Smooth trajectory learning of teleoperated hydraulic manipulator with motion noise cancellation

Abstract

To automate heavy-duty hydraulic manipulators in construction applications, trajectory learning from demonstration is increasingly in demand. However, it faces difficulties in motion noise owing to factors such as size scaling and oscillation tendency. A smooth trajectory learning method is established to overcome this problem by segmenting the demonstration and extracting the subgoals for motion noise cancellation. The imperfect demonstration trajectory is segmented by clustering the end-effector’s velocity in the task space with locally weighted noise cancellation to reduce the impact of velocity fluctuations. A sequentially hierarchical Dirichlet process algorithm with temporal encoding is designed to extract the intended subgoals and filter inefficient operations. Then, the learned trajectory is reconstructed, combined with dynamic motion primitives (DMP). The comparison test results indicate that the proposed method can learn a relevant trajectory that reflects the real intention of the user from an imperfect demonstration. Taking DMP and Sparse Sampling as comparisons, two cases of automatic trajectory tracking tasks are performed, which shows that the average position error with respect to the reference can be reduced because inefficient operations or movements are effectively filtered.

Graphical abstract

Keywords

hydraulic manipulator / learning from demonstration / teleoperation / Dirichlet process

References

[1] Shen Y Y, Wang J X, Feng C L, Wang Q. Hybrid-driven autonomous excavator trajectory generation combining empirical driver skills and optimization. Automation in Construction, 2024, 165: 105523

[2] Cheng M, He J B, Mu H Y, Yan X, Yang J, Ou J H, Ding R Q, Zhang J H, Xu B. Opportunities and challenges of electrohydraulic control systems in the electrification era of non-road mobile machinery. Journal of Advanced Manufacturing Science and Technology, 2024, 4(2): 2024001

[3] Feng H, Yin C B, Ma W, Yu H F, Cao D H. Parameters identification and trajectory control for a hydraulic system. ISA Transactions, 2019, 92: 228–240

[4] Soltani M M, Zhu Z H, Hammad A. Skeleton estimation of excavator by detecting its parts. Automation in Construction, 2017, 82: 1–15

[5] Yang J, Shi Z K, Wu Z Y. Vision-based action recognition of construction workers using dense trajectories. Advanced Engineering Informatics, 2016, 30(3): 327–336

[6] La Hera P, Ortiz Morales D, Mendoza-Trejo O. A study case of Dynamic Motion Primitives as a motion planning method to automate the work of forestry cranes. Computers and Electronics in Agriculture, 2021, 183: 106037

[7] Melenbrink N, Werfel J, Menges A. On-site autonomous construction robots: Towards unsupervised building. Automation in Construction, 2020, 119: 103312

[8] Jin Z Y, Pagilla P R. Shared control with efficient subgoal identification and adjustment for human–robot collaborative tasks. IEEE Transactions on Control Systems Technology, 2022, 30(1): 326–335

[9] Feng H, Yin C B, Li R, Ma W, Yu H F, Cao D H, Zhou J J. Flexible virtual fixtures for human-excavator cooperative system. Automation in Construction, 2019, 106: 102897

[10] Luo S Q, Cheng M, Ding R Q, Wang F, Xu B, Chen B K. Human–robot shared control based on locally weighted intent prediction for a teleoperated hydraulic manipulator system. IEEE/ASME Transactions on Mechatronics, 2022, 27(6): 4462–4474

[11] Zeestraten M J A, Havoutis I, Calinon S. Programming by demonstration for shared control with an application in teleoperation. IEEE Robotics and Automation Letters, 2018, 3(3): 1848–1855

[12] Raiola G, Restrepo S S, Chevalier P, Rodriguez-Ayerbe P, Lamy X, Tliba S, Stulp F. Co-manipulation with a library of virtual guiding fixtures. Autonomous Robots, 2018, 42(5): 1037–1051

[13] Lee J S, Ham Y, Park H, Kim J. Challenges, tasks, and opportunities in teleoperation of excavator toward human-in-the-loop construction automation. Automation in Construction, 2022, 135: 104119

[14] Cheng M, Li R M, Ding R Q, Xu B. Multitarget adaptive virtual fixture based on task learning for hydraulic manipulator. Journal of Field Robotics, 2024, 41(8): 2715–2731

