Safe expeditious whole-body control of mobile manipulators for collision avoidance

Abstract

Whole-body reactive obstacle avoidance for mobile manipulators (MM) remains an open research problem. Control Barrier Functions (CBF), combined with Quadratic Programming (QP), have become a popular approach for reactive control with safety guarantees. However, traditional CBF methods often face issues such as pseudo-equilibrium problems (PEP) and are ineffective in handling dynamic obstacles. To overcome these challenges, we introduce the Adaptive Cyclic Inequality (ACI) method. ACI takes into account both the obstacle’s velocity and the robot’s nominal control to define a directional safety constraint. When added to the CBF-QP, ACI helps avoid PEP and enables reliable collision avoidance in dynamic environments. We validate our approach on a mobile manipulator comprising both low-dimensional and high-dimensional subsystems, demonstrating the generality of the framework. In addition, we integrate a simple yet effective method for avoiding self-collisions, allowing the robot enabling comprehensive whole-body collision-free operation. Extensive benchmark comparisons and experiments demonstrate that our method performs well in unknown and dynamic scenarios, including difficult tasks like avoiding sticks swung by humans and rapidly thrown objects.

Keywords

Mobile manipulator / Robot safety / Collision avoidance / Control barrier functions / Quadratic programming

References

[1] G. Wang, W. Wang, P. Ding, Y. Liu, H. Wang, Z. Fan, H. Bai, Z. Hongbiao, Z. Du, Development of a search and rescue robot system for the underground building environment, J. Field Robot. 40 (3) (2023) 655-683.

[2] Z. Xie, L. Jin, X. Luo, M. Zhou, Y. Zheng, A biobjective scheme for kinematic control of mobile robotic arms with manipulability optimization, IEEE/ASME Trans. Mechatronics 29 (2) (2024) 1534-1545.

[3] P. Štibinger, G. Broughton, F. Majer, Z. Rozsypálek, A. Wang, K. Jindal, A. Zhou, D. Thakur, G. Loianno, T. Krajník, M. Saska, Mobile manipulator for autonomous localization, grasping and precise placement of construction material in a semi-structured environment, IEEE Robot. Autom. Lett. 6 (2) (2021) 2595-2602.

[4] C. Wang, Q. Zhang, Q. Tian, S. Li, X. Wang, D. Lane, Y. Petillot, S. Wang, Learning mobile manipulation through deep reinforcement learning, Sensors 20 (3) (2020) 939.

[5] C. Finn, Xin Yu Tan, Yan Duan, T. Darrell, S. Levine, P. Abbeel, Deep spatial autoencoders for visuomotor learning, 2016 IEEE International Conference on Robotics and Automation, ICRA, IEEE, Stockholm, Sweden, 2016, pp. 512-519.

[6] J. Haviland, N. Sünderhauf, P. Corke, A holistic approach to reactive mobile manipulation, IEEE Robot. Autom. Lett. 7 (2) (2022) 3122-3129.

[7] G. Rizzi, J.J. Chung, A. Gawel, L. Ott, M. Tognon, R. Siegwart, Robust sampling-based control of mobile manipulators for interaction with articulated objects, IEEE Trans. Robot. 39 (3) (2023) 1929-1946.

[8] Motion planning for robotics: A review for sampling-based planners, Biomim. Intell. Robot. 5 (1) (2025) 100207.

[9] W. Shaw Cortez, D. Oetomo, C. Manzie, P. Choong, Control barrier functions for mechanical systems: Theory and application to robotic grasping, IEEE Trans. Control Syst. Technol. 29 (2) (2021) 530-545.

[10] Z. Jian, Z. Yan, X. Lei, Z. Lu, B. Lan, X. Wang, B. Liang, Dynamic control barrier function-based model predictive control to safety-critical obstacle-avoidance of mobile robot, 2023 IEEE International Conference on Robotics and Automation, ICRA, IEEE, London, United Kingdom, 2023, pp. 3679-3685.

[11] P. Glotfelter, I. Buckley, M. Egerstedt, Hybrid nonsmooth barrier functions with applications to provably safe and composable collision avoidance for robotic systems, IEEE Robot. Autom. Lett. 4 (2) (2019) 1303-1310.

[12] B. Dai, R. Khorrambakht, P. Krishnamurthy, F. Khorrami, Differentiable optimization based time-varying control barrier functions for dynamic obstacle avoidance, Robot. Auton. Syst. 194 (2025) 105182.

[13] A. Bertino, P. Naseradinmousavi, M. Krstic, Experiment and design of prescribed-time safety filter for a 7-dof robot manipulator using CBF-QP, IFAC-PapersOnLine 55 (37) (2022) 481-487.

[14] X. Tan, D.V. Dimarogonas, On the undesired equilibria induced by control barrier function based quadratic programs, Automatica 159 (2024) 111359.

