Adaptive Admittance-Impedance Switching Control for Safe Mobile Manipulation: A Door Opening Application with Predictive Anti-Slip Logic

Abstract

Robotic manipulation in contact-rich environments demands control mechanisms that guarantee the stability, safety, and adaptability of the robots, even in the presence of uncertain dynamics. The present work aims to outline an advanced compliant control architecture for mobile manipulators, particularly focusing on safe human-robot interaction and task execution reliability. The proposed control architecture utilizes an adaptive impedance-admittance switching control scheme, where the control modes are adapted according to real-time estimations of the environmental stiffness and Cartesian inertia properties. A vision-driven perception scheme, implemented by utilizing the vectorized RGB-D ray tracing model, allows for accurate 3D localization even in the presence of sensing noise. To increase the reliability of the interaction, the proposed control scheme utilizes the admittance compliance model, along with adaptive stiffness, damping, and inertia properties. A predictive anti-slip gripping scheme has also been proposed to guarantee the slip probability to be below 15% by regulating the forces according to friction and load estimations. Additionally, the proposed control architecture utilizes the reinforcement learning principle to adapt the gains to guarantee the best performance, particularly focusing on the trade-offs between task execution, stability, and slip avoidance. The proposed architecture has been simulated using the MATLAB simulation environment, where the performance of the proposed architecture has been evaluated by considering door-opening tasks by the mobile manipulator, even in the presence of realistic physical constraints.

Introduction

This research focuses on improving robotic manipulation in complex, contact-rich environments, especially for high-precision assembly tasks. Traditional rigid control methods struggle with environmental uncertainty and varying stiffness, leading to the use of impedance and admittance control. However, since real-world environments are not purely stiff or compliant, a hybrid switching control approach is proposed.

The study introduces an enhanced impedance–admittance switching controller that smoothly transitions between control modes based on environmental stiffness, ensuring stable and continuous force interaction. In parallel, it integrates vision-based perception (RGB-D and 6D pose tracking) to enable accurate manipulation without relying on expensive force sensors, achieving precision up to 0.25 mm.

A key contribution is a unified simulation framework (implemented in MATLAB/Simscape) that combines adaptive control and vision feedback for tasks like peg-in-hole assembly and door opening. The system is designed for safe human-robot interaction, complying with ISO/TS 15066 standards.

The research specifically addresses challenges such as:

• Maintaining safe and effective force during contact-heavy tasks
• Adapting to unknown and changing environments
• Ensing stable grasp without slippage
• Coordinating multi-stage tasks (e.g., door opening)
• Guaranteeing human safety in shared spaces

To solve these, the proposed system integrates:

• Adaptive hybrid control (impedance–admittance switching)
• Vision-driven perception
• Predictive anti-slip grip control
• Finite state machine for task sequencing

The study also identifies gaps in existing research, including lack of real-time hybrid switching, predictive grasp stability, and multi-phase safety integration.

Conclusion

Safety Compliance: Safety compliance is ensured through the integrated safety architecture, which includes inherent compliance, predictive safety, and hard limits. This ensures compliance with the ISO/TS 15066 standard, which is applicable to collaborative robots. This is achieved through the layered safety concept, which ensures that there is no point of failure: 1) Adaptive Switching Controller: The Formenti model-based approach for adapting the duty cycle was instrumental in switching seamlessly between admittance and impedance control modes according to real-time environmental stiffness estimates. This feature was particularly valuable in dealing with the significant changes in environmental stiffness during door opening, from free space to stiff contact with massive objects. 2) Predictive Anti-Slip Logic: The predictive approach for maintaining grasp stability using load prediction and Stribeck friction models was highly effective in keeping slip percentages below 15%. This approach was more effective than traditional reactive control strategies in maintaining stability. 3) 3Multi-Phase Task Orchestration: The finite state machine with deliberate pauses at 30° and 60° was effective in accumulating adequate forces while ensuring that peak forces were well below safety limits (actual 90 N vs. limit 150 N). This hierarchical approach effectively decomposed the complex task into simpler phases with clear safety milestones. 4) Comprehensive Validation: Realistic simulation with realistic door dynamics (solid oak, 40 kg) and performance metric logging were effective in validating that the task was completed in 12.35 s with all safety and performance metrics satisfied. A comparison with other approaches (baseline admittance and impedance control) also demonstrated the effectiveness of the proposed approach. 5) Safety Compliance: Safety compliance is ensured through the integrated safety architecture, which includes inherent compliance, predictive safety, and hard limits. This ensures compliance with the ISO/TS 15066 standard, which is applicable to collaborative robots. This is achieved through the layered safety concept, which ensures that no single point of failure compromises safety.

