Uncertain Obstacle

Uncertain obstacle navigation focuses on enabling robots to safely and efficiently plan paths in environments with imprecisely known obstacles, addressing limitations of traditional methods that assume perfect knowledge. Current research emphasizes robust motion planning algorithms, often employing model predictive control or probabilistic methods like Bayes filters, to handle various uncertainty representations (e.g., Gaussian, polytopic, or arbitrary distributions with known moments). These advancements are crucial for deploying robots in real-world scenarios, improving safety and reliability in dynamic and unpredictable environments, such as autonomous driving and warehouse automation.