Integrating the proposed method with other motion planning or control techniques can significantly enhance the robot's navigation capabilities in complex environments. One potential integration could involve combining the trajectory optimization approach with reinforcement learning algorithms to enable adaptive and learning-based navigation strategies. By leveraging reinforcement learning for policy optimization and decision-making, the robot can learn to navigate more efficiently and effectively in dynamic and uncertain environments. Furthermore, integrating the trajectory optimization method with model predictive control (MPC) can enable real-time trajectory adjustments based on feedback from the environment and sensors. This combination can enhance the robot's agility and responsiveness to changing conditions, leading to smoother and safer navigation in complex environments.