Convex Decomposition for a Coverage Path Planning for Autonomous Vehicles: Interior Extension of Edges

Area of Science:

• Robotics and Autonomous Systems
• Computational Geometry
• Path Planning Algorithms

Background:

• Autonomous vehicle coverage path planning is crucial for area exploration.
• Complex boundaries and obstacles pose significant challenges to existing methods.
• Decomposition into convex sub-polygons is a common strategy to address these challenges.

Purpose of the Study:

• To propose a new, simple, and universally applicable convex decomposition method.
• To enhance the efficiency and effectiveness of coverage path planning for autonomous vehicles.
• To address limitations of existing methods in handling complex environments.

Main Methods:

• A novel convex decomposition technique is introduced.
• The method identifies decomposition points based on interior polygon angles exceeding 180 degrees.
• The approach is designed for applicability to any polygonal area shape.

Main Results:

• The proposed method effectively decomposes complex polygonal areas.
• Illustrative examples demonstrate the performance of the new technique.
• Comparisons with existing convex decomposition methods are presented.

Conclusions:

• The developed convex decomposition method offers a simplified and versatile solution.
• It improves the feasibility of coverage path planning in challenging environments.
• The method holds potential for advancing autonomous vehicle applications.