The distributed coordination of robot teams performing complex tasks is challenging to formulate. The different aspects of a complete task such as local planning for obstacle avoidance, global goal coordination and collaborative mapping are often solved separately, when clearly each of these should influence the others for the most efficient behaviour. In this paper we use the example application of distributed information acquisition as a robot team explores a large space to show that we can formulate the whole problem as a single factor graph with multiple connected layers representing each aspect. We use Gaussian Belief Propagation (GBP) as the inference mechanism, which permits parallel, on-demand or asynchronous computation for efficiency when different aspects are more or less important. This is the first time that a distributed GBP multi-robot solver has been proven to enable intelligent collaborative behaviour rather than just guiding robots to individual, selfish goals. We encourage the reader to view our demos at https://aalpatya.github.io/gbpstack

翻译：机器人团队执行复杂任务时的分布式协调问题难以建模。完整任务的不同方面，例如局部避障规划、全局目标协调以及协同建图，通常被单独处理，而实际上，为了获得最高效的行为，这些方面应当互相影响。本文以分布式信息获取在机器人团队探索大空间中的应用为例，证明我们可以将整个问题构建为单一因子图，其中包含多个表示各个方面的互联层。我们采用高斯置信传播作为推理机制，该机制支持并行、按需或异步计算，从而在各个方面重要性不同时提高效率。这是首次证明分布式GBP多机器人求解器能够实现智能协作行为，而不仅仅是引导机器人追求各自的自私目标。我们鼓励读者访问 https://aalpatya.github.io/gbpstack 观看我们的演示。