This paper introduces a novel trajectory planner for autonomous robots,specifically designed to enhance navigation by incorporating dynamic obstacleavoidance within the Robot Operating System 2 (ROS2) and Navigation 2 (Nav2)framework. The proposed method utilizes Model Predictive Control (MPC) with afocus on handling the uncertainties associated with the movement prediction ofdynamic obstacles. Unlike existing Nav2 trajectory planners which primarilydeal with static obstacles or react to the current position of dynamicobstacles, this planner predicts future obstacle positions using a stochasticVector Auto-Regressive Model (VAR). The obstacles' future positions arerepresented by probability distributions, and collision avoidance is achievedthrough constraints based on the Mahalanobis distance, ensuring the robotavoids regions where obstacles are likely to be. This approach considers therobot's kinodynamic constraints, enabling it to track a reference path whileadapting to real-time changes in the environment. The paper details theimplementation, including obstacle prediction, tracking, and the constructionof feasible sets for MPC. Simulation results in a Gazebo environmentdemonstrate the effectiveness of this method in scenarios where robots mustnavigate around each other, showing improved collision avoidance capabilities.