Multi-robot system improves navigation using object relations and collaboration
CoRelNav: Collaborative Relational Navigation for Multi-Robot Spatially Constrained Semantic Navigation
Robotics
Summary
Finding places or objects sometimes needs robots to understand not just what they are looking for but also how those targets relate to other things nearby. The authors created a system called CoRelNav that helps multiple robots work together by sharing what they find and deciding where to explore next. This teamwork lets robots check their ideas about where things are more accurately and avoid repeating the same searches. They tested this method in detailed computer simulations and real robots, showing it helps robots find objects better when navigation depends on context and relationships.
What this means in practice
- •For robotics engineers: Coordinate multiple robots to explore complex scenes efficiently using relational clues and shared observations for improved target finding.
- •For warehouse automation teams: Enable robot teams to locate items defined by their relations to other objects, improving navigation in cluttered or changing environments.
Authors
Jinyu He, Zihao Mao, Haonan Jin, Mengyin Fu, Wenjie Song
Abstract
Spatially constrained semantic navigation requires robots to identify targets specified not only by semantic categories but also by relations to surrounding objects. In unknown environments, resolving such goals requires efficient exploration together with sufficient target and contextual evidence for reliable relation verification. Existing methods leave relation-aware verification and multi-robot collaboration largely disconnected: relational navigation is predominantly single-agent, while multi-robot systems seldom coordinate distributed observations for instance-specific relation verification. We propose CoRelNav, whose core is coupling task-conditioned multi-robot exploration with candidate-driven collaborative verification. A spatial-semantic field converts task constraints, scene nodes, and object features into exploration utility; as candidate information accumulates, robots are reallocated toward complementary evidence under team navigation costs, while instance-consistent observations are aggregated across topology nodes. This coupling reduces redundant search and enables relation hypotheses to be resolved from distributed partial evidence that independent exploration or isolated-view verification can leave ambiguous. Experiments in photorealistic simulation demonstrate consistent improvements over representative baselines, with ablations validating the proposed exploration and verification mechanisms. We further deploy the complete system on two physical mobile robots, demonstrating its applicability to real-world collaborative navigation.