PILOC: A Pheromone Inverse Guidance Mechanism and Local-Communication Framework for Dynamic Target Search of Multi-Agent in Unknown Environments

TL;DR

PILOC introduces a decentralized multi-agent framework utilizing pheromone-based guidance and local communication, significantly improving search efficiency and robustness in unknown, dynamic environments without relying on global knowledge.

Contribution

It presents a novel pheromone inverse guidance mechanism integrated with deep reinforcement learning for decentralized multi-agent search in unknown environments.

Findings

Enhanced search efficiency in dynamic scenarios

Improved robustness under communication constraints

Superior performance compared to existing methods

Abstract

Multi-Agent Search and Rescue (MASAR) plays a vital role in disaster response, exploration, and reconnaissance. However, dynamic and unknown environments pose significant challenges due to target unpredictability and environmental uncertainty. To tackle these issues, we propose PILOC, a framework that operates without global prior knowledge, leveraging local perception and communication. It introduces a pheromone inverse guidance mechanism to enable efficient coordination and dynamic target localization. PILOC promotes decentralized cooperation through local communication, significantly reducing reliance on global channels. Unlike conventional heuristics, the pheromone mechanism is embedded into the observation space of Deep Reinforcement Learning (DRL), supporting indirect agent coordination based on environmental cues. We further integrate this strategy into a DRL-based multi-agent architecture and conduct extensive experiments. Results show that combining local communication with pheromone-based guidance significantly boosts search efficiency, adaptability, and system robustness. Compared to existing methods, PILOC performs better under dynamic and communication-constrained scenarios, offering promising directions for future MASAR applications.