Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi-Robot Coordination

TL;DR

This paper presents a novel online submodular coordination algorithm with bounded tracking regret, enabling multi-robot teams to adaptively coordinate in unpredictable, adversarial environments for complex tasks like target tracking and mapping.

Contribution

It introduces the first submodular coordination algorithm with bounded tracking regret that adapts to adversarial environment changes, extending the Sequential Greedy approach to unpredictable settings.

Findings

Algorithm achieves bounded suboptimality in dynamic environments.

Validated in simulated target tracking scenarios.

Degrades gracefully with environment adversariality.

Abstract

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.