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ATRON: Autonomous trash retrieval for oceanic neatness.

John Abanes1, Hyunjin Jang2, Behruz Erkinov3

  • 1Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, United States.

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Summary

This study developed an unmanned surface vehicle (USV) for autonomous floating debris removal. The USV utilizes advanced sensors and AI for navigation, debris identification, and efficient collection path planning.

Keywords:
YOLO object detectioncollision avoidanceorienteering problempath planninguncrewed marine vessel

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Area of Science:

  • Robotics and Autonomous Systems
  • Environmental Engineering
  • Computer Vision

Background:

  • Floating debris poses a significant threat to marine ecosystems and navigation.
  • Current methods for debris removal are often manual, inefficient, and costly.
  • Autonomous systems offer a promising solution for effective and scalable debris management.

Purpose of the Study:

  • To design and develop an unmanned surface vehicle (USV) capable of autonomously collecting floating debris.
  • To integrate advanced sensing, perception, and path planning algorithms for efficient debris removal operations.
  • To evaluate the performance of the developed USV in a controlled environment.

Main Methods:

  • A twin-hulled USV equipped with thrusters, a debris collection system (funnel, timing belt, electric motor), and navigation sensors (camera, LiDAR, IMU).
  • Simultaneous Localization and Mapping (SLAM) using LiDAR and IMU for USV odometry.
  • Object detection using YOLO for floating debris identification from camera frames.
  • Path planning incorporating the orienteering problem for debris collection order and Open Motion Planning Library (OMPL) for obstacle avoidance.
  • Trajectory generation using Pure Pursuit and PID control for path following.

Main Results:

  • The USV successfully identified and navigated towards floating debris in a controlled pool environment.
  • The integrated SLAM system provided accurate localization and mapping capabilities.
  • The path planning and control system enabled efficient debris collection routes while avoiding static obstacles.
  • Experimental estimation of velocity limits and PID tuning improved path-following accuracy.

Conclusions:

  • The developed USV demonstrates the feasibility of autonomous floating debris removal.
  • The combination of AI-driven perception and advanced motion planning is effective for marine debris collection.
  • Further research can focus on scaling this technology for real-world oceanic applications.