Cost-aware global frontier matching for ROS 2 multi-robot exploration

Abstract

Multi-robot frontier exploration supports warehouse mapping and inspection robotics, but geometric assignment can produce overlapping motion and inefficient target pairing. This paper presents a ROS 2 frontier-allocation layer that combines a weighted frontier cost with global one-to-one matching. The study isolates global matching from sequential assignment while keeping the cost formulation and ROS 2 execution stack fixed. Three policies are evaluated on two indoor maps, four team sizes, and three seeds. Across 72 completed main-policy runs, global matching gives the lowest mean completion time, travelled distance, path overlap, and assignment conflict. Relative to sequential cost-based assignment, it reduces completion time by 24.3%, travelled distance by 14.2%, and path overlap by 65.8%, with lower final coverage under the same stopping rule. The results support a coordination-efficiency benefit in the tested ROS 2 simulations; broader claims require larger, heterogeneous, dynamic, and physical deployments.