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Safe mobile robot navigation in human-centered environments using a heat map-based path planner

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Abstract

Safe robot navigation in human-centered environments is important to avoid collisions. A major limitation of the traditional path planning algorithms is that the global path is planned only with the knowledge of static obstacles in the map. This paper presents a novel ‘HMRP (heat map-based robot path planner)’ which uses fixed external cameras to generate a heat map of different passages based on congestion, so that robots can generate congestion-free paths at the global planning stage itself. The congestion values are maintained in a database and the paths are classified into hot and cold regions. Robot navigation is affected by the direction of movement of people. Hence, in this work, the HMRP-based planner also considers the direction of movement of people in passages which improves robot navigation. The proposed HMRP is compared with traditional path planning algorithms in real environment. Results show that the proposed HMRP algorithm generates congestion-free paths for safe robot navigation.

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References

  1. Brock O, Khatib O (1999) High-speed navigation using the global dynamic window approach. In: Proceedings 1999 ieee international conference on robotics and automation (Cat. No.99CH36288C), p 1, vol 1. https://doi.org/10.1109/ROBOT.1999.770002

  2. Che Y, Okamura AM, Sadigh D (2018) Efficient and trustworthy social navigation via explicit and implicit robot-human communication. arXiv:1810.11556

  3. Chung W, Kim H, Yoo Y, Moon C, Park J (2012) The detection and following of human legs through inductive approaches for a mobile robot with a single laser range finder. IEEE Trans Ind Electron 59(8):3156–3166. https://doi.org/10.1109/TIE.2011.2170389

    Article  Google Scholar 

  4. Cosgun A, Sisbot EA, Christensen HI (2016) Anticipatory robot path planning in human environments. In: 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 562–569. https://doi.org/10.1109/ROMAN.2016.7745174

  5. Delling D, Sanders P, Schultes D, Wagner D (2009) Engineering route planning algorithms. In: Lerner J, Wagner D, Zweig K (eds) Algorithmics of large and complex networks, lecture notes in computer science, vol 5515. Springer, Berlin, Heidelberg, pp 117–139. https://doi.org/10.1007/978-3-642-02094-0_7

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

    Article  MathSciNet  Google Scholar 

  7. Fox D, Burgard W, Thrun S (1997) The dynamic window approach to collision avoidance. IEEE Robot Autom Mag 4(1):23–33. https://doi.org/10.1109/100.580977

    Article  Google Scholar 

  8. Haralick R, Sternberg SR, Zhuang X (1987) Image analysis using mathematical morphology. IEEE Trans PAMI Pattern Anal Mach Intell 9(4):532–550. https://doi.org/10.1109/TPAMI.1987.4767941

    Article  Google Scholar 

  9. Hart P, Nilsson N, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. Syst Sci Cybern IEEE Trans 4(2):100–107. https://doi.org/10.1109/TSSC.1968.300136

    Article  Google Scholar 

  10. Koschi M, Pek C, Beikirch M, Althoff M (2018) Set-based prediction of pedestrians in urban environments considering formalized traffic rules. In: 2018 21st international conference on intelligent transportation systems (ITSC), pp 2704–2711. https://doi.org/10.1109/ITSC.2018.8569434

  11. LaValle SM (2006) Planning algorithms. Cambridge University Press, Cambridge, UK. http://planning.cs.uiuc.edu/. Accessed 11 Feb 2016

  12. Liu SB, Roehm H, Heinzemann C, Lütkebohle I, Oehlerking J, Althoff M (2017) Provably safe motion of mobile robots in human environments. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 1351–1357. https://doi.org/10.1109/IROS.2017.8202313

  13. Nishio T, Niitsuma M (2019) Environmental map building to describe walking dynamics for determination of spatial feature of walking activity. In: 2019 IEEE 28th international symposium on industrial electronics (ISIE), pp 2315–2320. https://doi.org/10.1109/ISIE.2019.8781155

  14. Nitta J, Sasaki Y, Mizoguchi H (2015) Path planning using pedestrian information map for mobile robots in a human environment. In: 2015 IEEE international conference on systems, man, and cybernetics, pp 216–221. https://doi.org/10.1109/SMC.2015.50

  15. Penin B, Giordano PR, Chaumette F (2019) Minimum-time trajectory planning under intermittent measurements. IEEE Robot Autom Lett 4(1):153–160. https://doi.org/10.1109/LRA.2018.2883375

    Article  Google Scholar 

  16. Rakhim B, Zhakatayev A, Adiyatov O, Varol HA (2019) Optimal sensor placement of variable impedance actuated robots. In: 2019 IEEE/SICE international symposium on system integration (SII), pp 141–146. https://doi.org/10.1109/SII.2019.8700432

