Real-time multi-agent collision-avoidance algorithms comprise a key enabling technology for the practical use of self-organising swarms of drones. This paper proposes a decentralised reciprocal collision-avoidance algorithm, which is based on stigmergy and scalable. The algorithm is computationally inexpensive, based on the gradient of the locally measured dynamic cumulative signal strength field which results from the signals emitted by the swarm. The signal strength acts as a repulsor on each drone, which then tends to steer away from the noisiest regions (cluttered environment), thus avoiding collisions. The magnitudes of these repulsive forces can be tuned to control the relative importance assigned to collision avoidance with respect to the other phenomena affecting the agent's dynamics. We carried out numerical experiments on a self-organising swarm of drones aimed at fighting wildfires autonomously. As expected, it has been found that the collision rate can be reduced either by decreasing the cruise speed of the agents and/or by increasing the sampling frequency of the global signal strength field. A convenient by-product of the proposed collision-avoidance algorithm is that it helps maintain diversity in the swarm, thus enhancing exploration.

中文翻译：

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基于 Stigmergy 的自组织群碰撞避免算法

实时多智能体防撞算法是自组织无人机群实际使用的关键技术。本文提出了一种基于stigmergy和可扩展性的去中心化互易碰撞避免算法。该算法基于本地测量的动态累积信号强度场的梯度，计算成本低，该梯度是由群发出的信号产生的。信号强度作为每架无人机的排斥器，然后倾向于避开最嘈杂的区域（杂乱的环境），从而避免碰撞。可以调整这些排斥力的大小，以控制分配给碰撞避免相对于影响代理动力学的其他现象的相对重要性。我们对旨在自主扑灭野火的自组织无人机群进行了数值实验。正如预期的那样，已经发现可以通过降低代理的巡航速度和/或通过增加全局信号强度场的采样频率来降低​​碰撞率。所提出的避免碰撞算法的一个方便的副产品是它有助于保持群中的多样性，从而增强探索。