Reward-modulated Spiking Neural Network for Self-organizing Collision Avoidance of Drone Swarm

This repository contains code from our paper [Nature-inspired Self-organizing Collision Avoidance for Drones Swarm Based on Reward-modulated Spiking Neural Network] published in Cell Patterns. https://www.cell.com/patterns/fulltext/S2666-3899(22)00236-7

We also provide the BrainCog-based version: https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/decision_making/swarm

If you use our code or refer to this project, please cite this paper: Feifei Zhao,Yi Zeng, Bing Han, Hongjian Fang, and Zhuoya Zhao. Nature-inspired Self-organizing Collision Avoidance for Drones Swarm Based on Reward-modulated Spiking Neural Network. Patterns, DOI：https://doi.org/10.1016/j.patter.2022.100611

Paper Introduction

The collaborative interaction mechanisms of biological swarms in nature are of great importance to inspire the study of swarm intelligence. This paper proposed a self-organizing obstacle avoidance model by drawing on the decentralized, self-organizing properties of intelligent behavior of biological swarms. Each individual independently adopts brain-inspired reward-modulated spiking neural network (RSNN) to achieve online learning and makes decentralized decisions based on local observations. The following picture shows the decision-making process of our model.

<center><img src="./process.jpg" width="50%"></center>

We validated the proposed model on swarm collision avoidance tasks (a swarm of unmanned aerial vehicles without central control) in a bounded space, carrying out simulation and real-world experiments. The drone swarm emerges with safe flight behavior, as shown in the following videos. Compared with artificial neural network-based online learning methods, our proposed method exhibits superior performance and better stability.

<img src="./simulation.gif" alt="mt" width="55%" /><img src="./figures/joy.gif" alt="mt" width="55%" /> <img src="./collision_avoidance.gif" alt="mt" width="55%" /><img src="./figures/joy.gif" alt="mt" width="55%" />

Run

• "reward-modulated snn on swarm collision avoidance.py" includes the self-organized collision avoidance implemented by RSNN for simulation scenarios.
• "flytestfive.py" includes five UAVs swarm collision avoidance validation in real bounded scenario.

Requirments

• "reward-modulated snn on swarm collision avoidance.py": python==3.7, numpy>=1.21.6
• "flytestfive.py": multi_robomaster