Nature-inspired self-organizing collision avoidance for drone swarm based on reward-modulated spiking neural network

Feifei Zhao; Yi Zeng; Bing Han; Hongjian Fang; Zhuoya Zhao

doi:10.1016/j.patter.2022.100611

Nature-inspired self-organizing collision avoidance for drone swarm based on reward-modulated spiking neural network

Patterns (N Y). 2022 Oct 28;3(11):100611. doi: 10.1016/j.patter.2022.100611. eCollection 2022 Nov 11.

Authors

Feifei Zhao¹, Yi Zeng^{1

2

3

4

5}, Bing Han^{1

4}, Hongjian Fang^{1

3}, Zhuoya Zhao^{1

3}

Affiliations

¹ Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
² National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
³ School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100049 China.
⁴ School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049 China.
⁵ Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.

Abstract

Biological systems can exhibit intelligent swarm behavior through relatively independent individual, local interaction and decentralized decision-making. A major research challenge of self-organized swarm intelligence is the coupling influences between individual behaviors. Existing methods optimize the behavior of multiple individuals simultaneously from a global perspective. However, these methods lack in-depth inspiration from swarm behaviors in nature, so they are short of flexibly adapting to real multi-robot online decision-making tasks. To overcome such limits, this paper proposes a self-organized collision avoidance model for real drones incorporating a bio-inspired reward-modulated spiking neural network (RSNN). The local interaction and autonomous learning of a single individual leads to the emergence of swarm intelligence. 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. Compared with artificial neural network-based online learning methods, our proposed method exhibits superior performance and better stability.

Keywords: decentralized decision-making; local interactions; reward-modulated spiking neural network; self-organizing collaboration; swarm intelligence emergence.