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Dynamic Optimization of a Steerable Screw In-pipe Inspection Robot Using HJB and Turbine Installation

Published online by Cambridge University Press:  16 January 2020

H. TourajizadehOpen the ORCID record for H. Tourajizadeh[Opens in a new window],
V. BoomeriOpen the ORCID record for V. Boomeri[Opens in a new window],
M. Rezaei and
A. Sedigh
H. Tourajizadeh*
Affiliation:
Mechanical Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran
V. Boomeri
Affiliation:
Mechanical Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran
M. Rezaei
Affiliation:
Mechanical Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran
A. Sedigh
Affiliation:
Mechanical Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran
*
*Corresponding author. E-mail: Tourajizadeh@khu.ac.ir
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Summary

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In this paper, two strategies are proposed to optimize the energy consumption of a new screw in-pipe inspection robot which is steerable. In the first method, optimization is performed using the optimal path planning and implementing the Hamilton–Jacobi–Bellman (HJB) method. Since the number of actuators is more than the number of degrees of freedom of the system for the proposed steerable case, it is possible to minimize the energy consumption by the aid of the dynamics of the system. In the second method, the mechanics of the robot is modified by installing some turbine blades through which the drag force of the pipeline fluid can be employed to decrease the required propulsion force of the robot. It is shown that using both of the mentioned improvements, that is, using HJB formulation for the steerable robot and installing the turbine blades can significantly save power and energy. However, it will be shown that for the latter case this improvement is extremely dependent on the alignment of the fluid stream direction with respect to the direction of the robot velocity, while this optimization is independent of this case for the former strategy. On the other hand, the path planning dictates a special pattern of speed functionality while for the robot equipped by blades, saving the energy is possible for any desired input path. The correctness of the modeling is verified by comparing the results of MATLAB and ADAMS, while the efficiency of the proposed optimization algorithms is checked by the aid of some analytic and comparative simulations.

Keywords

In-pipe inspection robotOptimal controlEnergy harvestingOptimal modeling

Information

Type
Articles
Information
Robotica , Volume 38 , Issue 11 , November 2020, pp. 2001 - 2022
Copyright
Copyright © Cambridge University Press 2020

