Abstract
Mobile manipulators, which are intrinsically redundant when the manipulator and mobile base are moving together, are known for their capabilities to carry out multiple tasks at the same time. This paper presents a whole-body control framework, inspired by legged bio-robots, for a velocity controlled non-holonomic mobile manipulator based on task priority. Control primitives, such as manipulability optimization, trajectory tracking of the end-effector and mobile base, and collision avoidance, are considered in the framework and arranged at different priorities. Lower priority tasks are projected into the null space of control tasks with higher priorities. As a result, lower level tasks are completed without affecting the performance of higher priority tasks. Several experiments are implemented to verify the effectiveness of the proposed controller. The proposed method is proved to be an effective way to solve the whole-body control problem of velocity controlled mobile manipulators.
Similar content being viewed by others
References
Metta G, Natale L, Nori F, et al. The iCub humanoid robot: an open-systems platform for research in cognitive development. Neural Netw, 2010, 23: 1125–1134
Zhou C, Wang X, Li Z, et al. Overview of gait synthesis for the humanoid COMAN. J Bionic Eng, 2017, 14: 15–25
Sakagami Y, Watanabe R, Aoyama C, et al. The intelligent ASIMO: system overview and integration. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002. 2478–2483
Nishiwaki K, Kagami S, Inoue H. Object manipulation by hand using whole-body motion coordination. In: Proceedings of IEEE International Conference on Mechatronics & Automation, 2005. 1778–1783
He W, Zhang S. Control design for nonlinear flexible wings of a robotic aircraft. IEEE Trans Contr Syst Technol, 2017, 25: 351–357
Guo W, Cai C, Li M, et al. A parallel actuated pantograph leg for high-speed locomotion. J Bionic Eng, 2017, 14: 202–217
Robuffo G P, Fuchs M, Alin A S, et al. On the kinematic modeling and control of a mobile platform equipped with steering wheels and movable legs. In: Proceedings of IEEE International Conference on Robotics and Automation, 2009. 4080–4087
Asfour T, Regenstein K, Azad P, et al. ARMAR-III: an integrated humanoid platform for sensory-motor control. In: Proceedings of IEEE/RAS International Conference on Humanoid Robots, 2006. 169–175
Ruggiero F, Petit A, Serra D, et al. Nonprehensile manipulation of deformable objects: achievements and perspectives from the robotic dynamic manipulation project. IEEE Robot Automat Mag, 2018, 25: 83–92
Ellekilde L P, Christensen H I. Control of mobile manipulation using the dynamical systems approach. In: Proceedings of IEEE International Conference on Robotics and Automation, 2009. 1370–1376
Hvilshøj M, Bøgh S, Madsen O, et al. The mobile robot “Little Helper”: concepts, ideas and working principles. In: Proceedings of IEEE International Conference on Emerging Technologies & Factory Automation, 2009. 1–4
Chen J, Kai S X. Cooperative transportation control of multiple mobile manipulators through distributed optimization. Sci China Inf Sci, 2018, 61: 120201
Tao B, Zhao X W, Ding H. Mobile-robotic machining for large complex components: a review study. Sci China Technol Sci, 2019, 62: 1388–1400
Chen F, Selvaggio M, Caldwell D G. Dexterous grasping by manipulability selection for mobile manipulator with visual guidance. IEEE Trans Ind Inf, 2019, 15: 1202–1210
Chen F, Gao B, Selvaggio M, et al. A framework of teleoperated and sterio vision guided mobile manipulation for industrial automation. In: Proceedings of IEEE International Conference on Mechatronics and Automation, 2016. 1641–1648
Siciliano B, Sciavicco L, Villani L, et al. Robotics: Modelling, Planning and Control. London: Springer, 2009. 502–506
Siciliano B, Slotine J J E. A general framework for managing multiple tasks in highly redundant robotic systems. In: Proceedings of IEEE International Conference on Advanced Robotics, 1991. 1211–1216
Nakamura Y, Hanafusa H, Yoshikawa T. Task-priority based control of robot manipulators. Int J Robot Res, 1987, 6: 3–15
Khatib O, Sentis L, Park J, et al. Whole-body dynamic behavior and control of human-like robots. Int J Human Robot, 2004, 1: 29–43
Sentis L, Khatib O. Synthesis of whole-body behaviors through hierarchical control of behavioral primitives. Int J Human Robot, 2005, 2: 505–518
Sentis L, Khatib O. A whole-body control framework for humanoids operating in human environments. In: Proceedings of IEEE International Conference on Robotics and Automation, 2006. 2641–2647
Mansard N, Khatib O, Kheddar A. A unified approach to integrate unilateral constraints in the stack of tasks. IEEE Trans Robot, 2009, 25: 670–685
Dietrich A, Thomas W, Alin A S. Dynamic whole-body mobile manipulation with a torque controlled humanoid robot via impedance control laws. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems, 2011. 3199–3206
