Directional Pheromone Gradient Observations for Decentralized Multi-Agent Reinforcement Learning in Swarm Drone Search and Rescue
Search-and-rescue (SAR) operations in disaster environments require drone swarms to coordinate efficiently despite incomplete information and potential communication failures. Existing stigmergy-based approaches provide low-bandwidth coordination but rely on fixed rules, whereas multi-agent reinforcement learning (MARL) can learn adaptive behaviors but often struggles with coordination under partial observability. To address these limitations, this paper proposes a Hybrid stigmergy–MARL framework that introduces directional pheromone-gradient observations, enabling each drone to infer the direction of likely victims and unexplored regions using locally available information. The proposed framework combines reinforcement learning with four virtual pheromone layers representing coverage history, victim likelihood, environmental risk, and communication quality. Victim detection is modeled through an abstract short-range thermal/visual sensing mechanism, while environmental information is shared through pheromone-based environmental memory to reduce dependence on direct communication. The simulated environment consists of a 40 × 40 grid, where each grid cell represents a discrete two-dimensional location. Victims occupy a single grid cell, and obstacles are modeled as static two-dimensional impassable cells. Experimental results show that the proposed approach achieved 98.9% area coverage and 93.3% victim detection, compared with 81.8% coverage and 71.7% victim detection for the RL-only baseline. Ablation experiments confirmed that directional gradient observations are the primary contributor to these improvements, while communication-loss experiments demonstrated robust performance even under complete communication outage. These findings indicate that directional pheromone-gradient observations provide an effective and communication-efficient mechanism for decentralized swarm coordination, improving search effectiveness and operational robustness in post-disaster SAR scenarios.