Motion Optimization

Motion optimization in robotics aims to generate efficient, safe, and collision-free robot movements, often addressing complex scenarios involving multiple objectives and dynamic environments. Current research emphasizes integrating learning-based methods, such as neural networks (including recurrent and diffusion models) and evolutionary algorithms, with traditional optimization techniques like A* and Model Predictive Control (MPC) to improve speed, robustness, and adaptability. These advancements are crucial for enhancing robot autonomy and performance in diverse applications, from manipulation and locomotion to human-robot collaboration and aerial base station deployment.