Skip to main content

Advertisement

Springer Nature Link
Log in

Woa-fism planning hexapod robot various gaits

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

Abstract

Compared to wheeled and tracked robots, hexapod robots have higher adaptability and higher flexibility in complex terrains. With various gaits, hexapod robots can fulfill different needs better. Existing researches mainly focused on three common gaits, they are single-leg swing gait, wave gait, and tripod gait. Instead of directly planning gaits with swarm intelligence algorithms (SIA), a gait planning method for hexapod robots named finite incremental state machine (FISM) is proposed. FISM focuses on four incremental states between two adjacent gaits of the robot, which greatly reduces the complexity of the gait planning algorithm so that gait planning with SIA is simplified to set the optimal transfer conditions of FISM. In addition, after comparing five optimization algorithms, the whale optimization algorithm (WOA) can set the optimal transfer conditions of FISM. The computer simulation shows WOA-FISM can plan various gaits, finally, a real robot test verifies the effectiveness of various gaits.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Wan CLH, Zhan ZYP, Yan LDJ, Ni Y, Jian J (2022) Review of bionic crawling micro-robots. J Intell Rob Syst 105(3):56

    Article  Google Scholar 

  2. Gon Y, Su G, Nai A, Bidwa ACR, Grezma J, Sartorett G, Daltori KA (2023) Legged robots for object manipulation: a review. Front Mech Eng 9:1142421–1142439. https://doi.org/10.3389/fmech.2023.1142421

    Article  Google Scholar 

  3. Widanag KN, De Silv MJ, Lalitharatn TD, Bul AM, Gopur R (2023) Developments in circular external fixators: a review. Injury 54(12):111157

    Article  Google Scholar 

  4. Se T, Ry S, Wo JH, Ki Y, Ki HS (2023) Stair-climbing robots: a review on mechanism sensing and performance evaluation. IEEE Access 11(5):60539–60561. https://doi.org/10.1109/ACCESS.2023.3286871

    Article  Google Scholar 

  5. Mihai Duguleana GM (2016) Neural networks based reinforcement learning for mobile robots obstacle avoidance. Expert Syst Appl 62:104–115

    Article  Google Scholar 

  6. žlajpah L (2008) Simulation in robotics. Math Comput Simul 79:879–897

  7. Akdağ M, Karagülle LMH (2012) An integrated approach for simulation of mechatronic systems applied to a hexapod robot. Math Comput Simul 82:818–835

    Article  MathSciNet  Google Scholar 

  8. Nygaar TF, Marti CP, Torrese J, Glett K, Howar D (2021) Real-world embodied AI through a morphologically adaptive quadruped robot. Nat Mach Intell 3(5):410–419

    Article  Google Scholar 

  9. Institut: Beihang Universit, C. Beijing 100191 (2017) Effects of pendular waist on gecko’s climbing: Dynamic gait analytical model and bio-inspired robot. J Bionic Eng 14:191–201

  10. Shengchang F, Shuyuan S, Xuan W, Xiaojie W (2023) A walking and climbing quadruped robot capable of ground-wall transition: design, mobility analysis and gait planning. Intel Serv Robot 16:431–451

    Article  Google Scholar 

  11. Tho M, Manoonpon P (2019) A fast online frequency adaptation mechanism for CPG-based robot motion control. IEEE Robot Autom Lett 4(4):3324–3331. https://doi.org/10.1109/LRA.2019.2926660

    Article  Google Scholar 

  12. Fu C (2009) A gait planning method applied to hexapod biomimetic robot locomotion. High Tech Commun Engl 1:7–12

    Google Scholar 

  13. Schuman E, Smit-Anseeu N, Zaytse P, Gleaso R, Shorte KA, Rem CD (2019) Effects of foot stiffness and damping on walking robot performance. In: International conference on robotics and automation (ICRA), pp 3698–3704. https://doi.org/10.1109/ICRA.2019.8794050

  14. Hongbo Z, Minzhou Lu TMJZTLFG (2016) Energy-efficient bio-inspired gait planning and control for biped robot based on human locomotion analysis. J Bionic Eng 13:271–282

