Stochastic Trajectory Optimization

Stochastic trajectory optimization focuses on finding optimal robot movements in uncertain environments, aiming for efficient and safe paths while accounting for unpredictable factors. Current research emphasizes robust methods, incorporating chance constraints for safety and techniques like Monte Carlo approximation and Gaussian processes to handle uncertainty, as well as strategies to improve efficiency through parallel optimization and diverse solution exploration using path signatures or via-point based approaches. These advancements are crucial for enabling more reliable and adaptable robot behavior in complex, real-world scenarios, impacting fields like robotics, autonomous navigation, and control systems.