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Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA)

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Abstract

Robotic Navigation Aids (RNAs) assist visually impaired individuals in independent navigation. However, existing research overlooks diverse obstacles and assumes equal responsibility for collision avoidance among intelligent entities. To address this, we propose Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA). Our FLC-ORCA method assigns responsibility for collision avoidance and predicts the velocity of obstacles using a LiDAR-based mobile robot. We conduct experiments in the presence of static, dynamic, and intelligent entities, recording navigation paths, time taken, angle changes, and rerouting occurrences. The results demonstrate that the proposed FLC-ORCA successfully avoids collisions among objects with different collision avoidance protocols and varying liabilities in circumventing obstacles. Comparative analysis reveals that FLC-ORCA outperforms other state-of-the-art methods such as Improved A* and Directional Optimal Reciprocal Collision Avoidance (DORCA). It reduces the overall time taken to complete navigation by 16% and achieves the shortest completion time of 1 min and 38 s, with minimal rerouting (1 occurrence) and the smallest angle change (12°). Our proposed FLC-ORCA challenges assumptions of equal responsibility and enables collision avoidance without pairwise manoeuvres. This approach significantly enhances obstacle avoidance, ensuring safer and more efficient robotic navigation for visually impaired individuals.

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Data availability

The recorded data regarding the hardware experimentation are available in Zenodo, with the identifier http://doi.org/10.5281/zenodo.5786618. While SLAM video recording of the robotic navigation can be accessed in the IEEE Dataport repository http://ieee-dataport.org/9441. Finally, the results of software simulation which supports our research findings are deposited in the IEEE Dataport repository http://ieee-dataport.org/9442.

References

  1. Roijezon U, Prellwitz M, Ahlmark DI, van Deventer J, Nikolakopoulos G, Hyyppa K (2019) A haptic navigation aid for individuals with visual impairments: indoor and outdoor feasibility evaluations of the LaserNavigator. J Vis Impair Blind 113(2):194–201. https://doi.org/10.1177/0145482X19842491

    Article  Google Scholar 

  2. Cardillo E, Caddemi A (2019) Insight on electronic travel aids for visually impaired people: a review on the electromagnetic technology. Electronics 8:1281

    Article  Google Scholar 

  3. Pandey A (2017) Mobile robot navigation and obstacle avoidance techniques: a review. Int Robot Autom J 2(3):10. https://doi.org/10.15406/iratj.2017.02.00023

    Article  Google Scholar 

  4. Gai J, ** for under-canopy navigation of agricultural robotic vehicle. Comput Electron Agric 188:106301. https://doi.org/10.1016/j.compag.2021.106301

    Article  Google Scholar 

  5. Groves K, Hernandez E, West A, Wright T, Lennox B (2021) Robotic exploration of an unknown nuclear environment using radiation informed autonomous navigation. Robotics 10(2):1–15. https://doi.org/10.3390/robotics10020078

    Article  Google Scholar 

  6. Soria E, Schiano F, Floreano D (2021) Predictive control of aerial swarms in cluttered environments. Nat Mach Intell 3(6):545–554. https://doi.org/10.1038/s42256-021-00341-y

    Article  Google Scholar 

  7. MohdRomlay MR, Mohd Ibrahim A, Toha SF, De Wilde P, Venkat I (2021) Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud. PLoS ONE 16(8):21. https://doi.org/10.1371/journal.pone.0256665

    Article  Google Scholar 

  8. Afif M, Ayachi R, Said Y, Pissaloux E, Atri M (2020) An evaluation of retinanet on indoor object detection for blind and visually impaired persons assistance navigation. Neural Process Lett 51(3):2265–2279. https://doi.org/10.1007/s11063-020-10197-9

    Article  Google Scholar 

  9. Zhang H, ** L, Ye C (2021) An RGB-D camera based visual positioning system for assistive navigation by a robotic navigation aid. IEEE/CAA J Autom Sin 8(8):1389–1400. https://doi.org/10.1109/JAS.2021.1004084

