自主水下机器人：在“深海盲盒”中开辟最优航路

Core Viewpoint - The article discusses the advancements in Autonomous Underwater Vehicles (AUVs) and their path planning capabilities in complex underwater environments, emphasizing the transition from traditional methods to intelligent, data-driven approaches [1][4]. Group 1: AUV Path Planning Framework - A comprehensive path planning framework for AUVs must consider three key elements: AUV motion characteristics, external environment modeling, and task optimization strategies [7]. - AUVs are modeled as six degrees of freedom (6-DoF) rigid body systems, utilizing computational fluid dynamics (CFD) combined with semi-empirical or data-driven methods to balance efficiency and accuracy in motion constraints [7][8]. Group 2: Environmental Modeling and Task Optimization - High-precision external environment modeling is crucial for path planning, with deep learning techniques effectively addressing data sparsity in underwater terrain reconstruction [8]. - Task optimization strategies focus on minimizing energy consumption for single AUVs and optimizing task allocation and communication in multi-AUV scenarios, utilizing distributed reinforcement learning and game theory to enhance collaborative operations [9]. Group 3: Traditional Algorithms in Path Planning - Traditional algorithms for AUV path planning are categorized into global and local planning, each with strengths and limitations. Global planning methods like Dijkstra and A* are effective in known environments but face challenges in high-dimensional spaces [10][11]. - Local path planning methods, such as Artificial Potential Field (APF) and Dynamic Window Approach (DWA), provide real-time obstacle avoidance but may struggle with local minima and global coherence in complex terrains [11]. Group 4: Machine Learning in AUV Navigation - The evolution of AUV path planning is shifting from rule-based to data-driven paradigms, with machine learning significantly enhancing autonomous decision-making capabilities in dynamic marine environments [12]. - Supervised learning is used for environmental feature extraction, while unsupervised learning aids in task allocation for multi-AUV systems, improving computational efficiency in resource optimization [12][13]. Group 5: Future Directions for AUV Technology - Future research aims to enhance AUV capabilities through meta-learning and lifelong learning mechanisms, enabling rapid adaptation to new environments with minimal data [16]. - The integration of multimodal perception technologies and lightweight models with edge computing is expected to improve environmental modeling reliability and real-time path planning efficiency [17].