A scalable tree based path planning for a service robot

• Autorzy: Nippun Kumaar A. A. , Kochuvila Sreeja , Nagaraja S. R.

Identyfikatory

DOI

10.14313/JAMRIS/1‐2022/4

• Języki publikacji: EN

Abstrakty

EN

Path planning plays a vital role in a mobile robot navi‐ gation system. It essentially generates the shortest tra‐ versable path between two given points. There are many path planning algorithms that have been proposed by re‐ searchers all over the world; however, there is very little work focussing on path planning for a service environ‐ ment. The general assumption is that either the environ‐ ment is fully known or unknown. Both cases would not be suitable for a service environment. A fully known en‐ vironment will restrict further expansion in terms of the number of navigation points and an unknown environ‐ ment would give an inefficient path. Unlike other envi‐ ronments, service environments have certain factors to be considered, like user‐friendliness, repeatability, sca‐ lability, and portability, which are very essential for a service robot. In this paper, a simple, efficient, robust, and environment‐independent path planning algorithm for an indoor mobile service robot is presented. Initially, the robot is trained to navigate to all the possible desti‐ nations sequentially with a minimal user interface, which will ensure that the robot knows partial paths in the en‐ vironment. With the trained data, the path planning al‐ gorithm maps all the logical paths between all the des‐ tinations, which helps in autonomous navigation. The al‐ gorithm is implemented and tested using a 2D simulator Player/Stage. The proposed system is tested with two dif‐ ferent service environment layouts and proved to have features like scalability, trainability, accuracy, and repe‐ atability. The algorithm is compared with various classi‐ cal path planning algorithms and the results show that the proposed path planning algorithm is on par with the other algorithms in terms of accuracy and efficient path generation.

Słowa kluczowe

EN

learning from demonstration; path mapping; path planning; navigation system; mobile robot; service robot

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 1
• Strony: 31--45
• Opis fizyczny: Bibliogr. 47 poz., rys.

Twórcy

autor

Nippun Kumaar A. A.

• Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India, www: www.nippunkumaar.in

autor

Kochuvila Sreeja

• Department of Electronics and Communication Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India, www: https://amrita.edu/faculty/k‑sreeja/

autor

Nagaraja S. R.

• Department of Mechanical Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India, www: https://amrita.edu/faculty/sr‑nagaraja/

