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Multi-Robot Path Planning for High-Density Parking Environments Considering Efficiency and Fairness.

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  • 1Department of Mechanical Engineering, Korea University, Seoul 02841, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a new autonomous parking system for high-density parking lots. It balances operational efficiency and task priority, effectively managing peak-season demand with multiple robots.

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Area of Science:

  • Robotics and Automation
  • Operations Research
  • Artificial Intelligence

Background:

  • Airport parking congestion necessitates efficient solutions like high-density parking (HDP) systems.
  • Conventional multi-agent pathfinding (MAPF) methods prioritize overall efficiency over individual task priority and robot feasibility.
  • Existing MAPF methods generate physically infeasible paths for parking robots.

Purpose of the Study:

  • To develop a multi-robot path planning approach balancing operational efficiency and individual task priority.
  • To address the limitations of conventional MAPF in real-world airport parking scenarios.
  • To create a practical foundation for time-based parking operations and robot fleet estimation.

Main Methods:

  • Proposed a novel multi-robot path planning approach enhancing priority-based search (PBS).
  • Dynamically adjusted priorities to ensure both system-wide efficiency and individual vehicle task completion.
  • Implemented a simulator replicating a real airport parking environment with 100 slots and parking robots.

Main Results:

  • The autonomous parking system effectively handled peak-season parking demand using real airport data.
  • Achieved a throughput exceeding 41 vehicles per hour with optimized weight values.
  • Demonstrated acceptable fairness in task completion while meeting high demand.

Conclusions:

  • The proposed method offers a practical solution for efficient and priority-aware autonomous parking.
  • Validates the system's capability to manage peak-season demand in complex airport environments.
  • Provides a basis for optimizing parking operations and determining optimal robot deployment.