Global Path Planning of Unmanned Surface Vehicle Based on Improved A-Star Algorithm.

Area of Science:

• Robotics
• Environmental Science
• Algorithm Development

Background:

• Unmanned Surface Vehicles (USVs) are crucial for environmental monitoring in diverse aquatic environments.
• Efficient path planning is essential for USV autonomy and operational effectiveness.
• Existing algorithms may face limitations in complex navigation scenarios.

Purpose of the Study:

• To develop an improved A-star algorithm for global path planning of USVs.
• To enhance the efficiency and practicality of USV navigation for environmental monitoring tasks.
• To address path folding and reduce computational load in pathfinding.

Main Methods:

• Utilized raster method for environment modeling and 8-neighborhood search.
• Implemented a bidirectional search strategy to optimize path exploration.
• Applied an improved evaluation function to minimize traversed nodes.
• Incorporated path smoothing to eliminate inflection points and ensure navigational feasibility.

Main Results:

• The improved A-star algorithm demonstrated superior efficiency in path planning compared to the conventional A-star.
• Fewer traversing nodes and inflection points were observed with the enhanced algorithm.
• Smoothed paths generated by the improved algorithm better align with practical USV navigation requirements.

Conclusions:

• The proposed improved A-star algorithm offers a more efficient and practical solution for USV path planning in environmental monitoring.
• The algorithm's ability to reduce computational complexity and produce smoother paths enhances USV operational capabilities.
• This advancement supports the wider application of USVs in inland and coastal environmental surveillance.