Dynamic Passing Strategy on Middle Size League Robot Soccer ERSOW Using Potential Passing Point

Passing is a key element in robot soccer, determining how well a team can maintain possession and create scoring opportunities. In dynamic match situations, static passing strategies are often ineffective due to moving obstacles and unpredictable opponent positions. This research proposes a dynamic passing strategy on Middle Size League robot soccer ERSOW using the Potential Passing Point (PPP) method. This method enables robots to determine optimal passing targets by generating candidate points in real-time, filtering them based on field constraints, obstacle-free trajectories, and proximity to the opponent's goal. If the selected passing point is too close to the goal and causes limited vision from the robot's camera, the system adjusts the target by computing its intersection with a predefined safe zone. The approach integrates obstacle detection, field analysis, and communication between teammate robots to dynamically execute passing with role-switching behavior. The proposed strategy increases the accuracy, reliability, and tactical flexibility of passing during gameplay. Experimental results show an average ball possession of 65.23%, passing accuracy of 78.16%, and 36 goals scored, demonstrating the effectiveness of the proposed method in improving overall team performance.