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

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.