    Article  Google Scholar 

  15. Luneckas T (2011) Analysis of hexapod robot locomotion. Mokslas Lietuvos Ateitis 2:36–39

    Article  Google Scholar 

  16. Li R, Meng H, Bai S, Yao Y, Zhang J (2018) Stability and gait planning of 3-UPU hexapod walking robot. Robotics 7:48. https://doi.org/10.3390/robotics7030048

    Article  Google Scholar 

  17. Zheng Zhongyuan ZYWG (2023) Research on piezoelectric driving microminiature three-legged crawling robot. J Bionic Eng 20:1481–1492

    Article  Google Scholar 

  18. Hu Pingzh LZ (2021) Gait planning of quadruped robots based on improved ant colony algorithm. Comput Eng Sci 43:2253–2262

    Google Scholar 

  19. Li Y, Chen Z, Wu C, Mao H, Sun P (2023) A hierarchical framework for quadruped robots gait planning based on DDPG. Biomimetics 8:382. https://doi.org/10.3390/biomimetics8050382

    Article  Google Scholar 

  20. Wang C, Chen X, Yu Z, Dong Y, Chen K, Gergondet P (2023) Robust humanoid robot vehicle ingress with a finite state machine integrated with deep reinforcement learning

  21. Yan Zhepin WYCSWH, Jiny Y (2023) A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning. Math Comput Simul 209:55–86

    Article  MathSciNet  Google Scholar 

  22. Zhang We YHTY, Qingshu G (2022) CPG modulates the omnidirectional motion of a hexapod robot in unstructured terrain. J Bionic Eng 20:558–567

    Article  Google Scholar 

  23. Wang R, Powell N (2020) Hybrid gait planning of a hexapod robot. Mod Electron Technol 4:11–16

    Article  Google Scholar 

  24. Wang B, Zhang K, Yang X, Cui X (2020) The gait planning of hexapod robot based on CPG with feedback. Int J Adv Robot Syst. https://doi.org/10.1177/1729881420930503

    Article  Google Scholar 

  25. Li Y, Fa X, Din L, Wan J, Li T, Ga H (2020) Fault-tolerant tripod gait planning and verification of a hexapod robot. Appl Sci 10(8):2959

    Article  Google Scholar 

  26. Yo B, Fa Y, Li D (2022) Fault-tolerant motion planning for a hexapod robot with single-leg failure using a foot force control method. Int J Adv Robot Syst 19(5):17298806221121070

    Google Scholar 

  27. Wang Y, Tian Yan GYWH (2024) Active torque-based gait adjustment multi-level control strategy for lower limb patient-exoskeleton coupling system in rehabilitation training. Math Comput Simul 215:357–381

    Article  MathSciNet  Google Scholar 

  28. Chen Z, Wang S, Wang J, Xu K, Lei T, Zhang H, Wang X, Liu D, Si J (2021) Control strategy of stable walking for a hexapod wheel-legged robot. ISA Trans 108:367–380

    Article  Google Scholar 

  29. Nogueira HS, Oliveira FG, Pio JL (2021) Discrete movement control of a bio-inspired multi-legged robot. In: 2021 Latin American robotics symposium (LARS), 2021 Brazilian symposium on robotics (SBR), and 2021 Workshop on robotics in education (WRE), pp 174–179. https://doi.org/10.1109/LARS/SBR/WRE54079.2021.9605370

  30. Fu C, Xizhe Z, Jihong Y, Jie Z (2010) A control structure for the autonomous locomotion on rough terrain with a hexapod robot. High Technol Lett 16(3):311–317

    Google Scholar 

  31. Lian J, Hui G (2024) Human evolutionary optimization algorithm. Expert Syst Appl 241:122638

    Article  Google Scholar 

  32. Wan K, Zha H, Men F, Zhan X (2023) Research on the jumping control methods of a quadruped robot that imitates animals. Biomimetics 8(1):36–52

    Article  Google Scholar 

  33. Su Y, Zon C, Pancher F, Che T, Luet TC (2023) Design of topology optimized compliant legs for bio-inspired quadruped robots. Sci Rep 13(1):4875

