Path Planning for Robotic Training in Virtual Environments using Deep Learning

Javier Pinzón-Arenas, Robinson Jimenez-Moreno, Astrid Rubiano


Reducing costs in the acquisition of industrial robots and their benefit in continuous production workdays have increased the number of investigations that expand the robot's action capabilities. This work proposes a trajectory planning system for a UR3 robot that is very usual in academic, research, and industrial applications. The system is presented based on a convolutional network training for regression tasks focused on learning the desired trajectory. A virtual environment has been developed to simulate different trajectories based on the interaction between UR3 robot and object detection and location through the convolutional network employed. This work exposes the network's training and the results of the transport of the object, where the robot can position itself on the desired tool (scissors and screwdriver), which is recognized by training a Faster network R-CNN and the re-localization of the tool in a conveyor band. For the trained trajectory’s, a ResNet-50 model is proposed, and the overall performance achieved was 92.63%, with a mean square error of 24.7 mm in the trained trajectory's repetition. Also, the boxplot of each ax in the trajectory is exposed since they show in a more detailed way the deviation of each of the points in the whole validation set. The average collection time, from when the system takes the workspace capture to its initial positioning after leaving the tool on the belt, was 51.3 seconds, enough for real-time applications.


CNN regression; faster R-CNN; path planning; virtual environment.

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F. Xiaoqing, B. Qun, X. Hongjun, f. Xiaolan, “Diffusion of industrial robotics and inclusive growth: Labour market evidence from cross country dataâ€, Journal of Business Research, 2020, ISSN 0148-2963, DOI :10.1016/j.jbusres.2020.05.051.

M. Cho, M. Jang and Y. Cho, "Evaluation of Social Robot Intelligence in Terms of Social Interactive Motion," 17th International Conference on Ubiquitous Robots (UR), Kyoto, Japan, pp. 608-611, 2020, DOI: 10.1109/UR49135.2020.9144920.

P. Galambos, "Cloud, Fog, and Mist Computing: Advanced Robot Applications," in IEEE Systems, Man, and Cybernetics Magazine, vol. 6, no. 1, pp. 41-45, Jan. 2020, DOI: 10.1109/MSMC.2018.2881233.

F. Sherwani, M. M. Asad and B. S. K. K. Ibrahim, "Collaborative Robots and Industrial Revolution 4.0 (IR 4.0)," International Conference on Emerging Trends in Smart Technologies (ICETST), Karachi, Pakistan, pp. 1-5, 2020, DOI: 10.1109/ICETST49965.2020.9080724.

T. Cvitanic, V. Nguyen, S. N. Melkote, “Pose optimization in robotic machining using static and dynamic stiffness modelsâ€, Robotics and Computer-Integrated Manufacturing, Volume 66, 2020, 101992, ISSN 0736-5845, DOI: 10.1016/j.rcim.2020.101992.

S. Campbell, N. O'Mahony, A. Carvalho, L. Krpalkova, D. Riordan and J. Walsh, "Path Planning Techniques for Mobile Robots A Review," 6th International Conference on Mechatronics and Robotics Engineering (ICMRE), Barcelona, Spain, 2020, pp. 12-16, DOI: 10.1109/ICMRE49073.2020.9065187.

Y. Hou, Y. Liu and F. Wang, "Research on Intelligent Path Planning Based on Transit Point," Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Dalian, China, 2020, pp. 419-422, DOI: 10.1109/IPEC49694.2020.9115175.

Y. Zhang, C. Wang, L. Hu and G. Qiu, "Inverse kinematics problem of industrial robot based on PSO-RBFNN," 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China, 2020, pp. 346-350, DOI: 10.1109/ITNEC48623.2020.9085179.

S. Ivanov, L. Ivanova and Z. Meleshkova, "Calculation and Optimization of Industrial Robots Motion," 26th Conference of Open Innovations Association (FRUCT), Yaroslavl, Russia, 2020, pp. 115-123, DOI: 10.23919/FRUCT48808.2020.9087376

B. Yang, W. Li, J. Wang, J. Yang, T. Wang and X. Liu, "A Novel Path Planning Algorithm for Warehouse Robots Based on a Two-Dimensional Grid Model," in IEEE Access, vol. 8, pp. 80347-80357, 2020, DOI: 10.1109/ACCESS.2020.2991076.

J. Herrera, D. Espitia, R. Jimenez-Moreno, R. Hernández, "Flood Fill Algorithm Dividing Matrices for Robotic Path Planning". International Journal of Applied Engineering Research ISSN: 0973-4562,v.13 fasc.11 p.8862 - 8870 ,2018.

J. Herrera, C. Pachon-Suescun, R. Jimenez-Moreno, "Scara Robot Path Planning Through flood fill Algorithm". International Journal of Applied Engineering Research ISSN: 0973-4562, v.13 fasc.19 p.14273 - 14281 ,2018.

