Robust Motion Planning

Robust motion planning aims to generate safe and reliable robot trajectories despite uncertainties in the environment, robot dynamics, or sensor data. Current research emphasizes improving the reliability and efficiency of trajectory optimization algorithms, often leveraging parallel computation and incorporating techniques like deep learning, control barrier functions, and Bayesian methods for uncertainty estimation and handling. These advancements are crucial for enabling safe and effective autonomous navigation in complex and unpredictable environments, with applications ranging from robotics and autonomous driving to spacecraft control.