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International Conference on Distributed Computing and Intelligent Technology

ICDCIT 2023: Distributed Computing and Intelligent Technology pp 149–164Cite as

Mapped-RRT* a Sampling Based Mobile Path Planner Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13776))

Abstract

Relative efficient computation of motion plans by a Wheeled Robot Platform (WRP) has resulted through an incremental sampling of the considered environment. Although the existing sampling-based path planning techniques hardly converge into an optimal solution and this creates a challenge for a path planner specifically in case of a complex environment occupied with dynamic obstacles. A partially unknown environment creates reasonable problems for a Point-To-Point (PTP) robot to decide certain steps which would merge to the ultimate optimum path solution. To tackle the aforesaid challenge, this concerned paper proposes a noble algorithmic approach, Mapped-RRT*, for concurrent accumulation of optical information from a concerned surrounding GPS (Global Positioning System)-denied indoor environment by combining 2D (Two-Dimensional) and 3D (Three Dimensional) sensor data followed by the application of a sampling pathfinding a strategy for obtaining near-optimum point to point navigation by a wheeled robot. The VO (Visual Odometry) is obtained from a simultaneously created map of the traversed trajectory and serves as an instant perceptible reference during the movement of the mobile robot. For near-perfect detection of every possible on-path obstacles both 2D as well as 3D sensors are used together to collect fused data based on their respective preferential identification process, and a unique algorithmic approach has been proposed for run time traversal map creation along with probabilistic optimized path plan, executable within the optimum amount of time. A numerical comparison of the proposed technique with the performance of conventional strategies with respect to taken parameters confirms the reliability of the carried-out technique. The experimental results would be a citation for future research work in justifying the constructed algorithm within the domain of clash-free ORN (Optimized Robot Navigation).

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Notes

  1. 1.

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References

  1. Azzam, R., Taha, T., Huang, S., Zweiri, Y.: Feature-based visual simultaneous localization and mapping: a survey. SN Appl. Sci. 2(2), 1–24 (2020)

    Article  Google Scholar 

  2. Yousif, K., Bab-Hadiashar, A., Hoseinnezhad, R.: An overview to visual odometry and visual slam: applications to mobile robotics. Intell. Ind. Syst. 1(4), 289–311 (2015)

    Article  Google Scholar 

  3. Merzlyakov, A., Macenski, S.: A comparison of modern general-purpose visual slam approaches. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021)

    Google Scholar 

  4. Subbanna, B. B., Choudhary, K., Singh, S., Kumar, S.: 2D material-based optical sensors: a review. ISSS J. Micro Smart Syst. 11(1), 169–177 (2022)

    Google Scholar 

  5. Yap, P.: Grid-based path-finding. In: Cohen, R., Spencer, B. (eds.) AI 2002. LNCS (LNAI), vol. 2338, pp. 44–55. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47922-8_4

    Chapter  Google Scholar 

  6. Jeong, I.-B., Lee, S.-J., Kim, J.-H.: RRT*-quick: a motion planning algorithm with faster convergence rate. In: Kim, J.-H., Yang, W., Jo, J., Sincak, P., Myung, H. (eds.) Robot Intelligence Technology and Applications 3. AISC, vol. 345, pp. 67–76. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16841-8_7

    Chapter  Google Scholar 

  7. Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2d lidar slam. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271–1278. IEEE (2016)

    Google Scholar 

  8. Li, J., Gao, W., Wu, Y., Liu, Y., Shen, Y.: High-quality indoor scene 3D reconstruction with RGB-D cameras: a brief review. Comput. Vis. Media 1–25 (2022)

    Google Scholar 

  9. Islam, F., Nasir, J., Malik, U., Ayaz, Y., Hasan, O.: RRT-smart: rapid convergence implementation of RRT towards optimal solution. In: 2012 IEEE International Conference on Mechatronics and Automation, pp. 1651–1656. IEEE (2012)

    Google Scholar 

  10. Fragkopoulos, C., Graeser, A.: A RRT based path planning algorithm for rehabilitation robots. In: ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), pp. 1–8 (2010)

    Google Scholar 

  11. Bruce, J., Veloso, M.M.: Real-time randomized path planning for robot navigation. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 288–295. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45135-8_23

    Chapter  Google Scholar 

  12. Gammell, J.D., Srinivasa, S.S., Barfoot, T.D.: Informed RRT*: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2997–3004. IEEE (2014)

    Google Scholar 

  13. Schmid, L., Pantic, M., Khanna, R., Ott, L., Siegwart, R., Nieto, J.: An efficient sampling-based method for online informative path planning in unknown environments. IEEE Robot. Autom. Lett. 5(2), 1500–1507 (2020)

    Article  Google Scholar 

  14. Chaudhuri, R., Deb, S., Shubham, S.: Bio inspired approaches for indoor path navigation and spatial map formation by analysing depth data. In: 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), pp. 1–6 (2022)

    Google Scholar 

  15. Liu, X., Gong, D.: A comparative study of a-star algorithms for search and rescue in perfect maze. In: 2011 International Conference on Electric Information and Control Engineering, pp. 24–27. IEEE (2011)

