A New Conflict Resolution Method for Multiple Mobile Robots in Cluttered Environments With Motion-Liveness.

Area of Science:

• Robotics
• Artificial Intelligence
• Optimization Theory

Background:

• Coordinating multiple mobile robots presents significant challenges, particularly in cluttered and dynamic environments.
• Ensuring motion-liveness and preventing collisions are critical for efficient robotic operations.
• Existing methods often struggle with computational complexity and scalability for large robot teams.

Purpose of the Study:

• To develop a novel conflict resolution methodology for multiple mobile robots.
• To ensure motion-liveness and minimize travel times in complex environments.
• To enhance the scalability and computational efficiency of multi-robot coordination.

Main Methods:

• Formulated conflict resolution as a mathematical optimization problem, minimizing overall robot travel times.
• Developed a speed coordination strategy to resolve motion conflicts.
• Implemented environment clustering and parallel programming to manage computational costs in cluttered spaces.

Main Results:

• Successfully resolved conflicts for 100 robots in under 1.23 seconds in a highly cluttered environment.
• Demonstrated real-time implementation feasibility through an experimental testbed.
• Achieved superior computational efficiency, scalability, and motion smoothness compared to existing methods.

Conclusions:

• The proposed methodology offers a mathematically sound and computationally efficient solution for multi-robot conflict resolution.
• The approach guarantees live and smooth robot motion, even in large-scale, complex scenarios.
• Environment clustering and parallel processing significantly enhance scalability for numerous robots.