References

[1] A. Ghanbarzadeh et al., “Safe Physical Human-Robot Interaction through Variable Impedance Control based on ISO/TS 15066,” arXiv preprint arXiv:2311.13814, 2023. [2] Safety and Performance of Industrial Collaborative Robots: Bounded Compensation in Precision Motion Control,” TechRxiv preprint, 2023. DOI: 10.36227/techrxiv.23498829 [3] X. Tu et al., “Whole-Body Control for Velocity-Controlled Mobile Collaborative Robots Using Coupling Dynamic Movement Primitives,” 2022. [4] R. Wen et al., “Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control,” IEEE Robotics & Automation Magazine, 2022. DOI: 10.1109/MRA.2023.3270222 [5] F. Tassi et al., “An adaptive compliance hierarchical quadratic programming controller for ergonomic human–robot collaboration,” Robotics and Computer-Integrated Manufacturing, vol. 78, p. 102381, 2022. DOI: 10.1016/j.rcim.2022.102381 [6] A. Anand et al., “Model-based variable impedance learning control for robotic manipulation,” Robotics and Autonomous Systems, vol. 171, p. 104531, 2023. DOI: 10.1016/j.robot.2023.104531 [7] H. Jun et al., “A strategy for human-robot collaboration in taking products apart for remanufacture,” FME Transactions, vol. 47, no. 4, pp. 731-738, 2019. DOI: 10.5937/FMET1904731H [8] W. Yan et al., “A Unified Interaction Control Framework for Safe Robotic Ultrasound Scanning with Human-Intention-Aware Compliance,” 2024. [9] Z. Fu et al., “Maxwell Model-Based Null Space Compliance Control in the Task-Priority Framework for Redundant Manipulators,” IEEE Access, vol. 8, pp. 60499-60511, 2020. DOI: 10.1109/ACCESS.2020.2975125 [10] A. Dobrovolskiy, “A Fuzzy Variable Admittance Control Method to Ensure Compliance Motion of a Cooperative Robot,” in Lecture Notes in Mechanical Engineering, pp. 1681-1690, 2023. DOI: 10.1007/978-981-99-0479-2_155 [11] Y. Sun et al., “Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints,” arXiv preprint arXiv:2303.02456, 2023. [12] “A Complementary Framework for Human-Robot Collaboration with a Mixed AR-Haptic Interface,” arXiv preprint arXiv:2210.06003, 2022. [13] H. Wang et al., “Impedance Control Strategy and Experimental Analysis of Collaborative Robots Based on Torque Feedback,” in Proc. IEEE Int. Conf. Robotics and Biomimetics (ROBIO), pp. 2346-2351, 2019. DOI: 10.1109/ROBIO49542.2019.8961470 [14] X. Ding et al., “Robust adaptive control of door opening by a mobile rescue manipulator based on unknown-force-related constraints estimation,” Robotica, vol. 36, no. 6, pp. 767-790, 2018. DOI: 10.1017/S0263574717000200 [15] D. Reyes-Uquillas et al., “Safe and intuitive manual guidance of a robot manipulator using adaptive admittance control towards robot agility,” Robotics and Computer-Integrated Manufacturing, vol. 70, p. 102127, 2021. DOI: 10.1016/J.RCIM.2021.102127 [16] Y. Chen et al., “Compliance while resisting: a shear-thickening fluid controller for physical human-robot interaction,” The International Journal of Robotics Research, 2025. DOI: 10.1177/02783649241234364 [17] J. Stückler et al., “Towards robust mobility, flexible object manipulation, and intuitive multimodal interaction for domestic service robots,” in RoboCup 2011: Robot Soccer World Cup XV, pp. 51-62, 2012. DOI: 10.1007/978-3-642-32060-6_5 [18] C. Ott et al., “Autonomous opening of a door with a mobile manipulator: A case study,” IFAC Proceedings Volumes, vol. 40, no. 15, pp. 349-354, 2007. DOI: 10.3182/20070903-3-FR-2921.00060 [19] B. Nemec et al., “Human robot cooperation with compliance adaptation along the motion trajectory,” Autonomous Robots, vol. 42, no. 5, pp. 1023-1035, 2018. DOI: 10.1007/S10514-017-9676-3 [20] Y. Li et al., “Stable and compliant motion of physical human–robot interaction coupled with a moving environment using variable admittance and adaptive control,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3409-3416, 2018. DOI: 10.1109/LRA.2018.2812916 [21] J. Kang et al., “A Versatile Door Opening System with Mobile Manipulator through Adaptive Position-Force Control and Reinforcement Learning,” Robotics and Autonomous Systems, vol. 180, p. 104760, 2024. DOI: 10.1016/j.robot.2024.104760 [22] E. Akdogan et al., “Hybrid impedance control of a robot manipulator for wrist and forearm rehabilitation: Performance analysis and clinical results,” Mechatronics, vol. 49, pp. 77-91, 2018. DOI: 10.1016/J.MECHATRONICS.2017.12.001

Copyright

Copyright © 2026 Amit Sable, Dr. S. S. Ohol. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.