An NMPC Approach using Convex Inner Approximations for Online Motion Planning with Guaranteed Collision Avoidance

TL;DR

This paper introduces CIAO, a convex inner approximation method within an NMPC framework, enabling efficient, feasible, and collision-free motion planning for robots, including high-dimensional and dynamic environments.

Contribution

The paper presents a novel convex inner approximation technique for collision avoidance in NMPC, ensuring feasibility and efficiency in motion planning.

Findings

CIAO outperforms state-of-the-art baselines in efficiency and path quality.

The method scales to high-dimensional systems with 12 states.

Real-world experiments validate safe motion planning in dynamic environments.

Abstract

Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remain subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a nonlinear model predictive control (NMPC) framework, that is based on a novel convex inner approximation of the collision avoidance constraint. The proposed Convex Inner ApprOximation (CIAO) method finds kinodynamically feasible and continuous time collision free trajectories, in few iterations, typically one. For a feasible initialization, the approach is guaranteed to find a feasible solution, i.e. it preserves feasibility. Our experimental evaluation shows that CIAO outperforms state of the art baselines in terms of planning efficiency and path quality. Experiments on a robot with 12 states show that it also scales to high-dimensional systems. Furthermore real-world experiments demonstrate its capability of unifying trajectory optimization and tracking for safe motion planning in dynamic environments.