    Article  Google Scholar 

  10. Angelopoulos AN, Ameri H, Mitra D, Humayun M (2019) Enhanced depth navigation through augmented reality depth map** in patients with low vision. Sci Rep 9(1):11230. https://doi.org/10.1038/s41598-019-47397-w

    Article  Google Scholar 

  11. Romlay MRM, Toha SF, Ibrahim AM, Venkat I (2021) Methodologies and evaluation of electronic travel aids for the visually impaired people: a review. Bull Electr Eng Informatics 10(3):1747–1758. https://doi.org/10.11591/eei.v10i3.3055

    Article  Google Scholar 

  12. Wahab MNA, Lee CM, Akbar MF, Hassan FH (2020) Path planning for mobile robot navigation in unknown indoor environments using hybrid PSOFS algorithm. IEEE Access 8:161805–161815. https://doi.org/10.1109/ACCESS.2020.3021605

    Article  Google Scholar 

  13. Zhang X, Yao X, Zhu Y, Hu F (2019) An ARCore based user centric assistive navigation system for visually impaired people. Appl Sci 9(5):2019

    Article  Google Scholar 

  14. Meyer E, Robinson H, Rasheed A, San O (2020) Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning. IEEE Access 8:41466–41481. https://doi.org/10.1109/ACCESS.2020.2976586

    Article  Google Scholar 

  15. Martinez-Cruz S, Morales-Hernandez LA, Perez-Soto GI, Benitez-Rangel JP, Camarillo-Gomez KA (2021) An outdoor navigation assistance system for visually impaired people in public transportation. IEEE Access. https://doi.org/10.1109/access.2021.3111544

    Article  Google Scholar 

  16. Pourtousi Z et al (2021) Ability of neural network cells in learning teacher motivation scale and prediction of motivation with fuzzy logic system. Sci Rep 11(1):1–17. https://doi.org/10.1038/s41598-021-89005-w

    Article  Google Scholar 

  17. Ren W, Member GS, Ma OU, Ji H (2020) Human posture recognition using a hybrid of fuzzy logic and machine learning approaches. IEEE Access 8:135628–135639. https://doi.org/10.1109/ACCESS.2020.3011697

    Article  Google Scholar 

  18. Hanyu E, Cui Y, Pedrycz W, Li Z (2019) Enhancements of rule-based models through refinements of Fuzzy C-means. Knowl Based Syst 170:43–60. https://doi.org/10.1016/j.knosys.2019.01.027

    Article  Google Scholar 

  19. Babanezhad M, Zabihi S, Behroyan I, Nakhjiri AT, Marjani A, Shirazian S (2021) Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm. Sci Rep 11(1):1–14. https://doi.org/10.1038/s41598-021-81957-3

    Article  Google Scholar 

  20. Romlay MRM, Azhar MI, Toha SF (2017) Two-wheel Balancing Robot: review on control methods and experiment. Int J Recent Technol Eng 7:106–112

    Google Scholar 

  21. Shihabudheen KV, Pillai GN (2018) Recent advances in neuro-fuzzy system: a survey. Knowl Based Syst 152:136–162. https://doi.org/10.1016/j.knosys.2018.04.014

    Article  Google Scholar 

  22. Kasmi B, Hassam A (2021) Comparative study between fuzzy logic and interval Type-2 fuzzy logic controllers for the trajectory planning of a mobile robot. Eng Technol Appl Sci Res 11(2):7011–7017. https://doi.org/10.48084/etasr.4031

    Article  Google Scholar 

  23. Zong C, Ji Z, Yu Y, Shi H (2020) Research on obstacle avoidance method for mobile robot based on multisensor information fusion. Sens Mater 32(4):1159–1170

    Google Scholar 

  24. Sui Z, Pu Z, Yi J, Wu S (2021) Formation control with collision avoidance through deep reinforcement learning using model-guided demonstration. IEEE Trans Neural Netw Learn Syst 32(6):2358–2372. https://doi.org/10.1109/TNNLS.2020.3004893

