The current OGM-CBF approach, while innovative, has several potential limitations that could hinder its performance in more complex environments:
Computational Complexity: The real-time computation of the signed distance function (SDF) and the control barrier function can become computationally intensive, especially in environments with a high density of obstacles. This may lead to delays in decision-making and control execution. To improve this, optimization techniques such as parallel processing or GPU acceleration can be employed to speed up the calculations.
Sensor Limitations: The effectiveness of the OGM-CBF is heavily reliant on the quality and accuracy of the sensor data used to construct the occupancy grid map. In challenging conditions, such as low visibility or adverse weather, sensor performance may degrade, leading to inaccurate maps. To mitigate this, the integration of robust sensor fusion techniques and redundancy in sensor modalities can enhance the reliability of the data used for mapping.
Static vs. Dynamic Environments: The current implementation primarily focuses on static obstacles and may struggle in highly dynamic environments where obstacles frequently change. Enhancing the OGM to incorporate dynamic updates and predictive modeling, as mentioned previously, can help address this limitation.
Scalability: As the complexity of the environment increases, the size of the occupancy grid map may also grow, leading to scalability issues. Implementing hierarchical mapping techniques or adaptive grid resolutions can help manage the size of the map while maintaining the necessary detail for safe navigation.
Parameter Tuning: The performance of the OGM-CBF is sensitive to the tuning of parameters such as the safety margins (ls and la) and the shaping function T. Developing adaptive tuning algorithms that adjust these parameters based on real-time feedback from the environment can improve the robustness and adaptability of the control strategy.
By addressing these limitations through computational optimizations, enhanced sensor integration, and adaptive algorithms, the OGM-CBF approach can be significantly improved to handle more complex environments and scenarios effectively.