Exploration of extreme or remote environments such as Mars is often recognized as an opportunity for multi-robot systems. However, this poses challenges for maintaining robust inter-robot communication without preexisting infrastructure. It may be that robots can only share information when they are physically in close proximity with each other. At the same time, atmospheric phenomena such as dust devils are poorly understood and characterization of their electrostatic properties is of scientific interest. We perform a comparative analysis of two multi-robot communication strategies: a distributed approach, with pairwise intermittent rendezvous, and a centralized, fixed base station approach. We also introduce and evaluate the effectiveness of an algorithm designed to predict the location and strength of electrostatic anomalies, assuming robot proximity. Using an agent-based simulation, we assess the performance of these strategies in a 2D grid cell representation of a Martian environment. Results indicate that a decentralized rendezvous system consistently outperforms a fixed base station system in terms of exploration speed and in reducing the risk of data loss. We also find that inter-robot data sharing improves performance when trying to predict the location and strength of an electrostatic anomaly. These findings indicate the importance of appropriate communication strategies for efficient multi-robot science missions.

翻译：对火星等极端或偏远环境的勘探常被视为多机器人系统的应用契机。然而，在缺乏预先部署基础设施的条件下，维持机器人间的稳健通信面临挑战：机器人可能仅能在物理距离足够近时实现信息共享。与此同时，尘卷风等大气现象尚缺乏深入理解，对其静电特性的表征具有科学意义。本文对两种多机器人通信策略展开对比分析：采用成对间歇性会合的分布式方法，以及采用固定基站的集中式方法。我们进一步提出并评估了一种基于机器人邻近假设预测静电异常位置与强度的算法效能。通过基于智能体的仿真实验，我们在二维网格化火星环境模型中评估了上述策略的表现。结果表明：在勘探速度与数据丢失风险降低方面，去中心化会合系统始终优于固定基站系统；同时，机器人间数据共享可提升静电异常位置与强度预测性能。这些发现揭示了恰当通信策略对实现高效多机器人科学任务的重要意义。