We enable efficient and effective coordination in unpredictable environments, i.e., in environments whose future evolution is unknown a priori and even adversarial. We are motivated by the future of autonomy that involves multiple robots coordinating in dynamic, unstructured, and adversarial environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring. Such tasks are often modeled as submodular maximization coordination problems. We introduce the first submodular coordination algorithm with bounded tracking regret, i.e., with bounded suboptimality with respect to optimal time-varying actions that know the future a priori. The bound gracefully degrades with the environments' capacity to change adversarially. It also quantifies how often the robots must re-select actions to "learn" to coordinate as if they knew the future a priori. The algorithm requires the robots to select actions sequentially based on the actions selected by the previous robots in the sequence. Particularly, the algorithm generalizes the seminal Sequential Greedy algorithm by Fisher et al. to unpredictable environments, leveraging submodularity and algorithms for the problem of tracking the best expert. We validate our algorithm in simulated scenarios of target tracking.
翻译:在不可预测的环境中,即未来演进不先验、甚至对抗性地未知的环境中,我们促成高效和有效的协调。我们的动机是未来的自主性,它涉及多个机器人在动态、无结构和对抗性环境中进行协调,以完成目标跟踪、环境制图和地区监测等复杂任务。这些任务往往以亚模式最大化协调问题为模型。我们引入了第一个次模式协调算法,在受约束的跟踪后后悔,即,在了解未来前先验的最佳时间变异行动方面,存在受约束的亚模式协调算法的次优化性。结合环境对抗性变化的能力,捆绑式地退化。它也量化了机器人必须重新选择“留意”来协调的复杂行动,如同他们知道未来一样。这种算法要求机器人根据先前机器人在序列中选择的行动,按顺序选择行动。特别是,这种算法将Fishercher等人的半序列格列迪亚算算法简单化为不可预测的环境,利用亚模式和算法来追踪我们的最佳模型问题。