Modern logistics systems increasingly require high flexibility in handling simultaneous package transfers in compact, dynamic environments without collisions. Improper handling of multi-package transfers in omnidirectional conveyor systems can lead to deadlocks, congestion, or delivery delays, particularly in grid-based environments where routing complexity increases with package variability and layout density. This research addresses these challenges by introducing Q-RCR, a modular Q-Learning-based framework with Rule-Based Conflict Resolution (RCR) for intelligent path planning and collision handling in Four-Wheeled Omnidirectional Cellular Conveyor (FOCC) systems. The research contribution is decoupling path learning and collision handling, enabling independent agent training while minimizing computational burden and improving convergence in multi-agent scenarios. The proposed Q-RCR framework integrates Q-Learning for route optimization with a rule-based conflict resolution module, applying four adaptive strategies: Sequential Transfer, Insert Path, Reroute, and Hybrid. The method is implemented in a grid-based FOCC environment, supporting eight-directional movement and handling various package sizes. Experiments were conducted in four scenarios with grid dimensions ranging from 8×11 to 12×12 and involving up to four simultaneous packages. Results show that Q-RCR consistently outperforms Double Q-Learning, RRT, and A* regarding delivery time, path smoothness, and the number of activated cells. The hybrid mode demonstrated the most effectiveness in handling frequent collisions and maintaining operational flow continuity. The proposed framework demonstrates strong adaptability, scalability, and responsiveness, offering a practical and intelligent solution for real-time multi-package coordination in flexible manufacturing and warehouse automation environments.
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