[15] Zare M, Kebria P M, Khosravi A, Nahavandi S. A survey of imitation learning: Algorithms, recent developments, and challenges. IEEE Transactions on Cybernetics, 2024, 54(12): 7173–7186

[16] Park S W, Marino H, Charles S K, Sternad D, Hogan N. Moving slowly is hard for humans: limitations of dynamic primitives. Journal of Neurophysiology, 2017, 118(1): 69–83

[17] Adhikari K, Tatinati S, Veluvolu K C, Chambers J A. Physiological tremor filtering without phase distortion for robotic microsurgery. IEEE Transactions on Automation Science and Engineering, 2022, 19(1): 497–509

[18] Zanoni A, Zago M, Paolini R, Quaranta G, Galli M, Masarati P. On task dependence of helicopter pilot biodynamic feedthrough and neuromuscular admittance: an experimental and numerical study. IEEE Transactions on Human-Machine Systems, 2021, 51(5): 421–431

[19] Xu B, Cheng M. Motion control of multi-actuator hydraulic systems for mobile machineries: Recent advancements and future trends. Frontiers of Mechanical Engineering, 2018, 13(2): 151–166

[20] Calinon S, Guenter F, Billard A. On learning, representing, and generalizing a task in a humanoid robot. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2007, 37(2): 286–298

[21] Ijspeert A J, Nakanishi J, Hoffmann H, Pastor P, Schaal S. Dynamical movement primitives: learning attractor models for motor behaviors. Neural Computation, 2013, 25(2): 328–373

[22] Frank F, Paraschos A, van der Smagt P, Cseke B. Constrained probabilistic movement primitives for robot trajectory adaptation. IEEE Transactions on Robotics, 2022, 38(4): 2276–2294

[23] Yin X C, Chen Q J. Trajectory generation with spatio-temporal templates learned from demonstrations. IEEE Transactions on Industrial Electronics, 2017, 64(4): 3442–3451

[24] Liang C J, Kamat V R, Menassa C C. Teaching robots to perform quasi-repetitive construction tasks through human demonstration. Automation in Construction, 2020, 120: 103370

[25] Song C W, Liu G F, Zhang X H, Zang X Z, Xu C C, Zhao J. Robot complex motion learning based on unsupervised trajectory segmentation and movement primitives. ISA Transactions, 2020, 97: 325–335

[26] Li J C, Ran M P, Xie L H. Efficient trajectory planning for multiple non-holonomic mobile robots via prioritized trajectory optimization. IEEE Robotics and Automation Letters, 2021, 6(2): 405–412

[27] Michini B, Walsh T J, Agha-Mohammadi A A, How J P. Bayesian nonparametric reward learning from demonstration. IEEE Transactions on Robotics, 2015, 31(2): 369–386

[28] Tavakol Aghaei V, Ağababaoğlu A, Yıldırım S, Onat A. A real-world application of Markov chain Monte Carlo method for Bayesian trajectory control of a robotic manipulator. ISA Transactions, 2022, 125: 580–590

[29] Rodrigues da Silva J A, Grassi V, Wolf D F. Maximum entropy inverse reinforcement learning using Monte Carlo tree search for autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(9): 11552–11562

[30] Zhang J, Li W, Yu J C, Zhang Q F, Cui S G, Li Y, Li S, Chen G S. Development of a virtual platform for telepresence control of an underwater manipulator mounted on a submersible vehicle. IEEE Transactions on Industrial Electronics, 2017, 64(2): 1716–1727

[31] Jin Z Y, Pagilla P R, Maske H, Chowdhary G. Task learning, intent prediction, and adaptive blended shared control with application to excavators. IEEE Transactions on Control Systems Technology, 2021, 29(1): 18–28

[32] Cheng M, Han Z, Ding R Q, Zhang J H, Xu B. Development of a redundant anthropomorphic hydraulically actuated manipulator with a roll-pitch-yaw spherical wrist. Frontiers of Mechanical Engineering, 2021, 16(4): 698–710

[33] Sun B L, Cheng M, Ding R Q, Xu B. Compliance motion control of the hydraulic dual-arm manipulator with adaptive mass estimation of unknown object. Frontiers of Mechanical Engineering, 2024, 19(1): 7

[34] MohammadiA, Tavakoli M, JazayeriA. Phansim: A simulink toolkit for the sensable phantom haptic devices. In: Proceedings of 23rd Canadian Congress of Applied Mechanics. Vancouver: Canada, 2011, 787–790

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