotors with real-time performance

Uses PyTorch and GPU for homogeneous agent simulation

Demonstrates functionality with two example scenarios

Abstract

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.