The use of Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs) is increasingly relevant in industrial and logistics environments, particularly during the early stages of system development, where control, navigation, and coordination strategies must be evaluated efficiently and have low implementation overhead. This paper presents a modular and transparent MATLAB-based simulation framework designed to support early-stage modeling, comparison, and validation of AMR/AGV behaviors under realistic operational conditions. The proposed framework is implemented exclusively in base MATLAB, without relying on dedicated Robotics or Control System Toolboxes, ensuring accessibility, reproducibility, and full transparency of the underlying models. The framework integrates several functional modules, including: (i) adaptive velocity profiling to highlight behavioral differences between AMR and AGV motion characteristics; (ii) trajectory tracking using classical Proportional–Integral–Derivative (PID) control and Model Predictive Control (MPC); (iii) obstacle avoidance based on artificial potential fields; (iv) a simplified SLAM-inspired occupancy grid mapping approach; and (v) a basic demonstration of multi-robot interaction and coordination. While no novel control or navigation algorithms are introduced, the framework enables a consistent and parametric comparison of classical and predictive control strategies within a unified simulation environment. Simulation results indicate that MPC-based control provides improved trajectory tracking accuracy and smoother motion, with reduced oscillatory behavior when compared to PID control, particularly under dynamic constraints. The obstacle avoidance and mapping modules support safe navigation in partially known environments, while the multi-robot demonstration illustrates scalable interaction principles applicable to fleet-level studies. Overall, the proposed framework constitutes a scalable and computationally efficient foundation for early-stage AMR/AGV research, benchmarking, and education, and provides a structured basis for future extensions involving advanced perception, coordination, and optimization strategies.