Joint control of a flying robot and a ground vehicle using leader-follower paradigm


Creative Commons License

Bağbaşi A. S., TURGUT A. E., ARIKAN K. B.

Turkish Journal of Electrical Engineering and Computer Sciences, cilt.32, sa.3, ss.483-500, 2024 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 3
  • Basım Tarihi: 2024
  • Doi Numarası: 10.55730/1300-0632.4082
  • Dergi Adı: Turkish Journal of Electrical Engineering and Computer Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, INSPEC, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.483-500
  • Anahtar Kelimeler: ground robot, haptic interaction, quantitative analysis, Tethered quadcopter
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In this study, a novel control framework for the collaboration of an aerial robot and a ground vehicle that is connected via a taut tether is proposed. The framework is based on a leader-follower paradigm. The leader follows a desired trajectory while the motion of the follower is controlled by an admittance controller using an extended state observer to estimate the tether force. Additionally, a velocity estimator is also incorporated to accurately assess the leader’s velocity. An essential feature of our system is its adaptability, enabling role switching between the robots when needed. Furthermore, the synchronization performance of the robots is evaluated through quantitative analysis using the RMS position error metric and circular variance metric that is used in the mirror game to measure synchronization in human interactions. Similar to human-human interactions, we have observed that robotic agents can effectively guide one another based solely on interaction cues and adapt as needed. Our findings demonstrate the remarkable ability of robots to closely follow each other using a velocity estimation approach, without the reliance on sensors. This work contributes to the field by advancing robotic collaboration capabilities under limited sensor conditions, capitalizing on the inherent sensing capabilities of the robots, and providing a versatile platform for the study of cooperative behaviors in artificial systems.