Path planning and positioning technology of intelligent inspection robot in cable tunnel

Shuangde Huang, Zigeng Zhou, Baoyu Xu, Shengwei Wang, Tao Wang

Abstract


Intelligent inspection robot is efficient for cable tunnel inspection. In this study, the path planning and positioning technology of inspection robot was studied. A new path planning technology based on Floyd algorithm and simulated annealing was proposed. The local shortest path was searched using Floyd algorithm, and then global shortest path was searched using simulated annealing algorithm. Radio frequency identification devices (RFID) combined with magnetic track guidance was used as positioning technology. Test of the positioning, straight driving and curve driving of intelligent inspection robot showed that the average error of the positioning technology, straight driving and curve driving was 7.1 cm, 4.2 cm and 5.6 cm respectively. It is concluded that the path planning and positioning technology proposed in this study has smaller errors, and will not affect the completion of inspection tasks. It provides a theoretical support for the application of intelligent inspection robot in cable tunnel.

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References


R. Montero, J. C. Victores, S. Martínez, et al., “Past, present and future of robotic tunnel inspection,” Automation in Construction, vol. 59, pp. S0926580515000229, 2015.

Z. Yang, P. Zou, G. Wu, et al., “Fabrication and Characterization of Highly Sensitive Methane Sensor Based on Titanium Dioxide,” Journal of Nanoelectronics & Optoelectronics, vol. 12, no. 8, pp. 815-819, 2017.

H. Sobreira, M. Pinto, A. P. Moreira, et al., “Robust Robot Localization Based on the Perfect Match Algorithm,” Lecture Notes in Electrical Engineering, vol. 321, pp. 607-616, 2015.

M. A. Hossain and I. Ferdous, “Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique,” Robotics and Autonomous Systems, vol. 64, pp. 137-141, 2015.

A. Hornung, S. Oßwald, D. Maier, et al., “Monte Carlo Localization for Humanoid Robot Navigation in Complex Indoor Environments,” International Journal of Humanoid Robotics, vol. 11, no. 02, pp. 1441002, 2014.

L. Ran, Y. Zhang, Q. Zhang, et al., “Convolutional Neural Network-Based Robot Navigation Using Uncalibrated Spherical Images,” Sensors, vol. 17, no. 6, pp. 1341, 2017.

D. Wu, D. Chatzigeorgiou, K. Youcef-Toumi, et al., “Node Localization in Robotic Sensor Networks for Pipeline Inspection,” IEEE Transactions on Industrial Informatics, vol. 12, no. 2, pp. 809-819, 2016.

L. S. Wei, Y. Guo, and X. F. Dai, “Path Planning Based on Warehousing Intelligent Inspection Robot in Internet of Things,” Advanced Materials Research, vol. 267, pp. 318-321, 2011.

H. Mo and L. Xu, “Research of biogeography particle swarm optimization for robot path planning,” Neurocomputing, vol. 148, pp. 91-99, 2015.

I. Chaari, A. Koubaa, H. Bennaceur, et al., “On the Adequacy of Tabu Search for Global Robot Path Planning Problem in Grid Environments,” Procedia Computer Science, vol. 32, pp. 604-613, 2014.

Z. Lin and Z. Jianli, “Frequent Item Sets and Association Rules Mining Algorithm Based on Floyd Algorithm,” Journal of Computational and Theoretical Nanoscience, vol. 12, no. 9, pp. 2574-2578, 2015.

X. S. He, W. J. Ding, and X. S. Yang, “Bat algorithm based on simulated annealing and Gaussian perturbations,” Neural Computing and Applications, vol. 25, no. 2, pp. 459-468, 2014.

X. Qin, G. Wu G, L. Jin, et al., “A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data,” Sensors, vol. 18, no. 2, pp. 596, 2018.

L. Attard, C. J. Debono, G. Valentino, et al., “Vision-based change detection for inspection of tunnel liners,” Automation in Construction, vol. 91, pp. 142-154, 2018.

T. M. Thi, C. Copot, D. T. Tran, et al., “Heuristic approaches in robot path planning: A survey,” Robotics and Autonomous Systems, pp. S0921889015300671, 2016.


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