受限环境下水下机器人-机械臂系统的分层拓扑感知规划与控制

中文摘要 AI 辅助

本文针对水下机器人-机械臂系统（UVMS）在受限、杂乱且部分已知环境中的自主干预问题展开研究，这类环境中较差的机动性、狭窄通道以及不确定的执行情况可能导致机器人进入无法恢复的区域。我们提出了MANTA，这是一个三层分层规划与控制框架，将通道可达性、操作可行性与闭环执行相结合。第一层在保守简化的基座空间中进行全局连通性推理，提取通往任务区域的可通行走廊候选；第二层通过联合优化连续基座运动与机械臂轨迹，细化每个候选走廊，生成无碰撞的基座-机械臂轨迹；第三层通过基于高斯过程模型的强化学习（MBRL），借助MC-PILCO学习到达并保持基座的策略，支持在规划的操作状态下的轨迹跟踪与位置保持。执行过程中，该框架会监控地图更新，当活动通道变得不可行时可触发恢复与路径修复。MANTA在受限UVMS规划与闭环跟踪实验中得到评估：在120次匹配的规划查询中，它比基于全状态采样的基线方法实现了更高的任务成功率，同时产生了更大的安全余量和更小的机械臂运动；学习到的MC-PILCO策略还进一步降低了训练和未见过的管状参考轨迹的位置与偏航跟踪误差。这些结果表明，MANTA是一种结构化且数据高效的框架，适用于洞穴、管道和杂乱海底结构中的安全自主水下干预。

英文摘要

This paper addresses autonomous intervention with an underwater vehicle--manipulator system (UVMS) in confined, cluttered, and partially known environments, where poor maneuverability, narrow passages, and uncertain execution may cause the robot to enter unrecoverable regions. We propose MANTA, a three-layer hierarchical planning-and-control framework that couples passage accessibility, manipulation feasibility, and closed-loop execution. The first layer performs global connectivity reasoning in a conservative reduced base space to extract traversable corridor candidates toward the task region. The second layer refines each candidate corridor by jointly optimizing the continuous base motion and arm trajectory, producing a collision-free base--arm trajectory. The third layer learns a reach-and-hold base policy using Gaussian-process model-based reinforcement learning (MBRL) through MC-PILCO, enabling trajectory tracking and station keeping at the planned manipulation state. During execution, the framework monitors map updates and can trigger recovery and route repair when the active passage becomes infeasible. MANTA is evaluated in confined UVMS planning and closed-loop tracking experiments. Across 120 matched planning queries, it achieves higher task success than full-state sampling-based baselines while producing larger clearance margins and lower arm motion. The learned MC-PILCO policy further reduces position and yaw tracking errors on both training and unseen tube-like references. These results show MANTA as a structured and data-efficient framework for safe autonomous underwater intervention in caves, tubes, and cluttered subsea structures.

发表机构

• University of Padova（帕多瓦大学）
• Polytechnic University of Bari（巴里理工大学）
• Norwegian University of Science and Technology（挪威科技大学）

机构由 AI 辅助整理，请以论文原文为准。