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<p>Vision offers richer context than traditional marine sensors (e.g., LiDAR, Doppler Velocity Logger (DVL), sonar) but is harder to interpret on water due to reflections, glare, and dynamic surfaces. SUSHI is a vision-first navigation system for Autonomous Surface Vehicles (ASVs) that fuses detection, water segmentation, and monocular depth to produce camera-centric navigation grids for planning and control. The proposed perception methods achieve 90% segmentation accuracy through knowledge distillation with SAM2 logits, requiring only 500-550 frames and approximately 30 minutes of training. The system implements a YOLO detection model that achieves 94.5% mAP@0.5 (F1 score: 0.91) for trash and obstacle detection in simulation, and benchmarks a monocular depth method that solves the issue of reflective surfaces and can work universally. Path planning uses a Multi-Field Synthesis (MFS) approach: a locally reactive artificial-potential-field component blended adaptively with a global wavefront flow field, mitigating local minima while preserving real-time responsiveness. A behavior layer prioritizes target seeking and mask-based visual exploration when explicit goals are absent. Validation was performed in the TOAST simulator and in a pool environment, demonstrating robust goal targeting and exploration using cameras with minimal side sensing for emergency avoidance.</p>

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SUSHI: A Vision System for Reactive, Uninformed ASV Navigation via Multi-Field Path Planning and Visual Exploration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月6日

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  • Hamze Hammami,
  • Muhammad Abban,
  • Abdul Maajid Aga,
  • Laith Mohamed,
  • Saif Alsaad,
  • Nidhal Abdulaziz

摘要

Vision offers richer context than traditional marine sensors (e.g., LiDAR, Doppler Velocity Logger (DVL), sonar) but is harder to interpret on water due to reflections, glare, and dynamic surfaces. SUSHI is a vision-first navigation system for Autonomous Surface Vehicles (ASVs) that fuses detection, water segmentation, and monocular depth to produce camera-centric navigation grids for planning and control. The proposed perception methods achieve 90% segmentation accuracy through knowledge distillation with SAM2 logits, requiring only 500-550 frames and approximately 30 minutes of training. The system implements a YOLO detection model that achieves 94.5% mAP@0.5 (F1 score: 0.91) for trash and obstacle detection in simulation, and benchmarks a monocular depth method that solves the issue of reflective surfaces and can work universally. Path planning uses a Multi-Field Synthesis (MFS) approach: a locally reactive artificial-potential-field component blended adaptively with a global wavefront flow field, mitigating local minima while preserving real-time responsiveness. A behavior layer prioritizes target seeking and mask-based visual exploration when explicit goals are absent. Validation was performed in the TOAST simulator and in a pool environment, demonstrating robust goal targeting and exploration using cameras with minimal side sensing for emergency avoidance.