Skip to main content
SpringerLink
Log in

Multi-robot path planning using a hybrid dynamic window approach and modified chaotic neural oscillator-based hyperbolic gravitational search algorithm in a complex terrain

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

The current research aims to enhance the humanoid NAO’s ability to plan their overall routes through static and dynamic terrains. The strategy is based on the fusion of the modified hyperbolic gravitational search algorithm and dynamic window approach (DWA) for the navigation of humanoids in various complex terrains. While the updated GSA enhances the fundamental design of basic GSA in terms of exploration and exploitation, the DWA aids in enhancing navigational velocity. The method helps improve overall computational time and, hence, the cost associated with path planning. The modifications to the hyperbolic GSA are carried out by introducing the chaotic neural oscillators to carry out a chaotic search in the immediate surroundings. Three different neural oscillators are chosen and applied to the path planning. The approach helps overcome the cons when the masses/agents show lesser movements in the population. Path planning is carried out in simulation and experimental terrains with and without dynamic obstacles. The proposed MGSA-DWA showed a significant improvement of more than 5% in the path length and time compared with the GSA-DWA. When merged with the Dining Philosophy model, the proposed model effectively avoided dynamic obstacles. The deviation between the simulation and experimental arena was below 6%. The robustness of the proposed model was obtained by comparing it with existing vision-based and other sensor-based approaches. Compared with the vision-based approach, the proposed model effectively traced an optimal path with a 30% improvement in time. The model greatly outperformed other sensor-based methods in a similar scenario.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Munawar HS, Inam H, Ullah F et al (2021) Towards smart healthcare: UAV-based optimized path planning for delivering COVID-19 self-testing kits using cutting edge technologies. Sustain 13:10426. https://doi.org/10.3390/SU131810426

    Article  Google Scholar 

  2. Chen H, Thomas F, Xiongzi L (2008) Automated industrial robot path planning for spray painting a process: a review. In: 4th IEEE Conf Autom Sci Eng CASE pp 522–527 https://doi.org/10.1109/COASE.2008.4626515

  3. Basiri A, Mariani V, Silano G et al (2022) A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture. J Navig 75:364–383. https://doi.org/10.1017/S0373463321000825

    Article  Google Scholar 

  4. Sabiha AD, Kamel MA, Said E, Hussein WM (2022) Real-time path planning for autonomous vehicle based on teaching–learning-based optimization. Intell Serv Robot 15:381–398. https://doi.org/10.1007/S11370-022-00429-3/FIGURES/19

    Article  Google Scholar 

  5. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (Ny) 179:2232–2248. https://doi.org/10.1016/J.INS.2009.03.004

    Article  MATH  Google Scholar 

  6. Vikas, Parhi DR, Kashyap AK, Deepak BBVL (2023) Gravity search algorithm-based path planning of single humanoid based on the study of different artificial intelligence techniques. pp 913–921. https://doi.org/10.1007/978-981-19-4606-6_83/COVER

  7. Vikas, Parhi DR, Kashyap AK (2023) Humanoid robot path planning using memory-based gravity search algorithm and enhanced differential evolution approach in a complex environment. Expert Syst Appl 215:119423. https://doi.org/10.1016/J.ESWA.2022.119423

  8. Huang L, Qin C (2019) A novel modified gravitational search algorithm for the real world optimization problem. Int J Mach Learn Cybern 10:2993–3002. https://doi.org/10.1007/S13042-018-00917-Y/FIGURES/6

    Article  Google Scholar 

  9. Pei L, Haibin D (2012) Path planning of unmanned aerial vehicle based on improved gravitational search algorithm. Sci China Technol Sci 55:2712–2719. https://doi.org/10.1007/s11431-012-4890-x

    Article  Google Scholar 

  10. Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25:1569–1584. https://doi.org/10.1007/S00521-014-1640-Y/TABLES/10

    Article  Google Scholar 

  11. Younes Z, Alhamrouni I, Mekhilef S, Reyasudin M (2021) A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid. Ain Shams Eng J 12:1985–1994. https://doi.org/10.1016/J.ASEJ.2020.10.021

    Article  Google Scholar 

  12. Bohat VK, Arya KV (2018) An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks. Knowl Based Syst 143:192–207. https://doi.org/10.1016/J.KNOSYS.2017.12.017

    Article  Google Scholar 

  13. Xu H, Jiang S, Zhang A (2021) Path planning for unmanned aerial vehicle using a mix-strategy-based gravitational search algorithm. IEEE Access 9:57033–57045. https://doi.org/10.1109/ACCESS.2021.3072796

    Article  Google Scholar 

  14. Khan TA, Ling SH (2021) A novel hybrid gravitational search particle swarm optimization algorithm. Eng Appl Artif Intell 102:104263. https://doi.org/10.1016/J.ENGAPPAI.2021.104263

