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An enhanced A* method incorporating an encrypted memory database for ASV efficient local path planning.

Yuanshuo Liu1, Defeng Wu1,2, Zheng You1,2

  • 1School of Marine Engineering, Jimei University, Xiamen, China.

Mathematical Biosciences and Engineering : MBE
|March 8, 2024
PubMed
Summary

This study introduces an enhanced A* algorithm for autonomous surface vehicle (ASV) path planning, improving efficiency and real-time performance using a modified heuristic and encrypted memory database for faster navigation in known waters.

Keywords:
autonomous surface vehicle (ASV)enhanced A* algorithmimproved heuristic functionmemory databasepath planning

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Area of Science:

  • Robotics and Autonomous Systems
  • Artificial Intelligence
  • Marine Engineering

Background:

  • Current autonomous surface vehicle (ASV) path planning methods often suffer from high time complexity, limiting real-time performance.
  • Existing algorithms like neural networks are computationally intensive, posing challenges for efficient local path planning.
  • The need for faster, more realistic, and secure path planning solutions for ASVs is critical.

Purpose of the Study:

  • To propose an enhanced A* algorithm for efficient local path planning in autonomous surface vehicles (ASVs).
  • To improve the real-time performance and accuracy of ASV path planning by modifying the A* algorithm's heuristic function and incorporating a memory database.
  • To enhance navigation security through an encrypted memory database for storing prior environmental path information.

Main Methods:

  • Modified the A* algorithm's heuristic function by integrating ASV motion characteristics for improved pathfinding efficiency.
  • Implemented a novel temporary target point mechanism to address issues with distant target detection ranges.
  • Incorporated an encrypted memory database to store and retrieve previously navigated water environmental paths as a priori information.

Main Results:

  • The enhanced A* algorithm demonstrated improved speed and real-time capabilities compared to existing path planning methods.
  • The modified heuristic function and temporary target point strategy effectively addressed challenges in ASV navigation.
  • The encrypted memory database facilitated faster navigation upon re-encountering known waters, while ensuring data security.

Conclusions:

  • The proposed enhanced A* algorithm offers a computationally efficient and effective solution for autonomous surface vehicle local path planning.
  • The integration of a modified heuristic and an encrypted memory database significantly boosts navigation speed and realism.
  • This approach provides a secure and optimized method for ASV navigation, particularly in environments with previously mapped routes.