Natural-artificial hybrid swarm: Cyborg-insect group navigation in unknown obstructed soft terrain
<|reference_start|>Natural-artificial hybrid swarm: Cyborg-insect group navigation in unknown obstructed soft terrain: Navigating multi-robot systems in complex terrains has always been a challenging task. This is due to the inherent limitations of traditional robots in collision avoidance, adaptation to unknown environments, and sustained energy efficiency. In order to overcome these limitations, this research proposes a solution by integrating living insects with miniature electronic controllers to enable robotic-like programmable control, and proposing a novel control algorithm for swarming. Although these creatures, called cyborg insects, have the ability to instinctively avoid collisions with neighbors and obstacles while adapting to complex terrains, there is a lack of literature on the control of multi-cyborg systems. This research gap is due to the difficulty in coordinating the movements of a cyborg system under the presence of insects' inherent individual variability in their reactions to control input. In response to this issue, we propose a novel swarm navigation algorithm addressing these challenges. The effectiveness of the algorithm is demonstrated through an experimental validation in which a cyborg swarm was successfully navigated through an unknown sandy field with obstacles and hills. This research contributes to the domain of swarm robotics and showcases the potential of integrating biological organisms with robotics and control theory to create more intelligent autonomous systems with real-world applications.<|reference_end|>
@article{bai2024natural-artificial,
    title={Natural-artificial hybrid swarm: Cyborg-insect group navigation in
  unknown obstructed soft terrain},
    author={Yang Bai, Phuoc Thanh Tran Ngoc, Huu Duoc Nguyen, Duc Long Le, Quang
  Huy Ha, Kazuki Kai, Yu Xiang See To, Yaosheng Deng, Jie Song, Naoki Wakamiya,
  Hirotaka Sato, Masaki Ogura},
    journal={arXiv preprint arXiv:2403.17392},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17392},
    primaryClass={cs.RO cs.SY eess.SY nlin.AO}
}