References

Peng, Z., Yang, S., Wen, G., Rahmani, A. and Yu, Y., “Adaptive distributed formation control for multiple non-holonomic,” Neurocomputing, 173, 1485–1494 (2015).CrossRefGoogle Scholar
Scaglia, G., Serrano, E., Rosales, A. and Albertos, P., “Linear interpolation based controller design for trajectory tracking under uncertainties: application to mobile robots,” Control Eng. Pract. 45, 123–132, (2015).CrossRefGoogle Scholar
Simba, K. R., Uchiyama, N. and Sano, S., “Real-time smooth trajectory generation for nonholonomic mobile robots using Bézier curves,” Robot. Comput.-Integ. Manuf. 41, 31–42 (2016).CrossRefGoogle Scholar
Filaretov, V. F. and Pryanichnikov, V. E., “Autonomous mobile university robots AMUR: Technology and applications to extreme robotics,” Procedia Eng. 100, 269–277, (2015).CrossRefGoogle Scholar
Liu, T. and Jiang, Z. P., “Distributed formation control of nonholonomic mobile robots without global position measurements,” Automatica, 49(2), 592–600 (2013).Google Scholar
Takahashi, M., Hayashi, I., Iwatsuki, N., Suzumori, K. and Ohki, N., “The development of an in-pipe micro robot applying the motion of an earthworm,” Seimitsu Kogaku Kaishi, 61, 90–94 (1995).Google Scholar
Suzumori, K., Wakimoto, S. and Takata, M., “A Miniature Inspection Robot Negotiating Pipes of Widely Varying Diameter,” 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), Taipei, Taiwan (IEEE, 2013) vol. 2, pp. 2735–2740.Google Scholar
Zagler, A. and Pfeiffer, F., “(MORITZ) a pipe crawler for tube junctions,” Robot. Automat., 3, 2954–2959 (2003).Google Scholar
Kim, J. H., Sharma, G. and Iyengar, S. S., “FAMPER: A Fully Autonomous Mobile Robot for Pipeline Exploration,” 2010 IEEE International Conference on Industrial Technology, Vina del Mar (IEEE, 2010) pp. 517–523. IEEE.CrossRefGoogle Scholar
Harish, P. and Venkateswarlu, V., “Design and motion planning of indoor pipeline inspection robot,” Int. J. Innovat. Technol. Explor. Eng. (IJITEE), 3(17), 7 (2013).Google Scholar
Suzumori, K., Miyagawa, T., Kimura, M. and Hasegawa, Y., “Micro inspection robot for 1-in pipes,” Mechatronics, 4(3), 286–292 (1999).Google Scholar
Nagaya, K., Yoshino, T., Katayama, M. and Murakami, I., “Wireless piping inspection vehicle using magnetic,” IEEE/ASME Trans. Mechatron. 17(3), 472–479 (2012).Google Scholar
Ciszewski, M., Buratowski, T., Giergiel, M. and Małka, P., “Virtual prototyping design and analysis of AN in-pipe inspection mobile robot,” Theor. Appl. Mech. 52(2), 417–429 (2014).Google Scholar
Kwon, Y. and Yi, B., “Design and motion planning of a two-module collaborative indoor pipeline inspection robot,” IEEE Trans. Robot. 28(3), 681–696 (2012).CrossRefGoogle Scholar
Park, J., Hyun, D., Cho, W. H., Kim, T. H. and Yang, H. S., “Normal-force control for an in-pipe robot according to the inclination of pipelines,” IEEE Trans. Indust. Electron. 58(12), 5304–5310 (2011).CrossRefGoogle Scholar
Zhang, Y. and Yan, G., In-pipe inspection robot with active pipe-diameter adaptability and automatic tractive force adjusting,” Mech. Mach. Theory 42(12), 1618–1631 (2007).CrossRefGoogle Scholar
Roh, S. and Choi, H. R., “Differential-drive in-pipe robot for moving inside urban gas pipelines,” IEEE Trans. Robot. 21(1), 1–17 (2005).Google Scholar
Dertien, E., Foumashi, M. M., Pulles, K. and Stramigioli, S., “Design of a Robot for In-pipe Inspection Using Omnidirectional Wheels and Active Stabilization,” 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China (IEEE, 2014) pp. 5121–5126.CrossRefGoogle Scholar
Li, P., Ma, S., Li, B. and Wang, Y., “Multifunctional Mobile Units with a Same Platform for In-pipe Inspection Robots,” In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (IEEE, 2008) pp. 2643–2648.Google Scholar
Kakogawa, A. and Ma, S., “Mobility of an in-pipe robot with screw drive mechanism inside curved pipes,” Robotics and Biomimetics (ROBIO) (2010) pp. 1530–1535.Google Scholar
Zhang, Y., Zhang, M., Sun, H. and Jia, Q., “Design and Motion Analysis of a Flexible Squirm Pipe Robot,” 2010 International Conference on Intelligent System Design and Engineering Application, Changsha (IEEE, 2010) vol. 1, pp. 527–531.CrossRefGoogle Scholar
Li, T., Ma, S., Li, B., Wang, M. and Wang, Y., “Control strategies of energy optimization for an in-pipe robot with inclining-angle-adjustable screw rollers,” J. Mech. Eng. 50(17), 8–16 (2014).CrossRefGoogle Scholar
Korayem, M. H., Irani, M. and Rafee Nekoo, S., “Motion control and dynamic load carrying capacity of mobile robot via nonlinear optimal feedback,” Manuf. Mater. Sci. 2(1), 1 (2012).Google Scholar
Abbaspour, A., Alipour, K., Jafari, H.Z., Ali, S. and Moosavian, A., “Optimal formation and control of cooperative wheeled mobile robots,” Comptes Rendus Mécanique 343(5–6), 307–321 (2015).CrossRefGoogle Scholar
Boukens, M., Boukabou, A. and Chadlib, M., “Robust adaptive neural network-based trajectory tracking control approach for nonholonomic electrically driven mobile robots,” Robot. Auton. Syst. 92, 30–40 (2017).CrossRefGoogle Scholar
Krishnan, J., Rajeev, U. P., Jayabalan, J. and Sheela, D. S., “Optimal motion planning based on path length minimisation,” Robot. Auton. Syst. 94, 245–263 (2017).CrossRefGoogle Scholar
Han, J. and Seo, Y., “Mobile robot path planning with surrounding point set and path improvement,” Appl. Soft Comput. 57, 35–47 (2017).Google Scholar
Song, R. Z., Xiao, W. D. and Sun, C. Y., “Optimal tracking control for a class of unknown discrete-time systems with actuator saturation via data-based ADP algorithm,” Acta Automatica Sinica 39 (9) 1413–1420 (2013).CrossRefGoogle Scholar
Adhyaru, D. M., Kar, I. N. and Gopal, M., “Bounded robust control of nonlinear systems using neural network–based HJB solution,” Neural Comput. Appl. 20(1), 91–103 (2011).CrossRefGoogle Scholar
Sioma, A., “Biologically-inspired water propulsion system,” J. Bionic Eng. 10(3), 274–281 (2013).CrossRefGoogle Scholar
Allotta, B., Costanzi, R., Monni, N., Pugi, L., Ridolf, A. and Vettori, G., “Design and simulation of an autonomous underwater vehicle,” ECCOMAS, Vienna, Austria (2012).Google Scholar
Zakeri, E., Farahat, S., Moezi, S.A. and Zare, A., “Robust sliding mode control of a mini unmanned underwater vehicle equipped with a new arrangement of water jet propulsions: Simulation and experimental study,” Appl. Ocean Res. 59, 521–542 (2016).CrossRefGoogle Scholar
Baskharone, E. A., Principles of Turbomachinery in Air-Breathing Engines (Cambridge University Press, 2006).CrossRefGoogle Scholar
Tourajizadeh, H., Rezaei, M. and Sedigh, A. H., “Optimal control of screw in-pipe inspection robot with controllable pitch rate,” J. Intell. Robot. Syst. 90(3–4), 269–286 (2018).Google Scholar
Li, P., Ma, S., Lyu, C., Jiang, X. and Liu, Y., “Energy-efficient control of a screw-drive pipe robot with consideration of actuator’s characteristics,” Robotics Biomim. 3(1), 11 (2016).CrossRefGoogle ScholarPubMed