Bibliografia

• [1] M. Algabri, H. Mathkour, H. Ramdane, and M. Alsulaiman, “Comparative study of soft computing techniques for mobile robot navigation in an unknown environment”, Computers in Human Behavior, vol. 50, 2015, pp. 42 – 56.https://doi.org/10.1016/j.chb.2015.03.062
• [2] V. Alvarez‑Santos, A. Canedo‑Rodriguez, R. Iglesias, X. Pardo, C. Regueiro, and M. Fernandez‑Delgado, “Route learning and reproduction in a tour‑guide robot”, Robotics and Autonomous Systems, vol. 63, Part 2, 2015, pp. 206 – 213.https://doi.org/10.1016/j.robot.2014.07.013
• [3] B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration”, Robotics and Autonomous Systems, vol. 57, no. 5, 2009, pp. 469 – 483.https://doi.org/10.1016/j.robot.2008.10.024
• [4] R. Arkin, “Motor schema‑based mobile robot navigation”, International Journal of Robotics Research, vol. 8, 1989, pp. 92–112.
• [5] A. Bakdi, A. Hentout, H. Boutami, A. Maoudj, O. Hachour, and B. Bouzouia, “Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy‑logic control”, Robotics and Autonomous Systems, vol. 89, 2017, pp. 95 – 109.https://doi.org/10.1016/j.robot.2016.12.008
• [6] D. A. Brenna, B. Brett, and M. V. Manuela, “Policy feedback for the refinement of learned motion control on a mobile robot”, International Journal of Social Robotics, vol. 4, no. 4, 2012, pp. 383–395. 10.1007/s12369‑012‑0156‑9
• [7] S. Choi, E. Kim, K. Lee, and S. Oh, “Real‑time nonparametric reactive navigation of mobile robots in dynamic environments”, Robotics and Autonomous Systems, vol. 91, 2017, pp. 11 – 24.https://doi.org/10.1016/j.robot.2016.12.003
• [8] K. Dermot, U. Nehmzow, and A. S. Billings. “Towards automated code generation for autonomous mobile robots”, June 2010.
• [9] H. S. Dewang, P. K. Mohanty, and S. Kundu,“A robust path planning for mobile robot using smart particle swarm optimization”,Procedia Computer Science, International Conference on Robotics and Smart Manufacturing (RoSMa2018), vol. 133, 2018, pp. 290 – 297. https://doi.org/10.1016/j.procs.2018.07.036
• [10] S. S. Dhanjal. Path Planning in Single and Multi-robot Systems. PhD thesis, BITS, Pilani, May 2016.
• [11] E. W. Dijkstra. “A note on two problems in connexion with graphs”, December 1959.10.1145/3544585.3544600
• [12] D. Dolgov, S. Thrun, M. Montemerlo, and J. Diebel.“Practical search techniques in path planning for autonomous driving”, Jan 2008.
• [13] S. Dong and B. Williams, “Learning and recognition of hybrid manipulation motions in variable environments using probabilistic flow tubes”, International Journal of Social Robotics, vol. 4, no. 4, 2012, pp. 357–368. 10.1007/s12369‑012‑0155‑x
• [14] M. Ehrenmann, O. Rogalla, R. Zöllner, and R. Dillmann. “Teaching service robots complex tasks: Programming by demostration for workshop and household environments”, 2002.
• [15] D. Ferguson and A. T. Stentz. “The field d* algorithm for improved path planning and replanning in uniform and non‑uniform cost environments”, June 2005.
• [16] E. Galceran and M. Carreras, “A survey on coverage path planning for roboics”, Robotics and Autonomous Systems, vol. 61, no. 12, 2013, pp. 1258 – 1276.https://doi.org/10.1016/j.robot.2013.09.004
• [17] A. M. E. Ghalamzan and M. Ragaglia, “Robot learning from demonstrations: Emulation learning in environments with moving obstacles”, Robotics and Autonomous Systems, vol. 101, 2018, pp. 45 – 56.https://doi.org/10.1016/j.robot.2017.12.001
• [18] S. Hacohen, S. Shoval, and N. Shvalb, “Applying probability navigation function in dynamic uncertain environments”, Robotics and Autonomous Systems, vol. 87, 2017, pp. 237 – 246.https://doi.org/10.1016/j.robot.2016.10.010
• [19] D. Halbert. “Programming by example”. PhD thesis, University of California, Berkeley, November 1984.
• [20] M. Hank and M. Haddad, “A hybrid approach for autonomous navigation of mobile robots in partially‑known environments”, Robotics and Autonomous Systems, vol. 86, 2016, pp. 113 – 127. https://doi.org/10.1016/j.robot.2016.09.009
• [21] P. Hart, N. Nilsson, and B. Rafael, “A formal basis for the heuristic determination of minimum cost paths”, IEEE Transactions on Systems Science and Cybernetics, vol. 4, 1968, pp. 100–107.
• [22] J. Hong, Y. Choi, and K. Park. “Mobile robot navigation using modified flexible vector field approach with laser range finder and IR sensor”, 2007 International Conference on Control, Automation and Systems, 2007, pp. 721–726. 10.1109/ICCAS.2007.4406993
• [23] R. Huq, G. K. Mann, and R. G. Gosine, “Mobile robot navigation using motor schema and fuzzy context dependent behavior modulation”, Applied Soft Computing, vol. 8, no. 1, 2008, pp. 422 – 436. https://doi.org/10.1016/j.asoc.2007.02.006
• [24] Y. K. Hwang and N. Ahuja, “A potential field approach to path planning”. In: IEEE Transaction on Robotics and Automation, vol. 8, 1992, pp. 23–32.
• [25] R. Jäkel, S. R. Schmidt‑Rohr, S. W. Rü hl, A. Kasper, Z. Xue, and R. Dillmann, “Learning of planning models for dexterous manipulation based on human demonstrations”, International Journal of Social Robotics, vol. 4, no. 4, 2012, pp. 437–448, 10.1007/s12369‑012‑0162‑y