    Article  Google Scholar 

  15. Shanker R, Bhattacharya M (2020) An automated computer-aided diagnosis system for classification of MR images using texture features and gbest-guided gravitational search algorithm. Biocybern Biomed Eng 40:815–835. https://doi.org/10.1016/J.BBE.2020.03.003

    Article  Google Scholar 

  16. Chakraborti T, Das Sharma K, Chatterjee A (2014) A novel local extrema based gravitational search algorithm and its application in face recognition using one training image per class. Eng Appl Artif Intell 34:13–22. https://doi.org/10.1016/J.ENGAPPAI.2014.05.002

    Article  Google Scholar 

  17. Das PK, Behera HS, Jena PK, Panigrahi BK (2016) Multi-robot path planning in a dynamic environment using improved gravitational search algorithm. J Electr Syst Inf Technol 3:295–313. https://doi.org/10.1016/J.JESIT.2015.12.003

    Article  Google Scholar 

  18. Khanesar MA, Bansal R, Martínez-Arellano G, Branson DT (2020) XOR binary gravitational search algorithm with repository: industry 4.0 applications. Appl Sci 10:6451. https://doi.org/10.3390/APP10186451

    Article  Google Scholar 

  19. Panda MR, Das PK, Pradhan SK, Behera HS (2016) An improved gravitational search algorithm and its performance analysis for multi-robot path planning. In: Proc - 2015 Int Conf Man Mach Interfacing, MAMI 2015. https://doi.org/10.1109/MAMI.2015.7456577

  20. Birx DL, Pipenberg SJ (1992) Chaotic oscillators and complex mapping feed forward networks (CMFFNS) for signal detection in noisy environments. Proc Int J Conf Neural Netw 2:881–888. https://doi.org/10.1109/IJCNN.1992.226876

    Article  Google Scholar 

  21. Yu X, Zhao Q, Lin Q, Wang T (2023) A grey wolf optimizer-based chaotic gravitational search algorithm for global optimization. J Supercomput 79(3):2691–2739. https://doi.org/10.1007/s11227-022-04754-3

    Article  Google Scholar 

  22. Lamamra K, Vaidyanathan S, Azar AT, Ben SC (2017) Chaotic system modelling using a neural network with optimized structure. Fract Order Control Synchron Chaotic Syst. https://doi.org/10.1007/978-3-319-50249-6_29/COVER

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  24. Lai X, Wu D, Wu D et al (2022) Enhanced DWA algorithm for local path planning of mobile robot. Ind Rob Ahead Print. https://doi.org/10.1108/IR-05-2022-0130/FULL/PDF

    Article  Google Scholar 

  25. Ballesteros J, Urdiales C, Velasco ABM, Ramos-Jimenez G (2017) A biomimetical dynamic window approach to navigation for collaborative control. IEEE Trans Hum Mach Syst 47:1123–1133. https://doi.org/10.1109/THMS.2017.2700633

    Article  Google Scholar 

  26. Cai P, Deng X (2020) Incipient fault detection for nonlinear processes based on dynamic multi-block probability related kernel principal component analysis. ISA Trans 105:210–220. https://doi.org/10.1016/J.ISATRA.2020.05.029

    Article  Google Scholar 

  27. Kang Y, De Lima DA, Victorino AC (2015) Dynamic obstacles avoidance based on image-based dynamic window approach for human-vehicle interaction. In: IEEE Intell Veh Symp Proc 2015-August, pp 77–82 https://doi.org/10.1109/IVS.2015.7225666

  28. Özdemir A, Bogosyan SO (2023) Gap based elastic trees as a novel approach for fast and reliable obstacle avoidance for UGVs. J Intell Rob Syst 107(1):9. https://doi.org/10.1007/s10846-022-01792-0

    Article  Google Scholar 

  29. Kobayashi M, Motoi N (2022) Local path planning: dynamic window approach with virtual manipulators considering dynamic obstacles. IEEE Access 10:17018–17029. https://doi.org/10.1109/ACCESS.2022.3150036

    Article  Google Scholar 

  30. Mai X, Li D, Ouyang J, Luo Y (2021) An improved dynamic window approach for local trajectory planning in the environment with dense objects. J Phys Conf Ser 1884:012003. https://doi.org/10.1088/1742-6596/1884/1/012003

    Article  Google Scholar 

  31. Kim J, Yang GH (2022) Improvement of dynamic window approach using reinforcement learning in dynamic environments. Int J Control Autom Syst 20:2983–2992. https://doi.org/10.1007/S12555-021-0462-9/METRICS

    Article  Google Scholar 

  32. Tagliavini L, Colucci G, Botta A et al (2022) Wheeled mobile robots: state of the art overview and kinematic comparison among three omnidirectional locomotion strategies. J Intell Robot Syst. https://doi.org/10.1007/S10846-022-01745-7

    Article  Google Scholar 

  33. Zhong X, Tian J, Hu H, Peng X (2020) Hybrid path planning based on safe A* algorithm and adaptive window approach for mobile robot in large-scale dynamic environment. J Intell Robot Syst 991(99):65–77. https://doi.org/10.1007/S10846-019-01112-Z

