This research focuses on improving autonomous robot navigation in dynamic environments by integrating Model Predictive Control (MPC) and Discrete-Time Control Barrier Function (D-CBF) with sensor fusion techniques. The primary challenge addressed is ensuring safe and efficient navigation while avoiding both static and dynamic obstacles. The proposed system incorporates data from Marvelmind sensors to enhance real-time obstacle detection and trajectory planning, reducing uncertainty in navigation. By leveraging predictive modeling and adaptive safety margins, the navigation system dynamically adjusts its path to prevent collisions while maintaining smooth movement. The study also explores the computational efficiency of MPC and D-CBF, ensuring real-time feasibility for mobile robots operating in complex scenarios.
To validate the effectiveness of the proposed navigation framework, the research employs a combination of simulation and real-world experiments within controlled environments. The system's performance is assessed based on key metrics, including obstacle avoidance accuracy, trajectory tracking efficiency, and energy consumption. The results are expected to demonstrate that the integration of MPC and D-CBF, combined with optimized computational techniques, enables safer and more reliable mobile robot navigation. The findings of this research contribute to advancing autonomous robotic systems, particularly in applications requiring precise movement in unpredictable environments.
Keywords: MPC, D-CBF, ROS, Marvelmind, Mobile Robot Navigation, Obstacle Avoidance