  This paper presents a method for local motion planning in unstructured
environments with static and moving obstacles, such as humans. Given a
reference path and speed, our optimization-based receding-horizon approach
computes a local trajectory that minimizes the tracking error while avoiding
obstacles. We build on nonlinear model-predictive contouring control (MPCC) and
extend it to incorporate a static map by computing, online, a set of convex
regions in free space. We model moving obstacles as ellipsoids and provide a
correct bound to approximate the collision region, given by the Minkowsky sum
of an ellipse and a circle. Our framework is agnostic to the robot model. We
present experimental results with a mobile robot navigating in indoor
environments populated with humans. Our method is executed fully onboard
without the need of external support and can be applied to other robot
morphologies such as autonomous cars.