    Article  Google Scholar 

  25. Khnissi K, Ben Jabeur C, Seddik H (2020) A smart mobile robot commands predictor using recursive neural network. Rob Auton Syst 131:103593. https://doi.org/10.1016/j.robot.2020.103593

    Article  Google Scholar 

  26. Bouguettaya A, Zarzour H (2022) Deep learning techniques to classify agricultural crops through UAV imagery: a review. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07104-9

    Article  Google Scholar 

  27. Stergiou K, Karakasidis TE (2021) Application of deep learning and chaos theory for load forecasting in Greece. Neural Comput Appl 33(23):16713–16731. https://doi.org/10.1007/s00521-021-06266-2

    Article  Google Scholar 

  28. Lin Z, Yue M, Chen G, Sun J (2021) Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles. Trans Inst Meas Control. https://doi.org/10.1177/01423312211024798

    Article  Google Scholar 

  29. Citakoglu H (2015) Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation. Comput Electron Agric 118:28–37. https://doi.org/10.1016/j.compag.2015.08.020

    Article  Google Scholar 

  30. Citakoglu H (2017) Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Theor Appl Climatol 130(1–2):545–556. https://doi.org/10.1007/s00704-016-1914-7

    Article  Google Scholar 

  31. Cobaner M, Citakoglu H, Kisi O, Haktanir T (2014) Estimation of mean monthly air temperatures in Turkey. Comput Electron Agric 109:71–79. https://doi.org/10.1016/j.compag.2014.09.007

    Article  Google Scholar 

  32. Citakoglu H, Cobaner M, Haktanir T, Kisi O (2014) Estimation of monthly mean reference evapotranspiration in Turkey. Water Resour Manag 28(1):99–113. https://doi.org/10.1007/s11269-013-0474-1

    Article  Google Scholar 

  33. Shentu S, **e F, Liu X, Gong Z (2020) Motion control and trajectory planning for obstacle avoidance of the mobile parallel robot driven by three tracked vehicles. Robotica 39(6):1037–1050. https://doi.org/10.1017/S0263574720000880

    Article  Google Scholar 

  34. Ajeil FH, Ibraheem IK, Azar AT, Humaidi AJ (2020) Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment. Int J Adv Robot Syst 17(3):1–15. https://doi.org/10.1177/1729881420929498

    Article  Google Scholar 

  35. Mortazavi A, Moloodpoor M (2021) Enhanced butterfly optimization algorithm with a new fuzzy regulator strategy and virtual butterfly concept. Knowl Based Syst 228:107291. https://doi.org/10.1016/j.knosys.2021.107291

    Article  Google Scholar 

  36. Rawat P, Chauhan S (2021) Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06059-7

    Article  Google Scholar 

  37. Snape J, Member S, Guy SJ, Manocha D (2011) The hybrid reciprocal velocity obstacle. IEEE Trans Robot 27599:696–706

    Article  Google Scholar 

  38. Liang J, Patel U, Sathyamoorthy AJ, Manocha D (2020) Crowd-steer: realtime smooth and collision-free robot navigation in densely crowded scenarios trained using high-fidelity simulation. In: IJCAI international joint conference on artificial intelligence pp 4221–4228. https://doi.org/10.24963/ijcai.2020/583.

  39. Yao S, Chen G, Qiu Q, Ma J, Chen X, Ji J (2021) Crowd-aware robot navigation for pedestrians with multiple collision avoidance strategies via map-based deep reinforcement learning. ar**v preprint (online). Available: https://github.com/snape/RVO2.

  40. Murugan NPM (2020) Natural disaster resilience approach (NDRA) to online social networks. J Ambient Intell Humaniz Comput 12:5651. https://doi.org/10.1007/s12652-020-02644-1

    Article  Google Scholar 

  41. Kleinmeier B (2021) Modeling of behavioral changes in agent-based simulations (Doctoral dissertation, Technische Universität München)

  42. Van Den Berg J, Lin M, Manocha D (2008) Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE international conference on robotics and automation, pp 1928–1935

  43. Fiorini P, Shiller Z (1998) Motion planning in dynamic environments using velocity obstacles. Int J Robot Res. https://doi.org/10.1177/027836499801700706

