完全分布式协同多智能体水下障碍规避
Fully Distributed Cooperative Multi-agent Underwater Obstacle Avoidance
AI总结：
本文提出了一种受狗散步启发的多智能体水下障碍规避方法，通过模拟验证提升了智能体协同性能，优于传统视觉伺服方法。
摘要：
在复杂水下环境中导航具有挑战性，尤其是在通信和自身定位受限的情况下。部分完全分布式水下导航问题已通过引入多智能体机器人团队得到解决[1]，但当环境变得复杂时，问题仍未解决。本文首先研究了日常狗散步活动与协同水下障碍规避问题之间的联系。受此类比的启发，我们提出了一种新的狗散步范式，并将其应用于多智能体水下系统中。在各种场景下进行了模拟，其性能与传统方法相比，利用基于图像的视觉伺服技术进行多智能体设置的基准测试。结果表明，我们的受狗散步启发的范式显著增强了智能体之间的协同行为，并在通过障碍物导航方面优于现有方法。
英文摘要：
Navigation in cluttered underwater environments is challenging, especially when there are constraints on communication and self-localisation. Part of the fully distributed underwater navigation problem has been resolved by introducing multi-agent robot teams [1], however when the environment becomes cluttered, the problem remains unresolved. In this paper, we first studied the connection between everyday activity of dog walking and the cooperative underwater obstacle avoidance problem. Inspired by this analogy, we propose a novel dog walking paradigm and implement it in a multi-agent underwater system. Simulations were conducted across various scenarios, with performance benchmarked against traditional methods utilising Image-Based Visual Servoing in a multi-agent setup. Results indicate that our dog walking-inspired paradigm significantly enhances cooperative behavior among agents and outperforms the existing approach in navigating through obstacles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
