Enhancing Autonomous Door Traversal for Mobile Manipulators Using Quadratic Programming and Cartesian Compliance Control

Autonomous door traversal for mobile manipulators poses significant challenges owing to environmental uncertainties and positioning errors that can hinder precise control of the end effector. This study presents a novel control framework that combines Quadratic Programming (QP) with Cartesian Compliance Control (CCC) to enhance the performance of mobile manipulators in door-opening tasks. The QP formulation optimizes the control objectives and constraints, allowing for effective coordination between the mobile base and manipulator arm. CCC was employed to adapt to external forces and uncertainties, providing smoother and more precise interactions with the door. The proposed approach was validated through extensive simulations and real-world experiments. In the experiment, the performance of Velocity Control (VC) and CCC was quantitatively compared. CCC significantly reduced the applied force on the door, with a maximum force of 4.4 N compared to 11.9 N for VC. These results demonstrate that CCC offers better adaptability, force control, and trajectory accuracy for dynamic door-opening tasks. Overall, the integrated QP-CCC framework provides superior precision, robustness, and efficiency compared with traditional methods, contributing to advancements in autonomous robotic manipulation in complex environments.