REACT：用于系留水下机器人的实时纠缠感知覆盖路径规划
REACT: Real-time Entanglement-Aware Coverage Path Planning for Tethered Underwater Vehicles
AI总结：
REACT通过实时纠缠感知覆盖路径规划，实现了安全高效的水下结构检查，比传统方法快20%完成任务。
摘要：
使用系留水下机器人对水下结构进行检查常常受到缆绳缠绕风险的阻碍。我们提出了REACT（用于系留水下机器人的实时纠缠感知覆盖路径规划）框架，旨在克服这一限制。REACT包含一个计算高效的基于几何的缆绳模型，利用符号距离场（SDF）图来准确、实时地模拟围绕任意结构的紧绷缆绳配置。该模型使通过强制最大缆绳长度限制而实现高效的在线重新规划策略，从而主动防止缠绕。通过将REACT整合到覆盖路径规划框架中，我们实现了安全且无缠绕的检查路径，这些路径此前因缆绳限制而具有挑战性。完整的REACT框架的有效性在管道检查场景中得到验证，展示了安全导航和全面覆盖检查。模拟结果表明，REACT在保持缆绳限制的同时实现完全覆盖，并且比传统规划器快20%完成总任务，尽管由于主动避免缠绕而延长了检查时间，从而消除了大量任务后解缠操作。实际实验确认了这些益处，在实验中，REACT完成整个任务，而基线规划器因物理缆绳缠绕而失败。
英文摘要：
Inspection of underwater structures with tethered underwater vehicles is often hindered by the risk of tether entanglement. We propose REACT (real-time entanglement-aware coverage path planning for tethered underwater vehicles), a framework designed to overcome this limitation. REACT comprises a computationally efficient geometry-based tether model using the signed distance field (SDF) map for accurate, real-time simulation of taut tether configurations around arbitrary structures in 3D. This model enables an efficient online replanning strategy by enforcing a maximum tether length constraint, thereby actively preventing entanglement. By integrating REACT into a coverage path planning framework, we achieve safe and entanglement-free inspection paths, previously challenging due to tether constraints. The complete REACT framework's efficacy is validated in a pipe inspection scenario, demonstrating safe navigation and full coverage inspection. Simulation results show that REACT achieves complete coverage while maintaining tether constraints and completing the total mission 20% faster than conventional planners, despite a longer inspection time due to proactive avoidance of entanglement that eliminates extensive post-mission disentanglement. Real-world experiments confirm these benefits, where REACT completes the full mission, while the baseline planner fails due to physical tether entanglement.
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