A self-adaptive landmark-based aggregation method for robot swarms

Sadeghi Amjadi A., Raoufi M., TURGUT A. E.

ADAPTIVE BEHAVIOR, cilt.30, sa.3, ss.223-236, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 30 Sayı: 3
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1177/1059712320985543
  • Dergi Adı: ADAPTIVE BEHAVIOR
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Aerospace Database, Animal Behavior Abstracts, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Communication Abstracts, Computer & Applied Sciences, INSPEC, Psycinfo
  • Sayfa Sayıları: ss.223-236
  • Anahtar Kelimeler: Bio-inspired, swarm robotics, cue-based aggregation, landmark-based navigation, self-adaptive
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Aggregation, a widely observed behavior in social insects, is the gathering of individuals on any location or on a cue. The former being called the self-organized aggregation, and the latter being called the cue-based aggregation. One of the fascinating examples of cue-based aggregation is the thermotactic behavior of young honeybees. Young honeybees aggregate on optimal temperature zones in the hive using a simple set of behaviors. The state-of-the-art cue-based aggregation method BEECLUST was derived based on these behaviors. The BEECLUST method is a very simple, yet a very capable method that has favorable characteristics such as robustness to noise and simplicity to apply. However, the BEECLUST method does not perform well in low robot densities. In this article, inspired by the navigation techniques used by ants and bees, a self-adaptive landmark-based aggregation method is proposed. In this method, robots use landmarks in the environment to locate the cue once they "learn" the relative position of the cue with respect to the landmark. With the introduction of an error threshold parameter, the method also becomes adaptive to changes in the environment. Through systematic experiments in kinematic and realistic simulators with different parameters, robot densities, and cue sizes, it was observed that using the information of the environment makes the proposed method to show better performance than the BEECLUST in all the settings, including low robot density, high noise, and dynamic conditions.