Frontier-Based Exploration for Heterogeneous Multi-Agent Semantic Navigation
Yan Huang, Yuhang Wang, Huaping Liu, Haibin Duan, Di Guo · 2025
In visual semantic navigation, agents are tasked with locating and reaching multiple semantic targets in complex indoor environments using visual and semantic cues. Existing approaches typically adopt either a single agent or multiple homogeneous agents with identical capabilities. However, these configurations often suffer from limited perception and inefficient exploration. To address these challenges, We propose a heterogeneous multiagent visual semantic navigation framework that leverages the complementary perceptual capabilities of diverse agents. The system periodically fuses local observations to construct a unified global semantic map. A frontier-based exploration strategy, combined with centralized task allocation, enables efficient multi-target search. In complex and unseen indoor environments, the proposed method achieves higher task success rates and improved path efficiency compared to traditional single-agent baselines. The results highlight the advantages of heterogeneous collaboration in enhancing system performance and robustness under uncertainty.