This study proposed and evaluated an adaptive differential control strategy for an Automated Guided Vehicle (AGV) integrated with LiDAR-based obstacle sensing in a simulated environment. The control algorithm dynamically adjusted proportional gains for heading and distance errors in real time, enabling robust and precise path tracking under varying conditions. The AGV kinematics were modeled as a differential-drive mechanism, and a MATLAB-based simulation framework was developed to validate the approach. Results demonstrated that the adaptive controller reduced the average path tracking error by approximately 36% and halved the maximum heading error compared to a fixed-gain controller, highlighting its ability to correct deviations effectively while maintaining stability. Furthermore, the simulated LiDAR system accurately detected static obstacles within a 180° field of view, enhancing situational awareness. These findings underscored the potential of integrating adaptive control and sensor-based perception to improve the resilience, accuracy, and safety of AGV navigation in dynamic environments.