  17. Ratsamee P, Mae Y, Ohara K, Kojima M, Arai T (2013) Social navigation model based on human intention analysis using face orientation. In: 2013 IEEE/RSJ international conference on intelligent robots and systems, pp 1682–1687. https://doi.org/10.1109/IROS.2013.6696575

  18. Ravankar A, Ravankar AA, Hoshino Y, Emaru T, Kobayashi Y (2016) On a hopping-points svd and hough transform based line detection algorithm for robot localization and mapping. Int J Adv Robot Syst 13(3):98. https://doi.org/10.5772/63540

    Article  Google Scholar 

  19. Ravankar A, Ravankar AA, Kobayashi Y, Emaru T (2016) Path smoothing extension for various robot path planners. In: 2016 16th international conference on control, automation and systems (ICCAS), pp 263–268. https://doi.org/10.1109/ICCAS.2016.7832330

  20. Ravankar A, Ravankar A, Kobayashi Y, Emaru T (2017) Symbiotic navigation in multi-robot systems with remote obstacle knowledge sharing. Sensors 17(12):1581. https://doi.org/10.3390/s17071581

    Article  Google Scholar 

  21. Ravankar A, Ravankar A, Kobayashi Y, Hoshino Y, Peng CC (2018a) Path smoothing techniques in robot navigation: State-of-the-art, current and future challenges. Sensors 18(9):3170. https://doi.org/10.3390/s18093170

    Article  Google Scholar 

  22. Ravankar A, Ravankar A, Kobayashi Y, Hoshino Y, Peng CC, Watanabe M (2018b) Hitchhiking based symbiotic multi-robot navigation in sensor networks. Robotics 7(3):37. https://doi.org/10.3390/robotics7030037

    Article  Google Scholar 

  23. Ravankar A, Ravankar A, Rawankar A, Hoshino Y, Kobayashi Y (2019) Itc: Infused tangential curves for smooth 2d and 3d navigation of mobile robots. Sensors 19(20):4384. https://doi.org/10.3390/s19204384

    Article  Google Scholar 

  24. Sasaki T, Hashimoto H (2006) Human observation based mobile robot navigation in intelligent space. In: 2006 IEEE/RSJ international conference on intelligent robots and systems, pp 1044–1049. https://doi.org/10.1109/IROS.2006.281808

  25. Sasaki T, Brscic D, Hashimoto H (2010) Human-observation-based extraction of path patterns for mobile robot navigation. IEEE Trans Ind Electron 57(4):1401–1410. https://doi.org/10.1109/TIE.2009.2030825

    Article  Google Scholar 

  26. Silva G, Olivier A, Crétual A, Pettré J, Fraichard T (2018) Human inspired effort distribution during collision avoidance in human-robot motion. In: 2018 27th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 1111–1117. https://doi.org/10.1109/ROMAN.2018.8525623

  27. Stentz A (1995) The focussed d* algorithm for real-time replanning. In: Proceedings of the international joint conference on artificial intelligence, pp 1652–1659

  28. Stentz A, Mellon IC (1993) Optimal and efficient path planning for unknown and dynamic environments. Int J Robot Autom 10:89–100

    Google Scholar 

  29. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics (intelligent robotics and autonomous agents). The MIT Press, Cambridge

    MATH  Google Scholar 

  30. Zanlungo F, Ikeda T, Kanda T (2011) Social force model with explicit collision prediction. EPL (Europhys Lett) 93(6):68,005. https://doi.org/10.1209/0295-5075/93/68005

    Article  Google Scholar 

  31. Zenatti F, Fontanelli D, Palopoli L, Macii D, Nazemzadeh P (2016) Optimal placement of passive sensors for robot localisation. In: 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 4586–4593. https://doi.org/10.1109/IROS.2016.7759675

  32. Zhang TY, Suen CY (1984) A fast parallel algorithm for thinning digital patterns. Commun ACM 27(3):236–239. https://doi.org/10.1145/357994.358023

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, Kitami Institute of Technology, Hokkaido, Japan

    Abhijeet Ravankar, Yohei Hoshino & Michiko Watanabe

  2. Division of Human Mechanical System and Design, Hokkaido University, Sapporo, Japan

    Ankit A. Ravankar & Yukinori Kobayashi

Authors
  1. Abhijeet Ravankar
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  2. Ankit A. Ravankar
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  3. Yohei Hoshino
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  4. Michiko Watanabe
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  5. Yukinori Kobayashi
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Correspondence to Abhijeet Ravankar.

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This work was presented in part at the 24th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 23–25, 2019.

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Ravankar, A., Ravankar, A.A., Hoshino, Y. et al. Safe mobile robot navigation in human-centered environments using a heat map-based path planner. Artif Life Robotics 25, 264–272 (2020). https://doi.org/10.1007/s10015-020-00591-w

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