Minimalistic robot swarms offer a scalable, robust, and cost-effective approach to performing complex tasks with the potential to transform applications in healthcare, disaster response, and environmental monitoring. However, coordinating such decentralised systems remains a fundamental challenge, particularly when robots are constrained in communication, computation, and memory. In our study, individual robots frequently make errors when sensing the environment, yet the swarm can rapidly and reliably reach consensus on the best among $n$ discrete options. We compare two canonical mechanisms of opinion dynamics -- direct-switch and cross-inhibition -- which are simple yet effective rules for collective information processing observed in biological systems across scales, from neural populations to insect colonies. We generalise the existing mean-field models by considering asocial biases influencing the opinion dynamics. While swarms using direct-switch reliably select the best option in absence of asocial dynamics, their performance deteriorates once such biases are introduced, often resulting in decision deadlocks. In contrast, bio-inspired cross-inhibition enables faster, more cohesive, accurate, robust, and scalable decisions across a wide range of biased conditions. Our findings provide theoretical and practical insights into the coordination of minimal swarms and offer insights that extend to a broad class of decentralised decision-making systems in biology and engineering.

翻译：简约机器人集群通过可扩展、鲁棒且低成本的方式执行复杂任务，有望变革医疗、灾害响应和环境监测等应用领域。然而，协调此类去中心化系统仍面临根本性挑战，尤其当机器人在通信、计算和存储资源受限时。本研究中，单个机器人频繁出现环境感知错误，但集群仍能在$n$个离散选项中快速可靠地达成最优选择共识。我们比较了两种经典的舆论动力学机制——直接切换与交叉抑制——这是从神经群体到昆虫群落等跨尺度生物系统中普遍存在的简单而有效的集体信息处理规则。通过引入影响舆论动力学的非社会性偏置，我们拓展了现有平均场模型。研究发现：采用直接切换机制的集群在无偏置条件下能可靠选择最优选项，但偏置引入后性能显著下降，常导致决策死锁；而受生物启发的交叉抑制机制在广泛偏置条件下仍能实现更快速、更凝聚、更准确、更鲁棒且更具可扩展性的决策。本研究为简约集群协调提供了理论与实践洞见，其发现可延伸至生物学和工程学中更广泛的去中心化决策系统。