Robots are increasingly part of everyday life, from delivery robots on pavements to service robots in restaurants. Ensuring smooth motion while safely avoiding humans remains a challenge. Properly utilizing uncertainty-aware human trajectory prediction is one of the key aspects in achieving smooth robot motion among humans. Recent work on human trajectory prediction uses non-parametric distributions for human motion uncertainties, allowing for more diverse and accurate predictions. While leveraging advanced trajectory predictions could enhance robot motion planning performance, the literature on utilizing these results in mobile robot motion planning is sparse. Uncertainties modelled by Gaussian distributions (or Gaussian mixture models) can be treated in stochastic model predictive control (MPC) by tightening the constraints based on the uncertainty level sets quite straightforwardly. However, for more general (including non-parametric) distributions, parameterizing such uncertainties for constraint robustness is much more challenging. Scenario-based stochastic MPC provides a way to handle such uncertainty distributions, but this results in a large number of constraints, making the optimal control problem (OCP) difficult to solve. In this thesis, you will investigate appropriate ways of sampling and selecting the human uncertain trajectories to be imposed as constraints in the OCP, and assess the resulting computational efficiency and robot motion performance. The developed controller will be implemented as a nav2 plugin and deployed on a physical mobile robot. The objective is to showcase a demonstration in which the robot drives among people in the canteen at lunchtime, navigating through narrow, twist-and-turning corridors and avoiding moving people based on their motion predictions. You will get the prediction of human trajectories and the uncertainty measurements using state-of-the-art neural networks. Furthermore, you will formulate an OCP such that optimal robot trajectories probabilistically satisfy the collision avoidance constraints given the human trajectory predictions. In addition, you will solve the resulting OCP fast enough for real-time applications. Last but not least, you will experimentally validate your method on physical mobile robots around humans.