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Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

Published online by Cambridge University Press:  28 May 2021

H. S. Hewawasam Open the ORCID record for H. S. Hewawasam [Opens in a new window] ,
M. Yousef Ibrahim ,
Gayan Kahandawa  and
T. A. Choudhury
H. S. Hewawasam*
Affiliation:
School of Science, Engineering and Information Technology, Federation University Australia, Gippsland, Australia
M. Yousef Ibrahim
Affiliation:
School of Science, Engineering and Information Technology, Federation University Australia, Gippsland, Australia
Gayan Kahandawa
Affiliation:
School of Science, Engineering and Information Technology, Federation University Australia, Gippsland, Australia
T. A. Choudhury
Affiliation:
School of Science, Engineering and Information Technology, Federation University Australia, Gippsland, Australia
*
*Corresponding author. Email: h.hewawasam@federation.edu.au
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Abstract

This paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests.

Keywords

agoraphilic algorithmpath planningobject trackingmobile robot navigationartificial potential fieldmoving obstacles
Type
Article
Information
Robotica , Volume 40 , Issue 2 , February 2022 , pp. 329 - 347
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Garcia, M. P., Montie, O., Castillo, O., Sepúlveda, R. and Melin, P., “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation,” Appl. Soft Comput. 9(3), 1102–1110 (2009).10.1016/j.asoc.2009.02.014CrossRefGoogle Scholar
Ibrahim, Y. and Fernandes, A., “Study on Mobile Robot Navigation Techniques,” Proceedings of the 2004 IEEE International Conference on Industrial Technology, 2004. IEEE ICIT’04 (2004) pp. 230–236.Google Scholar
Akbaripour, H. and Masehian, E., “Semi-lazy probabilistic roadmap: A parameter-tuned, resilient and robust path planning method for manipulator robots,” Int. J. Adv. Manuf. Technol. 89(5), 1401–1430 (2017).10.1007/s00170-016-9074-6CrossRefGoogle Scholar
Siming, W., Tiantian, Z. and Weijie, L., “Mobile Robot Path Planning Based on Improved Artificial Potential Field Method,” Proceedings of the 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE) (2018) pp. 29–33.Google Scholar
Zhang, P. Y., Lü, T. S. and Song, L. B., “Soccer robot path planning based on the artificial potential field approach with simulated annealing; Soccer robots; Soccer robots,” Robotica 22(5), 563 (2004).10.1017/S0263574703005666CrossRefGoogle Scholar
Kloetzer, M., Mahulea, C. and Gonzalez, R., “Optimizing Cell Decomposition Path Planning for Mobile Robots Using Different Metrics,” Proceedings of the 19th International Conference on System Theory, Control and Computing (ICSTCC) (2015) pp. 565–570.Google Scholar
Schouwenaars, T., Moor, B. D., Feron, E. and How, J., “Mixed Integer Programming for Multi-Vehicle Path Planning,” Proceedings of the 2001 European Control Conference (ECC) (2001) pp. 2603–2608.Google Scholar
Marble, J. D. and Bekris, K. E., “Asymptotically Near-Optimal Is Good Enough for Motion Planning,” In: Robotics Research: The 15th International Symposium ISRR (Christensen, H. I. and Khatib, O., eds.) (Springer International Publishing, Cham, 2017) pp. 419–436.10.1007/978-3-319-29363-9_24CrossRefGoogle Scholar
Johansson, R. and Saffiotti, A., “Navigating by Stigmergy: A Realization on an RFID Floor for Minimalistic Robots,” Proceedings of the 2009 IEEE International Conference on Robotics and Automation (2009) pp. 245–252.Google Scholar
Moon, C. and Chung, W., “Kinodynamic planner dual-tree RRT (DT-RRT) for two-wheeled mobile robots using the rapidly exploring random tree,” IEEE Trans. Indus. Electr. 62(2), 1080–1090 (2015).CrossRefGoogle Scholar
Khaksar, W., Khaksar, M., Motlagh, O. and Hong, T., “Sampling-based tabu search approach for online path planning,” Adv. Rob. 26(8–9), 1013–1034 (2012).10.1163/156855312X632166CrossRefGoogle Scholar
Mingxin, Y., Sun’an, W., Canyang, W. and Kunpeng, L.. “Hybrid ant colony and immune network algorithm based on improved APF for optimal motion planning,” Robotica 28(6), 833 (2010).10.1017/S0263574709990567CrossRefGoogle Scholar
Mo, H. and Xu, L., “Research of biogeography particle swarm optimization for robot path planning,” Neurocomputing 148(1), 91–99 (2015).CrossRefGoogle Scholar
Pandey, K. and Parhi, D., “Trajectory planning and the target search by the mobile robot in an environment using a behavior-based neural network approach,” Robotica 38(9), 1627–1641 (2020).CrossRefGoogle Scholar
Alomari, A., Phillips, W., Aslam, N. and Comeau, F., “Dynamic fuzzy-logic based path planning for mobility-assisted localization in wireless sensor networks,” Sensors 17(8), (2017). https://www.mdpi.com/1424-8220/17/8/1904 Google Scholar
Khatib, O., “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” Proceedings of the 1985 IEEE International Conference on Robotics and Automation (1985) pp. 500–505.Google Scholar
