Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments

TL;DR

This paper introduces an optimization-based local motion planning method using model predictive contouring control for collision avoidance in unstructured environments with static and moving obstacles, demonstrated on a mobile robot.

Contribution

It extends nonlinear MPCC to include static maps and moving obstacles modeled as ellipsoids, enabling onboard collision avoidance in dynamic, unstructured environments.

Findings

Successfully navigates indoor environments with humans

Operates fully onboard without external support

Applicable to various robot types like autonomous cars

Abstract

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.