Bio-Inspired Self-Organizing Collision Avoidance for Drones Swarm Based on Reward-Modulated Spiking Neural Network

Abstract

Biological systems can emerge intelligent swarm behavior through relatively simple individual, local interaction, and decentralized self-organizing decisionmaking. A major research challenge of self-organized swarm intelligence is the coupling influences between individual behaviors. Numerous swarm decisionmaking methods optimize the behavior of multiple individuals at the same time from a global perspective. Therefore, these methods are difficult to be used for online decision-making tasks of the real multi-robots system. This paper proposes a self-organized collision avoidance model for real drones incorporating a bio-inspired reward-modulated spiking neural network. Each individual independently uses the brain-inspired spiking neural network to update its own strategy according to the behavior of other agents. Subsequently, the local interaction and autonomous learning of a single individual lead to the emergence of swarm intelligence. We validated our model on swarm collision avoidance tasks in a bounded space, carrying out simulation and real-world experiments with a self-organized swarm of drones. Experimental results demonstrated that the swarm of UAVs without central control can exhibit collision avoidance even around perturbing obstacles.