LAUSR

This letter presents a sequential Model Predictive Control (MPC) approach to reactive motion planning for bipedal robots in highly cluttered environments with moving obstacles. The approach relies on a directed convex decomposition of the free space, which provides a safe corridor in the form of an ordered collection of mutually intersecting obstacle-free polytopes and waypoints. These are subsequently used to define a corresponding sequence of MPC programs that drive the system to a goal location avoiding static and moving obstacles. This way, the planner focuses on the free space in the vicinity of the robot, thus alleviating the need to consider all the obstacles simultaneously and reducing computational time. We verify the efficacy of our approach in high-fidelity simulations with the bipedal robot Digit, demonstrating robust reactive planning and trajectory realization amidst static and moving obstacles.