    Article  MATH  Google Scholar 

  44. Van Den Berg J, Guy SJ, Lin M, Manocha D (2011) Reciprocal n -body collision avoidance. In: Springer Tracts in Advanced Robotic pp 3–19

  45. Alonso-mora J, Breitenmoser A, Beardsley P, Siegwart R (2012) Reciprocal collision avoidance for multiple car-like robots. IEEE Int Conf Robot Autom, pp 360–366

  46. Levy A, Keitel C, Engel S, Mclurkin J (2015) The extended velocity obstacle and applying ORCA in the real world. In: International conference on robotics and automation pp 16–22

  47. Godoy J, Guy SJ, Gini M, Karamouzas I (2020) C-Nav: distributed coordination in crowded multi-agent navigation. Rob Auton Syst 133:103631. https://doi.org/10.1016/j.robot.2020.103631

    Article  Google Scholar 

  48. Cheng H, Zhu Q, Liu Z, Xu T, Lin L (2017) Decentralized navigation of multiple agents based on ORCA and model predictive control. IEEE/RSJ international conference on intelligent robots and systems, pp 3446–3451

  49. Zhong X, Zhong X, Peng X (2014) Velocity-change-space-based dynamic motion planning for mobile robots navigation. Neurocomputing 143:153–163. https://doi.org/10.1016/j.neucom.2014.06.010

    Article  Google Scholar 

  50. Choi M, Rubenecia A, Shon T, Choi HH (2017) Velocity obstacle based 3D collision avoidance scheme for low-cost micro UAVs. Sustainability 9:1174. https://doi.org/10.3390/su9071174

    Article  Google Scholar 

  51. Huang Y, van Gelder PHAJM, Wen Y (2018) Velocity obstacle algorithms for collision prevention at sea. Ocean Eng 151:308–321. https://doi.org/10.1016/j.oceaneng.2018.01.001

    Article  Google Scholar 

  52. LisiniBaldi T, Scheggi S, Aggravi M, Prattichizzo D (2018) Haptic guidance in dynamic environments using optimal reciprocal collision avoidance. IEEE Robot Autom Lett 3(1):265–272. https://doi.org/10.1109/LRA.2017.2738328

    Article  Google Scholar 

  53. Niu H, Ma C, Han P (2021) Directional optimal reciprocal collision avoidance”. Rob Auton Syst 136:103705. https://doi.org/10.1016/j.robot.2020.103705

    Article  Google Scholar 

  54. Guo K, Wang D, Fan T, Pan J (2021) VR-ORCA: variable responsibility optimal reciprocal collision avoidance. IEEE Robot Autom Lett 6(3):4520–4527. https://doi.org/10.1109/LRA.2021.3067851

    Article  Google Scholar 

  55. Arul HS, Manocha D (2021) V-RVO: Decentralized multi-agent collision avoidance using voronoi

  56. Janardanan JK (2013) Decentralized collision avoidance. In: Computer science and engineering: Theses, Dissertations student research, vol 61

  57. Mao R, Gao H, Guo L (2020) A novel collision-free navigation approach for multiple nonholonomic robots based on ORCA and linear MPC. Math Probl Eng. https://doi.org/10.1155/2020/4183427

    Article  MathSciNet  MATH  Google Scholar 

  58. Alonso-mora J, Breitenmoser A, Rufli M, Beardsley P, Siegwart R (2013) Optimal reciprocal collision avoidance for multiple non-holonomic robots. In: Distributed autonomous robotic systems pp 203–216

  59. He L, van den Berg J (2013) Meso-scale planning for multi-agent navigation. In: 2013 IEEE international conference on robotics and automation (ICRA), pp 2839–2844

  60. Bareiss D, Van Den Berg J (2015) Generalized reciprocal collision avoidance. Int J Rob Res 34(12):1501–1514. https://doi.org/10.1177/0278364915576234

    Article  Google Scholar 

  61. Snape J, Manocha D (2010) Navigating multiple simple-airplanes in 3D workspace. In: IEEE international conference on robotics and automation, pp 3974–3980. https://doi.org/10.1109/ROBOT.2010.5509580

