From estimation to control for robotic navigation: Probabilistic and optimal approaches

Nowadays, mobile robots capable of autonomous navigation and interaction in unfamiliar and dynamic environments have received great attention among researchers. The robot must be able to precisely perceive its environment, make appropriate inference, plan its path, and travel around safely in order to achieve this goal. In robotics, maneuvering in a complex setting has been challenging. Several methods propose robust architectures in which the agent acts conservative in respect to uncertainty by considering worst case scenario, while others provide adaptive policies which try to adjust the actions given the concurrent knowledge. The usually suffers from guaranteed stability and efficiency in data. The novelty in this report is two folded: the first is to suggest a probabilistic framework for estimating environmental hazards and dynamic models. By improving MCMC, we propose an online method to obtain the model parameters distribution. The second novelty, is to propose an inference model and update framework for human navigational intent. We will discuss how one can apply these insights in a safe path planning problem by considering the environment's uncertainty in a probabilistic manner.