• [26] F. Kamil, T. S. Hong, W. Khaksar, M. Y. Moghrabiah, N. Zulkifli, and S. A. Ahmad, “New robot navigation algorithm for arbitrary unknown dynamic environments based on future prediction and priority behavior”, Expert Systems with Applications, vol. 86, 2017, pp. 274 – 291. https://doi.org/10.1016/j.eswa.2017.05.059
• [27] J. Lee, Y. Nam, S. Hong, and W. Cho, “New potential functions with random force algorithms using potential field method”, Journal of Intelligent & Robotic Systems, vol. 66, no. 3, 2012, pp. 303–319. 10.1007/s10846‑011‑9595‑z
• [28] R. Limosani, A. Manzi, L. Fiorini, F. Cavallo, and P. Dario, “Enabling global robot navigation based on a cloud robotics approach”,International Journal of Social Robotics, vol. 8, no. 3, 2016, pp. 371–380. 10.1007/s12369‑016‑0349‑8
• [29] S. M. Lavalle. “Rapidly‑exploring random trees: A new tool for path planning”, May 1999.
• [30] T. T. Mac, C. Copot, D. T. Tran, and R. D. Keyser, “Heuristic approaches in robot path planning: A survey”, Robotics and Autonomous Systems, vol. 86, 2016, pp. 13 – 28. https://doi.org/10.1016/j.robot.2016.08.001
• [31] D. Nakhaeinia, S. Tang, S. Noor, and O. Motlagh, “A review of control architectures for autonomous navigation of mobile robots”, International Journal of Physical Sciences, vol. 6, 2011, pp. 169–174.
• [32] U. Nehmzow, O. Akanyeti, C. Weinrich, T. Kyriacou, and S. A. Billings. “Programming mobile robots by demonstration through system identification”, European Conference on Mobile Robots, Freiburg, Germany, 19 Sep 2007 ‑ 21 Sep 2007.
• [33] A. A. NippunKumaar and T. S. B. Sudarshan, “Mobile robot programming by demonstration”. In: 2011 Fourth International Conference on Emerging Trends in Engineering Technology, vol. 394, 2011, pp. 206–209.
• [34] A. A. NippunKumaar and T. S. B. Sudarshan, “Learning from demonstration with state based obstacle avoidance for mobile service robots”, Applied Mechanics and Materials, vol. 394, 2013, pp. 448–4556.
• [35] A. NippunKumaar and T. Sudarshan, “Sensor counter approach for a mobile robot to navigate a path using programming by demonstration”, Procedia Engineering, International Conference on Communication Technology and System Design, vol. 30, 2012, pp. 554 – 561. https://doi.org/10.1016/j.proeng.2012.01.898
• [36] S. Osentoski, B. Pitzer, C. Crick, G. Jay, S. Dong, D. Grollman, H. B. Suay, and O. C. Jenkins, “Remote robotic laboratories for learning from demonstration”, International Journal of Social Robotics, vol. 4, no. 4, 2012, pp. 449–461. 10.1007/s12369‑012‑0157‑8
• [37] A. Pandey, S. Kumar, K. K. Pandey, and D. R. Parhi, “Mobile robot navigation in unknown static environments using anfis controller”, Perspectives in Science, Recent Trends in Engineering andMaterial Sciences, vol. 8, 2016, pp. 421 – 423. https://doi.org/10.1016/j.pisc.2016.04.094
• [38] B. Patle, A. Pandey, A. Jagadeesh, and D. Parhi, “Path planning in uncertain environment by using firefly algorithm”, Defence Technology, vol. 14, no. 6, 2018, pp. 691 – 701.https://doi.org/10.1016/j.dt.2018.06.004
• [39] A. T. Rashid, A. A. Ali, M. Frasca, and L. Fortuna, “Path planning with obstacle avoidance based on visibility binary tree algorithm”, Robotics and Autonomous Systems, vol. 61, no. 12, 2013, pp. 1440 – 1449. https://doi.org/10.1016/j.robot.2013.07.010
• [40] I. R. Roberto, N. Ulrich, K. Theocharis, and B. Steve. “Modelling and characterisation of a mobile robot’s operation”, September 2006.
• [41] A. Stentz. “Optimal and efficient path planning for partially‑known environments”, May 1994.
• [42] Z. Tahir, A. H. Qureshi, Y. Ayaz, and R. Nawaz, “Potentially guided bidirectionalized rrt* for fast optimal path planning in cluttered environments”, Robotics and Autonomous Systems, vol. 108, 2018, pp. 13 – 27. https://doi.org/10.1016/j.robot.2018.06.013
• [43] P. Vadakkepat, T. H. Lee, and L. Xin. “Application of evolutionary artificial potential field in robot soccer system”, Annual Conference of the North American Fuzzy Information Processing Society, July 2001.
• [44] J. Wang, H. Kimura, and M. Sugisaka, “Intelligent control for the vision‑based indoor navigation of an a‑life mobile robot”, Artificial Life and Robotics, vol. 8, no. 1, 2004, pp. 29–33. 10.1007/s10015‑004‑0283‑y
• [45] L. Wang, “Automatic control of mobile robot based on autonomous navigation algorithm”, Artificial Life and Robotics, vol. 24, no. 4, 2019, pp. 494–498. 10.1007/s10015‑019‑00542‑0
• [46] L. Xu, L. G. Zhang, D. G. Chen, and Y. Z. Chen, “The mobile robot navigation in dynamic environment”, 2007 International Conference on Machine Learning and Cybernetics, vol. 1, 2007, pp. 566–571. 10.1109/ICMLC.2007.4370209
• [47] D. Zhenjun, Q. Daokui, X. Fang, and X. Dianguo. “A hybrid approach for mobile robot path planning in dynamic environments”, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2007, pp. 1058‑1063. 10.1109/ROBIO.2007.4522310

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).