Motion planning for Lunar Surface Person Tracking Robot via Flatness-Based Safe-MPC with Virtual Disturbances

As lunar exploration advances, astronauts face increasing challenges in completing surface missions efficiently. Person-following robots offer valuable assistance but remain limited by robustness, computational constraints, and safety requirements. To overcome these issues, this paper introduces a Flatness-Based Safe Model Predictive Control (Safe-MPC) framework enhanced with Virtual Disturbances. By exploiting the differential flatness property, the method reformulates the control problem in the flat output space, enabling efficient trajectory generation between set-points while respecting safety constraints. Virtual disturbances are incorporated to construct adaptive tubes, which are then leveraged within a Tube-MPC scheme to ensure reliable tracking of the reference path. This design not only improves robustness against uncertainties but also enhances real-time feasibility. Simulations in lunar environments verify that the proposed approach ensures reliable and safe target tracking, demonstrating its potential for astronaut-assistive robotics in future missions.