Abstract
In this article, a robot collision detection method based on the long short-term memory (LSTM) network is proposed. The method constructed an LSTM network based external torque prediction (LSTM-ETP) model can perform multi-parameter multi-step external torque prediction since the architecture of model is described in network and time dimensions. The multi-step prediction of external torque is performed sequentially at different time nodes, which can transform the single multi-step output into a continuous prediction output on the complete time scale, and achieve real-time collision detection based on the LSTM-ETP model. The model is trained using the motion data from a 6-DOF industrial robot without collision, and the proposed method was evaluated online using additional trajectories. Experiments show that the proposed method reduces the detection delay by about 30 %∼40 % compared to the previous method. This proposed method reduces the requirement for robot dynamics model accuracy while ensuring satisfactory collision detection performance.
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References
F. Flacco, T. Kroeger, A. De Luca and O. Khatib, A depth space approach for evaluating distance to objects: with application to human-robot collision avoidance, Journal of Intelligent and Robotic Systems, 80 (1) (2015) 7–22.
S. Haddadin, A. Albu-Schaffer, A. De Luca and G. Hirzinger, Collision detection and reaction: a contribution to safe physical human-robot interaction, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008) 3356–3363.
C.-N. Cho, J.-H. Kim, S.-D. Lee and J.-B. Song, Collision detection and reaction on 7 DOF service robot arm using residual observer, Journal of Mechanical Science and Technology, 26 (4) (2012) 1197–1203.
B. Jung, J. C. Koo, H. R. Choi and H. Moon, Human-robot collision detection under modeling uncertainty using frequency boundary of manipulator dynamics, Journal of Mechanical Science and Technology, 28 (11) (2014) 4389–4395.
S. Lu, J. H. Chung and S. A. Velinsky, Human-robot interaction detection: a wrist and base force/torque sensors approach, Robotica, 24 (4) (2006) 419–427.
M. Indri, S. Trapani and I. Lazzero, Development of a virtual collision sensor for industrial robots, Sensors, 17 (5) (2017) 1–23.
F. Dimeas, L. D. Avendaño-Valencia and N. Aspragathos, Human - robot collision detection and identification based on fuzzy and time series modelling, Robotica, 33 (9) (2015) 1886–1898.
A.-N. Sharkawy, P. N. Koustoumpardis and N. Aspragathos, Human-robot collisions detection for safe human-robot interaction using one multi-input-output neural network, Soft Computing, 24 (9) (2020) 6687–6719.
A.-N. Sharkawy and A. A. Mostfa, Neural networks’ design and training for safe human-robot cooperation, Journal of King Saud University - Engineering Sciences, 34 (8) (2022) 582–596.
Y. J. Heo, D. Kim, W. Lee, H. Kim, J. Park and W. K. Chung, Collision detection for industrial collaborative robots: a deep learning approach, IEEE Robotics and Automation Letters, 4 (2) (2019) 740–746.
T. Zhang, P. Ge, Y. Zou and Y. He, Robot collision detection without external sensors based on time-series analysis, Journal of Dynamic Systems Measurement and Control-Transactions of the AMSE, 143 (4) (2021) 1–12.
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Computation, 9 (8) (1997) 1735–1780.
V. Mata, F. Benimeli, N. Farhat and A. Valera, Dynamic parameter identification in industrial robots considering physical feasibility, Advanced Robotics, 19 (1) (2005) 101–119.
C. D. Sousa and R. Cortesão, Physical feasibility of robot base inertial parameter identification: a linear matrix inequality approach, The International Journal of Robotics Research, 33 (6) (2014) 931–944.
W. Khalil, M. Gautier and P. Lemoine, Identification of the payload inertial parameters of industrial manipulators, Proceedings 2007 IEEE International Conference on Robotics and Automation, Rome, Italy (2007) 4943–4948.
M. Iwatani and R. Kikuuwe, An elastoplastic friction force estimator and its application to external force estimation and force-sensorless admittance control, IEEE/SICE International Symposium on System Integration, Sapporo, Japan (2016) 45–50.
M. Iwatani and R. Kikuuwe, An external force estimator using elastoplastic friction model with improved static friction behavior, 14th International Conference on Control, Automation, Robotics and Vision, Phukhet, Thailand (2016) 1–6.
T. Zhang and J. Hong, Collision detection method for Industrial robot based on envelope-like lines, Industrial Robot: The International Journal of Robotics Research and Application, 46 (4) (2019) 510–517.
A. Kouris, F. Dimeas and N. Aspragathos, A frequency domain approach for contact type distinction in human-robot collaboration, IEEE Robotics and Automation Letters, 3 (2) (2018) 720–727.
S. Haddadin, A. De Luca and A. Albu-Schäffer, Robot collisions: a survey on detection, isolation, and identification, IEEE Transactions on Robotics, 33 (6) (2017) 1292–1312.
S. Chen, M. Luo and F. He, A universal algorithm for sensorless collision detection of robot actuator faults, Advances in Mechanical Engineering, 10 (1) (2018) 1–10.
M. Indri, S. Trapani and I. Lazzero, A general procedure for collision detection between an industrial robot and the environment, IEEE 20th Conference on Emerging Technologies amd Factory Automation, Luxembourg (2015) 1–8.
J. Swevers, C. Ganseman, J. De Schutter and H. Van Brussel, Experiment robot identification using optimized periodic trajectories, Mechanical Systems and Signal Processing, 10 (5) (1996) 561–577.
N. Liu, L. Li, B. Hao, L. Yang, T. Hu, T. Xue and S. Wang, Modeling and simulation of robot inverse dynamics using LSTM-based deep learning algorithm for smart cities and factories, IEEE Access, 7 (2019) 173989–173998.
Q. Fu and H. Wang, Intelligent prediction for remaining useful life of complex system based on multi-layer LSTM, Journal of Ordnance Equipment Engineering, 43 (1) (2022) 161–169.
K. M. Park, Y. Park, S. Yoon and F. C. Park, Collision detection for robot manipulators using unsupervised anomaly detection algorithms, IEEE-ASME Transactions on Mechatronics, 27 (5) (2021) 2841–2851.
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This work is supported by the Science and Technology Planning Project of Guangdong Province, China [grant numbers 2019B040402006, 2020A0103010].
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Tie Zhang is a Professor and a Ph.D. candidate supervisor of the School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China. He received his Ph.D. degree in Mechanical Manufacturing and Automation from South China University of Technology in 2001. His main research interests include optimal design and control of robots, automation and intelligent systems.
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Zhang, T., Chen, Y., Ge, P. et al. LSTM-based external torque prediction for 6-DOF robot collision detection. J Mech Sci Technol 37, 4847–4855 (2023). https://doi.org/10.1007/s12206-023-0837-3
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DOI: https://doi.org/10.1007/s12206-023-0837-3