Long-Horizon Geometry-Aware Navigation among Polytopes via MILP-MPC and Minkowski-Based CBFs

TL;DR

This paper introduces a hierarchical planning and control framework combining MILP-MPC and Minkowski-based CBFs for safe, long-horizon, geometry-aware robot navigation among polytopic obstacles.

Contribution

It integrates long-horizon MILP-MPC planning with geometry-aware CBF safety filters, explicitly considering robot shape and environment geometry.

Findings

Successfully navigates U-shaped and maze environments.

Mitigates local minima in reactive CBF navigation.

Enables real-time, safe, geometry-aware navigation.

Abstract

Autonomous navigation in complex, non-convex environments remains challenging when robot dynamics, control limits, and exact robot geometry must all be taken into account. In this paper, we propose a hierarchical planning and control framework that bridges long-horizon guidance and geometry-aware safety guarantees for a polytopic robot navigating among polytopic obstacles. At the high level, Mixed-Integer Linear Programming (MILP) is embedded within a Model Predictive Control (MPC) framework to generate a nominal trajectory around polytopic obstacles while modeling the robot as a point mass for computational tractability. At the low level, we employ a control barrier function (CBF) based on the exact signed distance in the Minkowski-difference space as a safety filter to explicitly enforce the geometric constraints of the robot shape, and further extend its formulation to a high-order CBF (HOCBF). We demonstrate the proposed framework in U-shaped and maze-like environments under single- and double-integrator dynamics. The results show that the proposed architecture mitigates the topology-induced local-minimum behavior of purely reactive CBF-based navigation while enabling safe, real-time, geometry-aware navigation.