This project is dedicated to the complex task of optimizing skid steer vehicle trajectories across diverse terrains. Leveraging insights from prior research, our approach integrates a linear lateral dynamic model with a nonlinear Model Predictive Control (NMPC) strategy. The goal is to significantly enhance the adaptability and efficiency of skid steer vehicles operating in challenging environments.
- Linear Lateral Dynamic Model: Incorporates advanced modeling techniques to accurately predict vehicle behavior.
- Nonlinear Model Predictive Control (NMPC): Utilizes NMPC for adaptive and efficient trajectory planning.
- Real-Time Trajectory Evaluation: Generates optimal trajectories for short distances and simulates vehicle motion along these paths, enabling real-time performance assessment.
- Continuous Development: Ongoing refinement of the NMPC to boost performance over longer distances.
- Our system is currently capable of generating and simulating optimal trajectories for short distances.
- We are actively working on enhancing the NMPC for improved performance over extended ranges.
- Addressing System Limitations: Identifying and mitigating current system constraints for broader applicability.
- Robust Dynamic Models: Exploring more robust dynamic models for better accuracy in trajectory optimization.
- Hybrid Control Systems: Investigating the potential of hybrid control systems for real-time adaptation to diverse scenarios.
Our preliminary results establish a solid foundation for future research and development in this field. We aim to continue our work towards optimizing skid steer vehicle trajectories in challenging terrains, pushing the boundaries of what's currently possible in vehicle trajectory optimization.