  62. Van Den Berg J, Snape J, Guy SJ, Manocha D (2011) Reciprocal collision avoidance with acceleration-velocity obstacles. In: IEEE international conference on robotics and automation 3475–3482. https://doi.org/10.1109/ICRA.2011.5980408

  63. Wang Y, Cavallaro A (2017) Active visual tracking in multi-agent scenarios. In: 14th IEEE international conference on advanced video and signal based surveillance (AVSS), pp 1–6

  64. Pandey A, Panwar VS, Ehtesham Hasan M, Parhi DR (2020) V-REP-based navigation of automated wheeled robot between obstacles using PSO-tuned feedforward neural network. J Comput Des Eng 7(4):427–434. https://doi.org/10.1093/jcde/qwaa035

    Article  Google Scholar 

  65. Nadour M, Boumehraz M, Cherroun L, Puig V (2019) Mobile robot visual navigation based on fuzzy logic and optical flow approaches. Int J Syst Assur Eng Manag 10(6):1654–1667. https://doi.org/10.1007/s13198-019-00918-2

    Article  Google Scholar 

  66. Aouf A, Boussaid L, Sakly A (2019) Same fuzzy logic controller for two-wheeled mobile robot navigation in strange environments. Robot J 1:1–11. https://doi.org/10.1155/2019/2465219

    Article  Google Scholar 

  67. Nakrani NM, Joshi MM (2021) A human-like decision intelligence for obstacle avoidance in autonomous vehicle parking. Appl Intell. https://doi.org/10.1007/s10489-021-02653-3

    Article  Google Scholar 

  68. Chen G et al (2021) Deep reinforcement learning of map—based obstacle avoidance for mobile robot navigation. SN Comput Sci. https://doi.org/10.1007/s42979-021-00817-z

    Article  Google Scholar 

  69. Pandey KK, Parhi DR (2019) 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. https://doi.org/10.1017/S0263574719001668

    Article  Google Scholar 

  70. Song H, Li A, Wang T, Wang M (2021) Multimodal deep reinforcement learning with auxiliary task. Sensors 21(4):1363

    Article  Google Scholar 

  71. Wang H, Fu Z, Zhou J, Fu M, Ruan L (2021) Cooperative collision avoidance for unmanned surface vehicles based on improved genetic algorithm. Ocean Eng 222:108612. https://doi.org/10.1016/j.oceaneng.2021.108612

    Article  Google Scholar 

  72. Lopez-Gonzalez A, Campaña JAM, Martínez EGH, Contro PP (2019) Multi robot distance based formation using parallel genetic algorithm. Appl Soft Comput J 86:105929. https://doi.org/10.1016/j.asoc.2019.105929

    Article  Google Scholar 

  73. Sari WE, Wahyunggoro O, Fauziati S (2016) A comparative study on fuzzy Mamdani-Sugeno-Tsukamoto for the childhood tuberculosis diagnosis. In: AIP conference proceeding, vol 1755. https://doi.org/10.1063/1.4958498

  74. Julio BarónVelandia SCVA, Quintana JSC (2021) Environment humidity and temperature prediction in agriculture using Mamdani inference systems agriculture using Mamdani inference systems. Int J Electr And Computer Eng 11(4):3502–3509

    Google Scholar 

  75. Erke S, Bin D, Yiming N, Qi Z, Liang X, Dawei Z (2020) An improved A-Star based path planning algorithm for autonomous land vehicles. Int J Adv Robot Syst 17(5):1–13. https://doi.org/10.1177/1729881420962263

    Article  Google Scholar 

  76. Tannenbaum C, Ellis RP, Eyssel F, Zou J, Schiebinger L (2019) Sex and gender analysis improves science and engineering. Nature 575:137–146. https://doi.org/10.1038/s41586-019-1657-6