    Article  Google Scholar 

  34. Hou X, Xin L, Fu Y, Na Z, Gao G, Liu Y, Xu Q, Zhao P, Yan G, Su Y, Cao K (2023) A self-powered biomimetic mouse whisker sensor (BMWS) aiming at terrestrial and space objects perception. Nano Energy 118:109034

    Article  Google Scholar 

  35. Wang X, Fu H, Deng G, Tang K, Chen C, Liu C (2024) Research on motion planning of hexapod robots based on DRL and free gait. J Syst Simul. https://doi.org/10.16182/j.issn1004731x.joss.22-1220

    Article  Google Scholar 

  36. Wang X, Fu H, Deng G, Liu C, Tang K, Chen C (2023) Hierarchical free gait motion planning for hexapod robots using deep reinforcement learning. IEEE Trans Ind Inform 19(11):10901–10912. https://doi.org/10.1109/TII.2023.3240758

  37. Bhardwa G, Dasgupt S, Sukavana N, Balasubramania R (2023) Soft soil gait planning and control for biped robot using deep deterministic policy gradient approach. arXiv preprint arXiv:2306.08063

  38. Wang X, Fu H, Deng G, Liu C, Tang K, Chen C (2023) Hierarchical free gait motion planning for hexapod robots using deep reinforcement learning. IEEE Trans Ind Inform 19(11):10901–10912. https://doi.org/10.1109/TII.2023.3240758

    Article  Google Scholar 

  39. Ros JD, Mohanaprakas T, KrishnaRa M, Diviyasr S (2023) Designing a biped robot’s gait using reinforcement learning’s-actor critic method. In: 2023 International conference on inventive computation technologies (ICICT). IEEE, pp 142–146

  40. Ma F, Yan W, Chen L, Cui R (2022) CPG-based motion planning of hybrid underwater hexapod robot for wall climbing and transition. IEEE Robot Autom Lett 7(4):12299–12306. https://doi.org/10.1109/LRA.2022.3216233

    Article  Google Scholar 

  41. Zhan W, Gon Q, Yan H, Tan Y (2023) CPG modulates the omnidirectional motion of a hexapod robot in unstructured terrain. J Bionic Eng 20(2):558–567

    Article  Google Scholar 

  42. Hülako Halit YO (2020) Control of three-axis manipulator placed on heavy-duty pentapod robot. Simul Model Pract Theory 108:102264

    Article  Google Scholar 

  43. Ome E, Riad Z, Amu A-Y, Issa B, Fad A (2020) Modeling of a biped robot for investigating foot drop using MATLAB/Simulink. Simul Model Pract Theory 98:101972

    Article  Google Scholar 

  44. Luneckas M, Luneckas T, Udris D, Plonis D, Maskeliunas R, Damasevicius R (2019) Energy-efficient walking over irregular terrain: a case of hexapod robot. Metrol Meas Syst 26(4):645–660

    Article  Google Scholar 

  45. Luneckas M, Luneckas T, Kriaučiūnas J, Udris D, Plonis D, Damaševičius R, Maskeliūnas R (2021) Hexapod robot gait switching for energy consumption and cost of transport management using heuristic algorithms. Appl Sci 11(3):1339

    Article  Google Scholar 

  46. Luneckas M, Luneckas T, Udris D, Plonis D, Maskeliūnas R, Damaševičius R (2021) A hybrid tactile sensor-based obstacle overcoming method for hexapod walking robots. Intel Serv Robot 14:9–24

    Article  Google Scholar 

  47. Zhao L, Jin Jie GJ (2021) Robust zeroing neural network for fixed-time kinematic control of wheeled mobile robot in noise-polluted environment. Math Comput Simul 185:289–307

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Electronic Engineering, Guizhou Communication Polytechnic, Guiyang, 551400, China

    Pingzhi Hu

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510000, Guangdong, China

    Mengjian Zhang

  3. College of Electronic Engineering, Guizhou University, Guiyang, 550025, China

    Deguang Wang

Authors
  1. Pingzhi Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Mengjian Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Deguang Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Pingzhi Hu.

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

Hu, P., Zhang, M. & Wang, D. Woa-fism planning hexapod robot various gaits. Intel Serv Robotics 17, 963–979 (2024). https://doi.org/10.1007/s11370-024-00548-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-024-00548-z

Keywords