H. Xizhi, J. Zhihui and X. Congcong, "Vehicle Path Planning Fusion Algorithm Based on Road Network," 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China, 2020, pp. 98-102, DOI: 10.1109/ITNEC48623.2020.9084895.

S. D. Han and J. Yu, "DDM: Fast Near-Optimal Multi-Robot Path Planning Using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics," in IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1350-1357, April 2020, DOI: 10.1109/LRA.2020.2967326.

R. Kumar, L. Singh and R. Tiwari, "Comparison of Two Meta –Heuristic Algorithms for Path Planning in Robotics," 2020 International Conference on Contemporary Computing and Applications (IC3A), Lucknow, India, 2020, pp. 159-162, DOI: 10.1109/IC3A48958.2020.233289.

R. Jiménez-Moreno and L. Brito, "Planeación de trayectorias para un móvil robótico en un ambiente 3D," 2014 IEEE Biennial Congress of Argentina (ARGENCON), Bariloche, 2014, pp. 125-129, DOI: 10.1109/ARGENCON.2014.6868483.

I. G. M. I. Moteir, K. Ismail, F. M. Zawawi and M. M. M. Azhar, "Urban Intelligent Navigator for Drone Using Convolutional Neural Network (CNN)," 2019 International Conference on Smart Applications, Communications and Networking (SmartNets), Sharm El Sheik, Egypt, 2019, pp. 1-4, DOI: 10.1109/SmartNets48225.2019.9069781.

J. Wang, W. Chi, C. Li, C. Wang and M. Q. -H. Meng, "Neural RRT*: Learning-Based Optimal Path Planning," in IEEE Transactions on Automation Science and Engineering, vol. 17, no. 4, pp. 1748-1758, Oct. 2020, DOI: 10.1109/TASE.2020.2976560.

I. Rafegas, M. Vanrell, L. A. Alexandre, G. Arias, “Understanding trained CNNs by indexing neuron selectivityâ€, Pattern Recognition Letters, 2019, ISSN 0167-8655. DOI: 10.1016/j.patrec.2019.10.013.

K. Elawaad, M. Ezzeldin and M. Torki, "DeepCReg: Improving Cellular-based Outdoor Localization using CNN-based Regressors," 2020 IEEE Wireless. Communications and Networking Conference (WCNC), Seoul, Korea (South), 2020, pp. 1-6, DOI: 10.1109/WCNC45663.2020.9120714.

R. Jiménez-Moreno, J. Pinzón-Arenas, C. Pachón-Suescún, “Assistant robot through deep learningâ€, International Journal of Electrical and Computer Engineering (IJECE), Vol 10, No 1: February 2020, p. 1053-1062.

Z. Zhong, L. Sun, Q. Huo, “Improved localization accuracy by LocNet for Faster R-CNN based text detection in natural scene imagesâ€, Pattern Recognition, Volume 96, 2019, 106986, ISSN 0031-3203, DOI: 10.1016/j.patcog.2019.106986.

J. Li, D. Zhang, J. Zhang, J. Zhang, T. Li, Y. Xia, Q. Yan, L. Xun, “Facial Expression Recognition with Faster R-CNNâ€, Procedia Computer Science, Volume 107, 2017, Pages 135-140, ISSN 1877-0509, DOI: 10.1016/j.procs.2017.03.069.

S. Wan, S. Goudos, “Faster R-CNN for multi-class fruit detection using a robotic vision systemâ€, Computer Networks, Volume 168, 2020, 107036, ISSN 1389-1286, DOI: 10.1016/j.comnet.2019.107036.

W. Xie, X. Fang and S. Wu, "2.5D Navigation Graph and Improved A-Star Algorithm for Path Planning in Ship inside Virtual Environment," 2020 Prognostics and Health Management Conference (PHM-Besançon), Besancon, France, 2020, pp. 295-299, DOI: 10.1109/PHM-Besancon49106.2020.00057.

J. Qi, H. Yang and H. Sun, "MOD-RRT*: A Sampling-based algorithm for robot path planning in dynamic environment," in IEEE Transactions on Industrial Electronics, ISSN: 1557-9948 June 2020. DOI: 10.1109/TIE.2020.2998740.

G. Bolano, A. Roennau, R. Dillmann and A. Groz, "Virtual Reality for Offline Programming of Robotic Applications with Online Teaching Methods," 2020 17th International Conference on Ubiquitous Robots (UR), Kyoto, Japan, 2020, pp. 625-630, DOI: 10.1109/UR49135.2020.9144806.

R. Shaoqing, et al. “Faster R-CNN: Towards real-time object detection with region proposal networksâ€. Advances in neural information processing systems. p. 91-99. 2015.

H. Kaiming, et al. “Deep residual learning for image recognitionâ€. Proceedings of the IEEE conference on computer vision and pattern recognition. p. 770-778. 2016.

J. Pinzón-Arenas, R. Jiménez-Moreno and A. Rubiano. “Comparative approach of CNN regression architectures for robotic manipulator 2D trajectory estimationâ€. In Multimedia Conference 2019.



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