Abstract
The emerging research field developed to optimize the collaboration of human-robot systems for Industry 4.0 gives a central role to the tracking of human motion. Inertial Measurement Units (IMUs) represent a suitable solution to unobtrusively monitor workers in the industrial environment. However, the computation of IMUs orientation usually causes drift problems and affects the kinematics estimate. Moreover, the traditional Euler angles decomposition from the mutual independent orientation of IMUs is affected by mathematical singularities and it does not include joint constraints to avoid violation of physiological motion range. To overcome these limitations, this work aimed at developing a Denavit-Hartenberg upper limb model consistent with standard biomechanical guidelines and an optimization framework for the real-time tracking of human motion. At each time step, the joint variables of the model were estimated minimizing the difference between the modeled segments orientations and those obtained with the sensor fusion. The proposed method was validated with synthetic and real robot data, verifying the influence of a considerable drift on the estimate accuracy. Finally, a comparison between the optimized joint kinematics and the one obtained with traditional methods was made.
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References
Bauer, W., Bender, M., Braun, M., Rally, P., Scholtz, O.: Lightweight robots in manual assembly–best to start simply. In: Frauenhofer-Institut für Arbeitswirtschaft und Organisation IAO, Stuttgart (2016)
Losey, D.P., McDonald, C.G., Battaglia, E., O’Malley, M.K.: A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction. Appl. Mech. Rev. 70, 1–19 (2018)
Mainprice, J., Berenson, D.: Human-robot collaborative manipulation planning using early prediction of human motion. In: IEEE International Conference on Intelligent Robots and Systems. pp. 299–306. IEEE (2013)
Melchiorre, M., Scimmi, L.S., Mauro, S., Pastorelli, S.P.: Vision-based control architecture for human–robot hand-over applications. Asian J. Control. 23, 105–117 (2020)
Weitschat, R., Ehrensperger, J., Maier, M., Aschemann, H.: Safe and efficient human-robot collaboration part I: Estimation of human arm motions. In: Proceedings - IEEE International Conference on Robotics and Automation. pp. 1993–1999. IEEE (2018)
Goodvin, C., Park, E.J., Huang, K., Sakaki, K.: Development of a real-time three-dimensional spinal motion measurement system for clinical practice. Med. Biol. Eng. Comput. 44, 1061–1075 (2006)
Safeea, M., Neto, P.: Minimum distance calculation using laser scanner and IMUs for safe human-robot interaction. Robot. Comput. Integr. Manuf. 58, 33–42 (2019)
Cereatti, A., Trojaniello, D., Croce, U. Della: Accurately measuring human movement using magneto-inertial sensors: techniques and challenges; Accurately measuring human movement using magneto-inertial sensors: techniques and challenges. (2015)
Digo, E., Pierro, G., Pastorelli, S., Gastaldi, L.: Evaluation of spinal posture during gait with inertial measurement units. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 234, 1094–1105 (2020)
Digo, E., Antonelli, M., Cornagliotto, V., Pastorelli, S., Gastaldi, L.: Collection and analysis of human upper limbs motion features for collaborative robotic applications. Robotics 9, 33 (2020)
Digo, E., Gastaldi, L., Antonelli, M., Pastorelli, S.P., Cereatti, A., Caruso, M.: Real-time estimation of upper limbs kinematics with IMUs during typical industrial gestures. In: International Conference on Industry 4.0 and Smart Manufacturing (2021)
Škulj, G., Vrabič, R., Podržaj, P.: A wearable imu system for flexible teleoperation of a collaborative industrial robot. Sensors. 21, (2021)
Caruso, M., Sabatini, A.M., Knaflitz, M., Gazzoni, M., Croce, U. Della, Cereatti, A.: Orientation estimation through magneto-inertial sensor fusion: a heuristic approach for suboptimal parameters tuning. IEEE Sens. J. 21, 3408–3419 (2021)
Wu, G., et al.: ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion - Part II: Shoulder, elbow, wrist and hand. J. Biomech. 38, 981–992 (2005)
Cornagliotto, V., Digo, E., Pastorelli, S.: Using a robot calibration approach toward fitting a human arm model. In: Zeghloul, S., Laribi, M.A., Sandoval, J. (eds.) RAAD 2021. MMS, vol. 102, pp. 199–207. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75259-0_22
Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: Modelling. planning and control. Springer, London (2010)
El-Sheimy, N., Hou, H., Niu, X.: Analysis and modeling of inertial sensors using allan variance. IEEE Trans. Instrum. Meas. 57, 140–149 (2008)
Madgwick, S.O.H., Harrison, A.J.L., Vaidyanathan, R.: Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE Int. Conf. Rehabil. Robot. (2011)
Caruso, M., Sabatini, A.M., Laidig, D., Seel, T., Knaflitz, M., Croce, U. Della, Cereatti, A.: Analysis of the accuracy of ten algorithms for orientation estimation using inertial and magnetic sensing under optimal conditions: one size does not fit all. Sensors. 21, 2543 (2021)
Caruso, M., Sabatini, A.M., Knaflitz, M., Croce, U. Della, Cereatti, A.: Extension of the rigid-constraint method for the heuristic suboptimal parameter tuning to ten sensor fusion algorithms using inertial and magnetic sensing. Sensors 21, 6307 (2021)
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Digo, E., Cereatti, A., Gastaldi, L., Pastorelli, S., Caruso, M. (2022). Modeling and Kinematic Optimization of the Human Upper Limb for Collaborative Robotics. In: Niola, V., Gasparetto, A., Quaglia, G., Carbone, G. (eds) Advances in Italian Mechanism Science. IFToMM Italy 2022. Mechanisms and Machine Science, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-031-10776-4_66
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