Virtual Elastic Tether: a New Approach for Multi-agent Navigation in Confined Aquatic Environments
<|reference_start|>Virtual Elastic Tether: a New Approach for Multi-agent Navigation in Confined Aquatic Environments: Underwater navigation is a challenging area in the field of mobile robotics due to inherent constraints in self-localisation and communication in underwater environments. Some of these challenges can be mitigated by using collaborative multi-agent teams. However, when applied underwater, the robustness of traditional multi-agent collaborative control approaches is highly limited due to the unavailability of reliable measurements. In this paper, the concept of a Virtual Elastic Tether (VET) is introduced in the context of incomplete state measurements, which represents an innovative approach to underwater navigation in confined spaces. The concept of VET is formulated and validated using the Cooperative Aquatic Vehicle Exploration System (CAVES), which is a sim-to-real multi-agent aquatic robotic platform. Within this framework, a vision-based Autonomous Underwater Vehicle-Autonomous Surface Vehicle leader-follower formulation is developed. Experiments were conducted in both simulation and on a physical platform, benchmarked against a traditional Image-Based Visual Servoing approach. Results indicate that the formation of the baseline approach fails under discrete disturbances, when induced distances between the robots exceeds 0.6 m in simulation and 0.3 m in the real world. In contrast, the VET-enhanced system recovers to pre-perturbation distances within 5 seconds. Furthermore, results illustrate the successful navigation of VET-enhanced CAVES in a confined water pond where the baseline approach fails to perform adequately.<|reference_end|>
@article{yao2024virtual,
    title={Virtual Elastic Tether: a New Approach for Multi-agent Navigation in
  Confined Aquatic Environments},
    author={Kanzhong Yao, Xueliang Cheng, Keir Groves, Barry Lennox, Ognjen
  Marjanovic, and Simon Watson},
    journal={arXiv preprint arXiv:2403.10629},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10629},
    primaryClass={cs.RO cs.SY eess.SY}
}