    Article  Google Scholar 

  34. Seder M, Petrović I (2007) Dynamic window based approach to mobile robot motion control in the presence of moving obstacles. In: Proc—IEEE Int Conf Robot Autom, pp 1986–1991 https://doi.org/10.1109/ROBOT.2007.363613

  35. Xin P, Wang X, Liu X, Wang Y, Zhai Z, Ma X (2023) Improved bidirectional RRT* algorithm for robot path planning. Sensors 23(2):1041. https://doi.org/10.3390/s23021041

    Article  Google Scholar 

  36. Kashyap AK, Parhi DR, Muni MK, Pandey KK (2020) A hybrid technique for path planning of humanoid robot NAO in static and dynamic terrains. Appl Soft Comput J 96:106581. https://doi.org/10.1016/J.ASOC.2020.106581

    Article  Google Scholar 

  37. Zhang TW, Xu GH, Zhan XS, Han T (2022) A new hybrid algorithm for path planning of mobile robot. J Supercomput 78:4158–4181. https://doi.org/10.1007/S11227-021-04031-9/TABLES/10

    Article  Google Scholar 

  38. Parhi DR, Mohanta JC (2011) Navigational control of several mobile robotic agents using Petri-potential-fuzzy hybrid controller. Appl Soft Comput 11:3546–3557. https://doi.org/10.1016/J.ASOC.2011.01.027

    Article  Google Scholar 

  39. Kashyap AK, Parhi DR (2023) Modified type-2 fuzzy controller for intercollision avoidance of single and multi-humanoid robots in complex terrains. Intel Serv Robot 16(1):87–108. https://doi.org/10.1007/s11370-022-00448-0

    Article  Google Scholar 

  40. Petrovan A, Pop P, Sabo C, Zelina I (2023) Novel two-level hybrid genetic algorithms based on different Cayley-type encodings for solving the clustered shortest-path tree problem. Expert Syst Appl 215:119372. https://doi.org/10.1016/j.eswa.2022.119372

    Article  Google Scholar 

  41. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc ICNN’95 Int Conf Neural Netw 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968

    Article  Google Scholar 

  42. Pelusi D, Mascella R, Tallini L et al (2020) Improving exploration and exploitation via a hyperbolic gravitational search algorithm. Knowl Based Syst 193:105404. https://doi.org/10.1016/J.KNOSYS.2019.105404

    Article  Google Scholar 

  43. Wang Y, Gao S, Yu Y et al (2020) A gravitational search algorithm with chaotic neural oscillators. IEEE Access 8:25938–25948. https://doi.org/10.1109/ACCESS.2020.2971505

    Article  Google Scholar 

  44. Aihara K, Matsumoto G (1987) Forced oscillations and routes to chaos in the hodgkin-huxley axons and squid giant axons. Chaos Biol Syst. https://doi.org/10.1007/978-1-4757-9631-5_15

    Article  Google Scholar 

  45. Falcke M, Huerta R, Rabinovich MI et al (2000) Modeling observed chaotic oscillations in bursting neurons: the role of calcium dynamics and IP3. Biol Cybern 82:517–527. https://doi.org/10.1007/S004220050604

    Article  Google Scholar 

  46. Dijkstra EW (1971) Hierarchical ordering of sequential processes. Orig Concurr Program. https://doi.org/10.1007/978-1-4757-3472-0_5

    Article  Google Scholar 

  47. Ravankar AA, Ravankar A, Emaru T, Kobayashi Y (2020) HPPRM: hybrid potential based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots. IEEE Access 8:221743–221766. https://doi.org/10.1109/ACCESS.2020.3043333

    Article  Google Scholar 

  48. Ding H (2020) Motion path planning of soccer training auxiliary robot based on genetic algorithm in fixed-point rotation environment. J Ambient Intell Humaniz Comput 11:6261–6270. https://doi.org/10.1007/S12652-020-01877-4/TABLES/2

    Article  Google Scholar 

  49. Jahanshahi H, Jafarzadeh M, Sari NN et al (2019) Robot motion planning in an unknown environment with danger space. Electron 8:201

    Article  Google Scholar 

Download references

Acknowledgements

This study is not financed.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, NIT Rourkela, Rourkela, Odisha, 769008, India

    Vikas & Dayal R. Parhi

Authors
  1. Vikas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dayal R. Parhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

V helped in conceptualization, methodology, and writing—original draft preparation; DRP contributed to formal analysis and investigation and writing—review and editing.

Corresponding author

Correspondence to Vikas.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vikas, Parhi, D.R. Multi-robot path planning using a hybrid dynamic window approach and modified chaotic neural oscillator-based hyperbolic gravitational search algorithm in a complex terrain. Intel Serv Robotics 16, 213–230 (2023). https://doi.org/10.1007/s11370-023-00460-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-023-00460-y

Keywords

Search

Navigation