RANT: Ant-Inspired Multi-Robot Rainforest Exploration Using Particle Filter Localisation and Virtual Pheromone Coordination

TL;DR

RANT is an ant-inspired multi-robot framework that uses particle filter localisation and virtual pheromones to efficiently explore and map uncertain environments, reducing redundancy and improving coverage.

Contribution

The paper introduces RANT, a novel multi-robot exploration approach combining particle filters and virtual pheromones for effective environmental mapping.

Findings

Particle filtering improves hotspot engagement reliability.

Coordination reduces overlap and redundancy.

Increasing team size enhances coverage with diminishing returns.

Abstract

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.