Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments
This addresses safe navigation for robots in dynamic, human-populated environments, but it is incremental as it builds on existing MPCC methods.
The paper tackles local motion planning for robots in unstructured environments with static and moving obstacles by extending nonlinear model-predictive contouring control to compute convex free-space regions online, achieving collision avoidance while minimizing tracking error, with experimental validation on a mobile robot navigating among humans.
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.