PierGuard: A Planning Framework for Underwater Robotic Inspection of Coastal Piers

2025-05-07

AI Summary

To address the low planning efficiency and slow convergence of underwater robot path planning in near-shore harbor inspection under high-clutter and low-visibility conditions, this paper proposes a neuro-inspired bidirectional RRT* framework. The method introduces three key innovations: (1) a lightweight CNN-driven heuristic region generation scheme specifically designed for underwater cluttered environments, enabling dynamic environment perception and guided sampling integration; (2) an enhanced bidirectional sampling strategy; and (3) a geometry-learning hybrid validation mechanism. Evaluated through joint validation on real-ocean calibration data and high-fidelity simulation, the approach achieves 2.6× and 4.9× higher planning efficiency than state-of-the-art geometric and learning-based methods, respectively, while significantly improving path quality and convergence speed. The framework has been successfully validated in multiple autonomous inspection missions in real maritime environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning within complex environments. Sampling-based path planning methods, such as Rapidly-exploring Random Tree* (RRT*), have demonstrated notable performance in high-dimensional spaces. In recent years, researchers have begun designing various geometry-inspired heuristics and neural network-driven heuristics to further enhance the effectiveness of RRT*. However, the performance of these general path planning methods still requires improvement when applied to highly cluttered underwater environments. In this paper, we propose PierGuard, which combines the strengths of bidirectional search and neural network-driven heuristic regions. We design a specialized neural network to generate high-quality heuristic regions in cluttered maps, thereby improving the performance of the path planning. Through extensive simulation and real-world ocean field experiments, we demonstrate the effectiveness and efficiency of our proposed method compared with previous research. Our method achieves approximately 2.6 times the performance of the state-of-the-art geometric-based sampling method and nearly 4.9 times that of the state-of-the-art learning-based sampling method. Our results provide valuable insights for the automation of pier inspection and the enhancement of maritime safety. The updated experimental video is available in the supplementary materials.

Problem

• Enhancing underwater robot path planning for pier inspection
• Improving RRT* performance in cluttered underwater environments
• Combining bidirectional search with neural network heuristics

Innovation

• Combines bidirectional search with neural networks
• Designs neural network for heuristic regions
• Improves path planning in cluttered environments