    Article  Google Scholar 

  77. Omar MA, Ahmed HM, Batakoushy HA, Abdel MA (2020) Spectrochimica Acta Part A: molecular and biomolecular spectroscopy new spectro fl uorimetric analysis of empagli flozin in its tablets and human plasma using two level full factorial design. Spectrochim Acta Part A Mol Biomol Spectrosc 235:118307. https://doi.org/10.1016/j.saa.2020.118307

    Article  Google Scholar 

  78. Mihăilescu M et al (2021) Full factorial design for gold recovery from industrial solutions. Toxics 9(5):1–17. https://doi.org/10.3390/toxics9050111

    Article  Google Scholar 

  79. Walzenbach S (2019) Hiding sensitive topics by design? An experiment on the reduction of social desirability bias in factorial surveys. Surv Res Methods 13(1):103–121

    Google Scholar 

  80. Romlay MRM, Ibrahim AM, Toha SF, Ahmad MS (2022) UNITY simulation for navigation using FLC-ORCA, improved A-Star & directional ORCA. IEEE Dataport

  81. Romlay MRM, Ibrahim AM, Toha SF, Ahmad MS (2021) Computation time, searched nodes and path length for navigation using improved A-star, directional ORCA and FLC-ORCA. Zenodo. https://doi.org/10.5281/zenodo.5786618

  82. Romlay MRM, Ibrahim AM, Toha SF, Ahmad MS (2022) SLAM Recording for Navigation using FLC-ORCA, improved A star and directional ORCA. IEEE Dataport

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Acknowledgements

This research is supported by the IRAGS 2018 Grant: IRAGS18-014-0015 awarded by the International Islamic University Malaysia (IIUM).

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Authors and Affiliations

  1. Department of Mechatronics Engineering, International Islamic University Malaysia (IIUM), Jalan Gombak, 53100, Kuala Lumpur, Malaysia

    Muhammad Rabani Mohd Romlay, Azhar Mohd Ibrahim, Siti Fauziah Toha & Muhammad Syahmi Ahmad

  2. Division of Natural Sciences, University of Kent, Canterbury, UK

    Philippe De Wilde

  3. School of Computing and Informatics, Universiti Teknologi Brunei, Tungku Highway, Gadong, 1410, BE, Brunei Darussalam

    Ibrahim Venkat

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Appendices

Appendix 1: Range and equation of membership function

Table

Table 11 Range and equation of each membership function input for FLC 1
Full size table

11 shows the input of position, velocity and acceleration of the FLC 1 with the given output of avoidance responsibility. The range of the obstacle’s distance is (0 < x ≤ 40 m), whereas the range of velocity is (0 < v ≤ 200 m/s) and acceleration is (\(-100 \mathrm{m}/{\mathrm{s}}^{2}<a\le 100 \mathrm{m}/{\mathrm{s}}^{2}\)).

The range and equations of each membership function of FLC 2 are described in Table

Table 12 Range and equations of each membership function for the input of FLC 2
Full size table

12. Within the table, the inputs of FLC 2 are shown to be the velocity, density and acceleration. The range of the velocity is (0 < v ≤ 200 m/s), density is (0 object/\({m}^{2}\) < ρ \(\le\) 8 objects/\({\mathrm{m}}^{2}\)), and acceleration is (\(-100 \mathrm{m}/{\mathrm{s}}^{2}<a\le 100 \mathrm{m}/{\mathrm{s}}^{2}\)).

Appendix 2: Input variables

Table

Table 13 Input variables of FLC 1
Full size table

13 shows the input variable of FLC 1. All possible combinations of antecedents and the corresponding consequents result in 48 total rules enumerated.

For the FLC 2, calculating all possible combinations assemble 36 rules in total. Table

Table 14 Input variables of FLC 2
Full size table

14 shows the input variable of FLC 2.

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Mohd Romlay, M.R., Mohd Ibrahim, A., Toha, S.F. et al. Obstacle avoidance for a robotic navigation aid using Fuzzy Logic Controller-Optimal Reciprocal Collision Avoidance (FLC-ORCA). Neural Comput & Applic 35, 22405–22429 (2023). https://doi.org/10.1007/s00521-023-08856-8

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