Hierarchical Topology-Aware Planning and Control of Underwater Vehicle-Manipulator Systems in Confined Environments

2026-08-09

AI Summary

This work addresses the challenge of underwater manipulator robots becoming irrecoverably stuck in confined, cluttered, and partially known environments due to limited maneuverability, narrow passages, and actuation uncertainty. To overcome this, the authors propose MANTA, a three-layer hierarchical planning and control framework that integrates topological-level global connectivity reasoning, base-arm coupled trajectory optimization, and a Gaussian process model-based reinforcement learning closed-loop controller (MC-PILCO), enabling dynamic replanning and real-time map updates. Experimental results across 120 trials demonstrate that the proposed approach significantly outperforms baseline methods in task success rate, achieves greater path clearance, reduces manipulator motion, and substantially lowers position and yaw tracking errors.

Abstract

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.

Problem

• underwater vehicle-manipulator system
• confined environments
• autonomous intervention
• unrecoverable regions
• maneuverability

Innovation

• hierarchical planning
• underwater vehicle-manipulator system
• model-based reinforcement learning
• collision-free trajectory optimization
• adaptive recovery