Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-10T16:19:26.792Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot

Published online by Cambridge University Press:  14 May 2019

Raouf Fareh ,
Mohammed Baziyad ,
Mohammad H. Rahman ,
Tamer Rabie  and
Maamar Bettayeb
Raouf Fareh*
Affiliation:
Electrical and Computer Engineering Department, University of Sharjah, UAE E-mails: trabie@sharjah.ac.ae, maamar@sharjah.ac.ae
Mohammed Baziyad
Affiliation:
Research Institute of Sciences and Engineering (RISE), University of Sharjah, UAE E-mail: mbaziyad@sharjah.ac.ae
Mohammad H. Rahman
Affiliation:
Mechanical/Biomedical Engineering Department, University of Wisconsin-Milwaukee, Milwaukee, USA E-mail: rahmanmh@uwm.edu
Tamer Rabie
Affiliation:
Research Institute of Sciences and Engineering (RISE), University of Sharjah, UAE E-mail: mbaziyad@sharjah.ac.ae
Maamar Bettayeb
Affiliation:
Research Institute of Sciences and Engineering (RISE), University of Sharjah, UAE E-mail: mbaziyad@sharjah.ac.ae MCEIES, King Abdulaziz University, Jeddah, KSA
*
*Corresponding author. E-mail: rfareh@sharjah.ac.ae
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.