[15] M.F. Reis, A.P. Aguiar, P. Tabuada, Control barrier function-based quadratic programs introduce undesirable asymptotically stable equilibria, IEEE Control. Syst. Lett. 5 (2) (2021) 731-736.

[16] V.M. Gonçalves, P. Krishnamurthy, A. Tzes, F. Khorrami, Control barrier functions with circulation inequalities, IEEE Trans. Control Syst. Technol., 2024, pp. 1-16.

[17] J. Nie, Y. Wang, Safe obstacle avoidance planning-control scheme for multiconstrained mobile manipulators, IEEE Trans. Ind. Informatics, 2024, pp. 1-10.

[18] W. Li, R. Xiong, Dynamical obstacle avoidance of task- constrained mobile manipulation using model predictive control, IEEE Access 7 (2019) 88301-88311.

[19] J. Yan, M. Liu, Neural dynamics-based model predictive control for mobile redundant manipulators with improved obstacle avoidance, IEEE Trans. Ind. Electron., 2024, pp. 1-10.

[20] G. Wang, H. Ma, H. Wang, P. Ding, H. Bai, W. Xu, W. Wang, Z. Du, Reactive mobile manipulation based on dynamic dual-trajectory tracking, Robot. Auton. Syst. 172 (2024) 104589.

[21] B. Burgess-Limerick, J. Haviland, C. Lehnert, P. Corke, Reactive base control for on-the-move mobile manipulation in dynamic environments, IEEE Robot. Autom. Lett. 9 (3) (2024) 2048-2055.

[22] J. Haviland, P. Corke, NEO: A novel expeditious optimisation algorithm for reactive motion control of manipulators, IEEE Robot. Autom. Lett. 6 (2) (2021) 1043-1050.

[23] M. Fuchs, C. Borst, P. Giordano, A. Baumann, E. Kraemer, J. Langwald, R. Gruber, N. Seitz, G. Plank, K. Kunze, R. Burger, F. Schmidt, T. Wimboeck, G. Hirzinger, Rollin’ justin - design considerations and realization of a mobile platform for a humanoid upper body, 2009 IEEE International Conference on Robotics and Automation, IEEE, Kobe, 2009, pp. 4131-4137.

[24] I.A. Sucan, M. Moll, L.E. Kavraki, The open motion planning library, IEEE Robot. Autom. Mag. 19 (4) (2012) 72-82.

[25] Y. Liu, Z. Li, H. Su, C.-Y. Su, Whole-body control of an autonomous mobile manipulator using series elastic actuators, IEEE/ASME Trans. Mechatronics 26 (2) (2021) 657-667.

[26] H. Zhang, Q. Sheng, J. Hu, X. Sheng, Z. Xiong, X. Zhu, Cooperative transportation with mobile manipulator: A capability map-based framework for physical human–robot collaboration, IEEE/ASME Trans. Mechatronics 27 (6) (2022) 4396-4405.

[27] T. Sandakalum, M.H. Ang, Motion planning for mobile manipulators—A systematic review, Machines 10 (2) (2022) 97.

[28] M. Mashali, L. Wu, R. Alqasemi, R. Dubey, Controlling a non-holonomic mobile manipulator in a constrained floor space, 2018 IEEE International Conference on Robotics and Automation, ICRA, 2018, pp. 725-731.

[29] A.D. Ames, X. Xu, J.W. Grizzle, P. Tabuada, Control barrier function based quadratic programs for safety critical systems, IEEE Trans. Autom. Control 62 (8) (2017) 3861-3876.

[30] H. Zhang, Z. Li, A. Clark, Model-based reinforcement learning with provable safety guarantees via control barrier functions, 2021 IEEE International Conference on Robotics and Automation, ICRA, 2021, pp. 792-798.

[31] R. Herguedas, M. Aranda, G. López-Nicolás, C. Sagüés, Y. Mezouar, Multirobot control with double-integrator dynamics and control barrier functions for deformable object transport, 2022 International Conference on Robotics and Automation, ICRA, 2022, pp. 1485-1491.

[32] A. Singletary, K. Klingebiel, J. Bourne, A. Browning, P. Tokumaru, A. Ames, Comparative analysis of control barrier functions and artificial potential fields for obstacle avoidance, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, IEEE, Prague, Czech Republic, 2021, pp. 8129-8136.

[33] H.H. Bauschke, J.M. Borwein, On the convergence of von Neumann’s alternating projection algorithm for two sets, Set-Valued Anal. 1 (2) (1993) 185-212.

[34] P. Glotfelter, J. Cortés, M. Egerstedt, Nonsmooth barrier functions with applications to multi-robot systems, IEEE Control. Syst. Lett. 1 (2) (2017) 310-315.

[35] S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, (2004).

[36] P. Corke, J. Haviland, Not your grandmother’s toolbox–the robotics toolbox reinvented for python, 2021 IEEE International Conference on Robotics and Automation, ICRA, IEEE, 2021, pp. 11357-11363.