发表机构
The University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对CPFA重复探索导致效率低的问题，提出ARPC算法，通过排斥信息素航点聚类引导机器人，在仿真中显著优于CPFA和GPFA。
AI 中文摘要
中央位置觅食算法（CPFA）结合位点忠诚度、信息素引导导航与无信息随机搜索，可实现机器人群体的分布式资源收集。但CPFA常重复访问已探索区域，导致其他区域搜索不足，资源稀缺时效率降低。本文提出受生物启发的自适应排斥信息素聚类算法（ARPC），机器人会沉积排斥信息素航点标记已探索位置，这些航点围绕巢穴聚类以估计低价值搜索区域，从而引导机器人转向可能未访问区域。ARPC整合了已知资源的利用与冗余探索的系统规避，提升了搜索多样性与资源发现效率。在ARGoS仿真平台上，针对不同场地规模、资源密度及聚类、随机、幂律空间分布的大量实验表明，ARPC始终优于CPFA与基于网格的CPFA（GPFA），尤其在早期发现阶段（提升10%）与后期收集阶段（最高提升60%，传统方法在此阶段通常性能下降）表现出显著增益。这些结果表明，ARPC为大规模异构群体觅食环境提供了可扩展且鲁棒的策略。
英文摘要
The Central Place Foraging Algorithm (CPFA) combines site fidelity, pheromone-guided navigation, and uninformed random search to enable decentralized resource collection in robot swarms. However, CPFA often revisits previously explored regions while leaving other areas insufficiently searched, reducing efficiency as resources become scarce. In this paper, we propose Adaptive Repulsive Pheromone Clustering (ARPC), a bio-inspired method in which robots deposit repulsive pheromone waypoints to mark previously explored locations. These waypoints are clustered around the nest to estimate low-value search regions, allowing robots to be redirected toward likely unvisited areas. By integrating the exploitation of known resources with systematic avoidance of redundant exploration, ARPC improves search diversity and resource discovery efficiency. Extensive simulations in ARGoS across varying arena sizes, resource densities, and clustered, random, and power-law spatial distributions demonstrate that ARPC consistently outperforms CPFA and the Grid-Based CPFA (GPFA). In particular, ARPC yields significant gains during both early discovery (10\%) and late-stage (up to 60\%) collection, where conventional methods typically degrade. These results indicate that ARPC provides a scalable and robust strategy for large-scale heterogeneous swarm foraging environments.
Comments14 pages, 5 figures, The 18th International Symposium on Distributed Autonomous Robotic Systems