Keywords

Path planningImage processingDepth sensorMobile robotKinematic controlProbabilistic roadmapObstacle avoidanceStabilityTrajectory tracking
Type
Articles
Information
Robotica , Volume 38 , Issue 2 , February 2020 , pp. 235 - 255
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Held, D., Levinson, J., Thrun, S. and Savarese, S., “Robust real-time tracking combining 3D shape, color, and motion,” Int. J. Rob. Res. 35(1–3), 30–49 (2016).CrossRefGoogle Scholar
Haddadin, S., Weitschat, R., Huber, F., Özparpucu, M. C., Mansfeld, N. and Albu-Schäffer, A., “Optimal control for viscoelastic robots and its generalization in real-time,” In: Robotics Research (Springer, 2016) pp. 131–148.CrossRefGoogle Scholar
Kim, H., Leutenegger, S. and Davison, A. J., “Real-time 3D reconstruction and 6-DoF tracking with an event camera,” In: European Conference on Computer Vision (ECCV), no. 6 (Springer, Cham, 2016) pp. 349–364.Google Scholar
Gómez, V. Vaquero, Polo, E. Repiso, Cortés, A. Sanfeliu, Vissers, J. and Kwakkernaat, M., “Low Cost, Robust and Real Time System for Detecting and Tracking Moving Objects to Automate Cargo Handling in Port Terminals,” Robot 2015: Second Iberian Robotics Conference, Lisbon, Portugal (Springer, 2015) pp. 491–502.Google Scholar
Patle, B., Parhi, D., Jagadeesh, A. and Sahu, O., “Real time navigation approach for mobile robot,” JCP 12(2), 135–142 (2017).CrossRefGoogle Scholar
Wang, K., Liu, Y. and Li, L., “Vision-based Tracking Control of Nonholonomic Mobile Robots without Position Measurement,” 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany (IEEE, 2013) pp. 5265–5270.CrossRefGoogle Scholar
Elbanhawi, M. and Simic, M., “Sampling-based robot motion planning: A review,” IEEE Access 2, 56–77 (2014).CrossRefGoogle Scholar
Nielsen, C. L. and Kavraki, L. E., “A Two Level Fuzzy PRM for Manipulation Planning,” In: Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2000), vol. 3, (IEEE, 2000).Google Scholar
Denny, J., Shi, K. and Amato, N. M., “Lazy Toggle PRM: A Single-query Approach to Motion Planning,” 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany (IEEE, 2013).Google Scholar
Patle, B. K., Parhi, D. R., Jagadeesh, A. and Kashyap, S. K., “Matrix-binary codes based genetic algorithm for path planning of mobile robot,” Comput. Electr. Eng. 67, 708–728 (2018).CrossRefGoogle Scholar
Wallack, A. S., Lifeng, L. and Ye, X., “System and method for robust calibration between a machine vision system and a robot,” ed: Google Patents (2016).Google Scholar
Chen, X. C., Cao, Z. Q., Yang, Y. Q. and Zhou, C., “Vision-based fuzzy controller for quadrotor tracking a ground target,” In: Advances in Science and Technology, vol. 101, (Trans Tech Publications, 2017) pp. 121–126.CrossRefGoogle Scholar
Li, Z., Yang, C., Su, C.-Y., Deng, J. and Zhang, W., “Vision-based model predictive control for steering of a nonholonomic mobile robot,” IEEE Trans. Control Syst. Technol. 24(2), 553–564 (2016).Google Scholar
Leitner, J., Harding, S., Frank, M., Förster, A. and Schmidhuber, J., “An integrated, modular framework for computer vision and cognitive robotics research (icvision),” In: BICA (Springer, Berlin, Heidelberg, 2012) pp. 205–210.Google Scholar
Choset, H., “Coverage for robotics-A survey of recent results,” Ann. Math. Artif. Intell. 31(1), 113–126 (2001).CrossRefGoogle Scholar
Singh, Y., Sharma, S., Hatton, D. and Sutton, R., “Optimal path planning of unmanned surface vehicles,” Indian J. Geo. Mar. Sci. 47(7), 1325–1334 (2018).Google Scholar
Duchoň, F., Babinec, A., Kajan, M., Beňo, P., Florek, M., Fico, T. and Jurišica, L., “Path planning with modified a star algorithm for a mobile robot,” Procedia Eng. 96, 59–69 (2014).CrossRefGoogle Scholar
Le, A., Prabakaran, V., Sivanantham, V. and Mohan, R., “Modified a-star algorithm for efficient coverage path planning in Tetris inspired self-reconfigurable robot with integrated laser sensor,” Sensors 18(8), 2585 (2018).CrossRefGoogle ScholarPubMed
Liang, X., Meng, G., Xu, Y. and Luo, H., “A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment,” Intell. Serv. Rob. 11(3), 301–312 (2018).CrossRefGoogle Scholar
Gammell, J. D., Srinivasa, S. S. and Barfoot, T. D., “Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,” arXiv preprint https://arxiv.org/abs/1404.2334arXiv:1404.2334 (2014).CrossRefGoogle Scholar
Aguilar, W. G. and Morales, S. G., “3D environment mapping using the Kinect V2 and path planning based on RRT algorithms,” Electronics 5(4), 70 (2016).CrossRefGoogle Scholar
Fareh, R., Rabie, T. and Baziyad, M., “Tracking Control for Wheeled Mobile Robot Using RGBD Sensor,” 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT), St. Paul’s Bay, Malta (IEEE, 2017).Google Scholar
Kanayama, Y., Kimura, Y., Miyazaki, F. and Noguchi, T., “A Stable Tracking Control Method for an Autonomous Mobile Robot,” 1990 IEEE International Conference on Robotics and Automation, Cincinnati, OH, USA (IEEE, 1990) pp. 384–389.CrossRefGoogle Scholar
Slotine, J.-J. E. and Li, W., Applied Nonlinear Control, vol. 1 (Prentice Hall, Englewood Cliffs, NJ, 1991).Google Scholar
Hornik, K., Stinchcombe, M. and White, H., “Multilayer feedforward networks are universal approximators,” Neural Networks 2(5), 359–366 (1989).CrossRefGoogle Scholar
Lee, W. J., Na, J., Kim, K., Lee, C.-J., Lee, Y. and Lee, J. M., “NARX modeling for real-time optimization of air and gas compression systems in chemical processes,” Comput. Chem. Eng. 115, 262–274 (2018).CrossRefGoogle Scholar