Motion Planning for Mobile Manipulators Navigating Doorways via Model Predictive Control

2026-07-31

AI Summary

This work addresses the challenge of enabling mobile manipulators to autonomously traverse doorways in human environments by coordinating base and arm motions for pushing or pulling doors. The authors propose a nonlinear model predictive control (MPC) framework that models the robot and the door as a coupled dynamical system to generate dynamically feasible and collision-free trajectories through the doorway. A key innovation is the incorporation of a penalty-based constraint mechanism, which ensures task feasibility without explicitly modeling the manipulator’s kinematics, thereby substantially simplifying the underlying optimization problem. Both simulation and real-world hardware experiments demonstrate that the approach efficiently produces safe and executable trajectories for door traversal under both pushing and pulling scenarios.

Abstract

Navigating doorways is a fundamental capability for mobile manipulators operating in human environments, requiring coordinated motion between the mobile base and manipulator arm. This paper presents a motion planning framework that generates dynamically feasible and collision-free trajectories for autonomously opening and traversing both push and pull doors. The proposed method formulates the robot and door as a coupled dynamical system within a nonlinear Model Predictive Control (MPC) optimization framework. Manipulation feasibility is enforced through a penalty-based constraint, avoiding explicit arm kinematic modeling in the planner. Simulations and a hardware experiment demonstrate that the approach successfully plans feasible trajectories for door traversal.

Problem

• mobile manipulators
• doorway navigation
• motion planning
• collision-free trajectories
• door manipulation

Innovation

• Model Predictive Control
• Mobile Manipulator
• Door Navigation
• Coupled Dynamical System
• Penalty-based Constraint