Hewawasam, H. S., Ibrahim, M. Y., Kahandawa, G. and Choudhury, T. A., “Development and Bench-marking of Agoraphilic Navigation Algorithm in Dynamic Environment,” Proceedings of the 2019 IEEE International Symposium on Industrial Electronics (2019).CrossRefGoogle Scholar
Li, G., Yamashita, A., Asama, H. and Tamura, Y., “An Efficient Improved Artificial Potential Field Based Regression Search Method for Robot Path Planning,” Proceedings of the 2012 IEEE International Conference on Mechatronics and Automation (2012) pp. 1227–1232.Google Scholar
Sfeir, J., Saad, M. and Saliah-Hassane, H., “An Improved Artificial Potential Field Approach to Real-Time Mobile Robot Path Planning in an Unknown Environment,” Proceedings of the 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE) (2011) pp. 208–213.Google Scholar
Babinec, A., Duchon, C., Trung, D., Dekan, M. Mikulová, Z. and Jurisica, L., “Vector Field Histogram* with look-ahead tree extension dependent on time variable environment,” Trans. Inst. Meas. Control 40(4), 1250–1264 (2018).CrossRefGoogle Scholar
Saranrittichai, P., Niparnan, N. and Sudsang, A. “Robust Local Obstacle Avoidance for Mobile Robot Based on Dynamic Window Approach,” Proceedings of the 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (2013) pp. 1–4.Google Scholar
Eriksen, B. H., Breivik, M., Pettersen, K. Y. and Wiig, M. S. “A Modified Dynamic Window Algorithm for Horizontal Collision Avoidance for AUVs,” Proceedings of the 2016 IEEE Conference on Control Applications (CCA) (2016) pp. 499–506.Google Scholar
Liu, C., Lee, S., Varnhagen, S. and Tseng, H. E. “Path Planning for Autonomous Vehicles Using Model Predictive Contro,” Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV) (2017) pp. 174–179.Google Scholar
Son, C., “Intelligent rule-based sequence planning algorithm with fuzzy optimization for robot manipulation tasks in partially dynamic environments,” Inf. Sci. 342(1), 209–221 (2016).CrossRefGoogle Scholar
Babic, B., Nesic, N. and Miljkovic, Z., “A review of automated feature recognition with rule-based pattern recognition,” Comput. Ind. 59(4), 321–337 (2008).CrossRefGoogle Scholar
Montiel, O., Orozco-Rosas, U. and Sepuĺveda, R., “Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles,” Expert Syst. Appl. 42(12), 5177–5191 (2015).CrossRefGoogle Scholar
Kovaćs, B., Szayer, G., Tajti, F., Burdelis, M. and Korondi, P., “A novel potential field method for path planning of mobile robots by adapting animal motion attributes,” Rob. Auto. Syst. 82(C), 24–34 (2016).CrossRefGoogle Scholar
Ibrahim, M. Y. and McFetridge, L., “The Agoraphilic Algorithm: A New Optimistic Approach for Mobile Robot Navigation,” Proceedings of the 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Proceedings (Cat. No.01TH8556) (2001) pp. 1334–1339.Google Scholar
Mohanan, M. G. and Salgoankar, A., “A survey of robotic motion planning in dynamic environments,” Rob. Auto. Syst. 100, 171–185 (2018). doi: 10.1016/j.robot.2017.10.011 CrossRefGoogle Scholar
Patle, B. K., Babu, G., Pandey, A., Parhi, D. R. K. and Jagadeesh, A., “A review: On path planning strategies for navigation of mobile robot,” Def. Technol. 15(4), 582–606 (2019).CrossRefGoogle Scholar
Abdel, M., Al-Rousan, M. and Quadan, L., “Reinforcement based mobile robot navigation in dynamic environment,” Rob. Comput. Integr. Manuf. 27(1), 135–149 (2011).Google Scholar
Yaonan, W., Yimin, Y., Xiaofang, Y., Yi, Z., Yuanli, Z., Feng, Y. and Lei, T., “Autonomous mobile robot navigation system designed in dynamic environment based on transferable belief model,” Measurement 44(8), 1389–1405 (2011).CrossRefGoogle Scholar
Xin, J., Jiao, X., Yang, Y. and Liui, D., “Visual Navigation for Mobile Robot with Kinect Camera in Dynamic Environment,” 2016 35th Chinese Control Conference (CCC) (2016) pp. 4757–4764.Google Scholar
Pandey, A. and Parhi, D. R., “Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm,” Def. Technol. 13(1), 47–58 (2016).CrossRefGoogle Scholar
McFetridge, L. and Ibrahim, M. Y., “A new methodology of mobile robot navigation: The agoraphilic algorithm,” Rob. Comput. Integr. Manuf. 25(3), 545–551 (2009).CrossRefGoogle Scholar
Hewawasam, H. S., Ibrahim, M. Y., Kahandawa, G. and Choudhury, T. A., “Comparative Study on Object Tracking Algorithms for Mobile Robot Navigation in GPS-Denied Environment,” Proceedings of the 2019 IEEE International Conference on Industrial Technology (2019).CrossRefGoogle Scholar
Elnagar, A., “Prediction of Moving Objects in Dynamic Environments Using Kalman Filters,” Proceedings of the 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation (Cat. No.01EX515) (2001) pp. 414–419.Google Scholar
Hewawasam, H. S., Ibrahim, M. Y., Kahandawa, G. and Choudhury, T. A., “Agoraphilic Navigation Algorithm in Dynamic Environment with and without Prediction of Moving Objects Location,” Proceedings of the 2019 IEEE International Symposium on Industrial Electronics Society (2019).CrossRefGoogle Scholar