This paper presents RANT, an ant-inspired multi-robot exploration framework for noisy, uncertain environments. A team of differential-drive robots navigates a 10 x 10 m terrain, collects noisy probe measurements of a hidden richness field, and builds local probabilistic maps while the supervisor maintains a global evaluation. RANT combines particle-filter localisation, a behaviour-based controller with gradient-driven hotspot exploitation, and a lightweight no-revisit coordination mechanism based on virtual pheromone blocking. We experimentally analyse how team size, localisation fidelity, and coordination influence coverage, hotspot recall, and redundancy. Results show that particle filtering is essential for reliable hotspot engagement, coordination substantially reduces overlap, and increasing team size improves coverage but yields diminishing returns due to interference.

翻译：本文提出RANT，一种面向噪声干扰及不确定性环境的蚁群启发式多机器人探索框架。该框架部署一组差速驱动机器人在10×10米地形中导航，通过对隐藏资源场进行含噪探测测量构建局部概率地图，同时由监控系统维持全局评估。RANT融合了粒子滤波定位、基于梯度驱动热点开发的仿生控制器，以及基于虚拟信息素阻断的轻量化防重访协同机制。我们通过实验分析了团队规模、定位精度和协同策略对覆盖率、热点召回率及冗余度的影响。结果表明：粒子滤波对实现可靠的热点追踪至关重要，协同机制能显著降低探索重叠，增加团队规模可提升覆盖率，但因干扰效应会产生收益递减现象。