    Google Scholar 

  16. Bloesch, M., Omari, S., Hutter, M., Siegwart, R.: Robust visual inertial odometry using a direct EKF-based approach. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 298–304. IEEE (2015)

    Google Scholar 

  17. Fan, X., Wang, Y., Zhang, Z.: An evaluation of lidar-based 2d slam techniques with an exploration mode. In: Journal of Physics: Conference Series, vol. 1905, p. 012021. IOP Publishing (2021)

    Google Scholar 

  18. Zingg, S., Scaramuzza, D., Weiss, S., Siegwart, R.: Mav navigation through indoor corridors using optical flow. In: 2010 IEEE International Conference on Robotics and Automation, pp. 3361–3368. IEEE (2010)

    Google Scholar 

  19. Wang, C.-C., Thorpe, C., Thrun, S., Hebert, M., Durrant-Whyte, H.: Simultaneous localization, mapping and moving object tracking. Int. J. Robot. Res. 26(9), 889–916 (2007)

    Article  Google Scholar 

  20. Chaudhuri, R., Deb, S.: Adversarial surround localization and robust obstacle detection with point cloud mapping. In: Das, A.K., Nayak, J., Naik, B., Vimal, S., Pelusi, D. (eds.) Computational Intelligence in Pattern Recognition. CIPR 2022. LNNS, vol. 480, pp. 100–109. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-3089-8_10

  21. Heo, J., Savvides, M.: Gender and ethnicity specific generic elastic models from a single 2d image for novel 2d pose face synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2341–2350 (2011)

    Google Scholar 

  22. Mustafa, M., Stancu, A., Guteirrez, S.P., Codres, E.A., Jaulin, L.: Rigid transformation using interval analysis for robot motion estimation. In: 2015 20th International Conference on Control Systems and Computer Science, pp. 24–31. IEEE (2015)

    Google Scholar 

  23. Zhang, X., Lai, J., Xu, D., Li, H., Fu, M.: 2d lidar-based slam and path planning for indoor rescue using mobile robots. J. Adv. Transp. (2020)

    Google Scholar 

  24. Chen, R., Jing, X., Zhang, S.: Comparative study on 3d optical sensors for short range applications. Opt. Lasers Eng. 149, 106763 (2022)

    Article  Google Scholar 

  25. Zhang, S., Zheng, L., Tao, W.: Survey and evaluation of RGB-D slam. IEEE Access 9, 21367–21387 (2021)

    Article  Google Scholar 

  26. Guo, Y., Wang, H., Qingyong, H., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3D point clouds: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(12), 4338–4364 (2020)

    Article  Google Scholar 

  27. Fraundorfer, F., Scaramuzza, D.: Visual odometry: Part ii: matching, robustness, optimization, and applications. IEEE Robot. Autom. Mag. 19(2), 78–90 (2012)

    Article  Google Scholar 

  28. Rukhin, A.L.: Pattern correlation matrices and their properties. Linear Algebra Appl. 327(1–3), 105–114 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  29. Glaw, X., Inder, K., Kable, A., Hazelton, M.: Visual methodologies in qualitative research: autophotography and photo elicitation applied to mental health research. Int. J. Qual. Methods 16(1), 1609406917748215 (2017)

    Article  Google Scholar 

  30. Dieterle, T., Particke, F., Patino-Studencki, L., Thielecke, J.: Sensor data fusion of lidar with stereo RGB-D camera for object tracking. In: 2017 IEEE Sensors, pp. 1–3 (2017)

    Google Scholar 

  31. Markom, M.A., et al.: A mapping mobile robot using RP lidar scanner. In: 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pp. 87–92 (2015)

    Google Scholar 

  32. Da Silva Neto, J.G., et al.: Comparison of RGB-D sensors for 3D reconstruction. In: 2020 22nd Symposium on Virtual and Augmented Reality (SVR), pp. 252–261 (2020)

    Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology Agartala, Agartala, India

    Rapti Chaudhuri, Suman Deb & Soma Saha

Authors
  1. Rapti Chaudhuri
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  2. Suman Deb
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  3. Soma Saha
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Corresponding author

Correspondence to Rapti Chaudhuri .

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Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Anisur Rahaman Molla

  2. Kent State University, Kent, OH, USA

    Gokarna Sharma

  3. Indian Institute of Management, Lucknow, India

    Pradeep Kumar

  4. Technology Innovation Institute, Abu Dhabi, United Arab Emirates

    Sanjay Rawat

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Chaudhuri, R., Deb, S., Saha, S. (2023). Mapped-RRT* a Sampling Based Mobile Path Planner Algorithm. In: Molla, A.R., Sharma, G., Kumar, P., Rawat, S. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2023. Lecture Notes in Computer Science, vol 13776. Springer, Cham. https://doi.org/10.1007/978-3-031-24848-1_11

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  • DOI: https://doi.org/10.1007/978-3-031-24848-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24847-4

  • Online ISBN: 978-3-031-24848-1

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