Borst C, Thomas W, Schmidt F, et al. Rollin’ Justin-mobile platform with variable base. In: Proceedings of IEEE International Conference on Robotics and Automation, 2009. 1597–1598
Dietrich A, Bussmann K, Petit F, et al. Whole-body impedance control of wheeled mobile manipulators. Auton Robot, 2016, 40: 505–517
Fonseca M D P A, Adorno B V. Whole-body modeling and hierarchical control of a humanoid robot based on dual quaternion algebra. In: Proceedings of XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR), 2016. 505–517
Silva F F A, Adorno B V. Whole-body control of a mobile manipulator using feedback linearization and dual quaternion algebra. J Intell Robot Syst, 2018, 91: 249–262
Seraji H. An on-line approach to coordinated mobility and manipulation. In: Proceedings of IEEE International Conference on Robotics and Automation, 1993. 28–35
Tan J, Xi N. Unified model approach for planning and control of mobile manipulators. In: Proceedings of IEEE International Conference on Robotics and Automation, 2001. 3145–3152
Brock O, Khatib O, Viji S. Task-consistent obstacle avoidance and motion behavior for mobile manipulation. In: Proceedings of IEEE Interantional Conference on Robotics and Automation, 2002. 388–393
Avanzini G B, Zanchettin A M, Rocco P. Constraint-based model predictive control for holonomic mobile manipulators. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robotics and Systems, 2015. 1473–1479
Yamamoto Y, Yun X. Coordinating locomotion and manipulation of a mobile manipulator. IEEE Trans Autom Control, 1994, 39: 1326–1332
Padois V, Fourquet J Y, Chiron P. Kinematic and dynamic model-based control of wheeled mobile manipulators: a unified framework for reactive approaches. Robotica, 2007, 25: 157–173
Baillieul J. Kinematic programming alternatives for redundant manipulators. In: Proceedings of IEEE International Conference on Robotics and Automation, 1985. 722–728
Dietrich A, Ott C, Albu-Schäffer A. An overview of null space projections for redundant, torque-controlled robots. Int J Robot Res, 2015, 34: 1385–1400
Kevin M L, Frank C P. Modern Robotics: Mechanics, Planning, and Control. Cambridge: Cambridge University Press, 2017. 413–420
Gilbert E G, Johnson D W, Keerthi S S. A fast procedure for computing the distance between complex objects in three-dimensional space. IEEE J Robot Automat, 1988, 4: 193–203
Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res, 1986, 5: 90–98
Alexander D, Thoms W, Holger T, et al. Extensions to reactive self-collision avoidance for torque and position controlled humanoids. In: Proceedings of IEEE International Conference on Robotics and Automation, 2011. 3445–3462
Merlet J P. Jacobian, manipulability, condition number, and accuracy of parallel robots. J Mech Des, 2006, 128: 199–206
Markus G, Farbod F, Timothy S, et al. Efficient kinematic planning for mobile manipulators with non-holonomic constraints using optimal control. In: Proceedings of IEEE International Conference on Robotics and Automation, 2017. 3411–3417
Nakanishi J, Cory R, Mistry M, et al. Operational space control: a theoretical and empirical comparison. Int J Robot Res, 2008, 27: 737–757
Yang C, Peng G, Li Y, et al. Neural networks enhanced adaptive admittance control of optimized robot-environment interaction. IEEE Trans Cybern, 2019, 49: 2568–2579
He W, Dong Y. Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Trans Neural Netw Learn Syst, 2018, 29: 1174–1186
Zhang S, Dong Y, Ouyang Y, et al. Adaptive neural control for robotic manipulators with output constraints and uncertainties. IEEE Trans Neural Netw Learn Syst, 2018, 29: 5554–5564
Zhang S, Yang P, Kong L, et al. Neural networks-based fault tolerant control of a robot via fast terminal sliding mode. IEEE Trans Syst Man Cybern Syst, 2019. doi: 10.1109/TSMC.2019.2933050
Argall B D, Chernova S, Veloso M, et al. A survey of robot learning from demonstration. Robot Autonom Syst, 2009, 57: 469–483
Yang C, Chen C, Wang N, et al. Biologically inspired motion modeling and neural control for robot learning from demonstrations. IEEE Trans Cogn Dev Syst, 2019, 11: 281–291
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 61773139), Shenzhen Science and Technology Program (Grant No. KQTD2016112515134654), Shenzhen Special Fund for Future Industrial Development (Grant No. JCYJ20160425150757025), and Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (Grant No. ICT1900357).
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Li, M., Yang, Z., Zha, F. et al. Design and analysis of a whole-body controller for a velocity controlled robot mobile manipulator. Sci. China Inf. Sci. 63, 170204 (2020). https://doi.org/10.1007/s11432-019-2741-6
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-019-2741-6