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Adaptive path finding algorithm in dynamic environment for warehouse robot

  • S.I. : Green and Human Information Technology 2019
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Abstract

Warehouse robots have been widely used by manufacturers and online retailer to automate good delivery process. One of the fundamental components when designing a warehouse robot is path finding algorithm. In the past, many path finding algorithms had been proposed to identify the optimal path and improve the efficiency in different conditions. For example, A* path finding algorithm is developed to obtain the shortest path, while D* obtains a complete coverage path from source to destination. Although these algorithms improved the efficiency in path finding, dynamic obstacle that may exist in warehouse environment was not considered. This paper presents AD* algorithm, a path finding algorithm that works in dynamic environment for warehouse robot. AD* algorithm is able to detect not only static obstacle but also dynamic obstacles while operating in warehouse environment. In dynamic obstacle path prediction, image of the warehouse environment is processed to identify and track obstacles in the path. The image is pre-processed using perspective transformation, dilation and erosion. Once obstacle has been identified using background subtraction, the server will track and predict future path of the dynamic object to avoid the obstacle.

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References

  1. Dubois J, Hamilton R (2017) The team who created Amazon’s warehouse robots returns with a new robot named Chuck. Retrieved from https://www.cnbc.com/2017/07/26/6-river-systems-former-kiva-execs-build-warehouse-robot.html

  2. Markets and Markets (2017) Warehouse robotics market by type mobile, articulated, cylindrical, SCARA, parallel & Cartesian. Retrieved from https://www.marketsandmarkets.com/Market-Reports/warehouse-robotic-market-128876258.html

  3. Ganeshmurthy M, Suresh G (2017) Path planning algorithm for autonomous mobile robot in dynamic environment. In: 2015 3rd International conference in signal processing, communication and networking (ICSCN), pp 1–6

  4. Ferguson D, Likhachev M, Stentz A (2005) A guide to heuristic-based path planning. In: Proceedings of the international workshop on planning under uncertainty for autonomous systems at international conference on automated planning and scheduling (ICAPS), pp 9–18

  5. Sariff N, Buniyamin N (2006) An overview of autonomous mobile robot path planning algorithms. In: 2006 4th student conference on research and development, pp 183–188

  6. Silva JB, Siebra CA, do Nascimento TP (2015) A new cost function heuristic applied to A* based path planning in static and dynamic environments. In: 2015 12th Latin American robotics symposium and 2015 3rd Brazilian symposium on robotics (LARS-SBR), pp 37–42

  7. Atyabi A (2013) Powers DM (2013) Review of classical and heuristic-based navigation and path planning approaches. Int J Adv Comput Technol 5(14):1

    Google Scholar 

  8. Russell SJ, Norvig P (2009) Artificial intelligence: a modern approach. Pearson Education Limited, New Delhi

    MATH  Google Scholar 

  9. Otte MW (2015) A survey of machine learning approaches to robotic path-planning. University of Colorado at Boulder, Boulder

    Google Scholar 

  10. Games RB (2014) Introduction to A*. Retrieved from https://www.redblobgames.com/pathfinding/a-star/introduction.html

  11. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271

    Article  MathSciNet  Google Scholar 

  12. Reddy H (2013) Path finding—Dijkstra’s and A* algorithm’s. Int J IT Eng 1–15. Retrieved from http://cs.indstate.edu/hgopireddy/algor.pdf

  13. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107

    Article  Google Scholar 

  14. Wang W, Ye S (2000) Principle and application of artificial intelligence. Electronic Industries Publishing Company

  15. Li L, Tao Y, Langton C (2002) Current situation and future of researching move robot technology. Robot 24(5):475–480

    Google Scholar 

  16. Duchon F, Babinec A, Kajan M, Beno P, Florek M, Fico T, Jurišica L (2014) Path planning with modified a star algorithm for a mobile robot. Procedia Eng 96:59–69

    Article  Google Scholar 

  17. Abiyev RH, Arslan M, Gunsel I, Cagman A (2017) Robot pathfinding using vision based obstacle detection. In: 3rd IEEE international conference on cybernetics (CYBCONF), pp 1–6

  18. Koenig S, Likhachev M (2002) D* Lite. In: Eighteenth national conference on artificial intelligence, pp 476–483

  19. Bay H, Tuytelaars T, Van Gool L (2006) SURF: speeded up robust features. In: European conference on computer vision, pp 404–417

  20. Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. In: International conference on computer vision theory and applications (VISAPP’09), pp 331–340

  21. OpenCV (2017) Thresholding with OpenCV. Retrieved from https://docs.opencv.org/3.4.0/d7/d4d/tutorial_py_thresholding.html

  22. KaewTraKulPong P, Bowden R (2001) An improved adaptive background mixture model for real-time tracking with shadow detection. In: 2nd European workshop on advanced video based surveillance systems (AVBS01), pp 1–5

  23. Henriques JF, Caseiro R, Martins P, Batista J (2014) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Publication Fund under Research Creativity and Management Office, Universiti Sains Malaysia.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia

    Mun-Kit Ng

  2. National Advanced IPv6 Centre, Universiti Sains Malaysia, George Town, Malaysia

    Yung-Wey Chong

  3. Department of Computer Science and Engineering, Sangji University, Wonju, South Korea

    Kwang-man Ko

  4. Division of Computer Science, Sookmyung Women’s University, Seoul, South Korea

    Young-Hoon Park

  5. Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

    Yu-Beng Leau

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  1. Mun-Kit Ng
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  2. Yung-Wey Chong
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  3. Kwang-man Ko
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  4. Young-Hoon Park
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Correspondence to Yung-Wey Chong.

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Ng, MK., Chong, YW., Ko, Km. et al. Adaptive path finding algorithm in dynamic environment for warehouse robot. Neural Comput & Applic 32, 13155–13171 (2020). https://doi.org/10.1007/s00521-020-04764-3

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