Skip to main content
SpringerLink
Log in

Swarm Robot Multitarget Search Strategy Based on Triangular Cones in a Complex Dynamic Nonconvex Obstacle Environment

  • Regular paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Previous studies have not extensively researched swarm robot multiobjective search in a complex environment sunch as unknown dynamic nonconvex (UDNC) obstacle environment to address collision prediction and low search efficiency. In addition, research on unnecessary excessive robot rotation and the loss caused by rotation during movement has not received enough attention. In this paper, a multitarget search strategy of a robot swarm based on triangular cones (MSTC) is proposed to satisfy the previously mentioned challenges. First, to address the problem of obstacle avoidance in a UDNC environment, an expanding triangular cone (ETC) method is proposed. This method not only considers the size of the robot but also enables the robot to avoid obstacles and other robots effectively and safely. Second, a roaming direction cone (RDC) and a target search cone (TSC) are proposed to improve the robot’s search ability. The RDC improves the ability to exploit the global unknown environment, while the TSC effectively enhances the local search capabilities. Furthermore, the three methods can avoid the time consumption, energy consumption and mechanical loss due to unnecessary direction rotation through a simple geometric relationship to improve the robot service life. Finally, the three proposed methods are combined to construct an MSTC strategy to simulate the multitarget search process of swarm robots. Simulation experiments are carried out using a simplified virtual force (SVF), a straight-line search method different from the nearest neighbour (LSDN), and a particle swarm optimization algorithm with kinematic constraints (KCPSO). The results show that the time consumption, the road consumption, the degree of rotation and the number of rotations have all made significant improvements.

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

Similar content being viewed by others

References

  1. Tan, Y., Zheng, Z.: Research advance in swarm robotics[J]. Def. Technol. 9(1), 18–39 (2013)

    Article  Google Scholar 

  2. Dorigo, M., Theraulaz, G., Trianni, V.: Reflections on the future of swarm robotics[J]. Science. Robotics. 5(49), eabe4385 (2020)

    Article  Google Scholar 

  3. Kantor, G., Singh, S., Peterson, R., et al.: Distributed search and rescue with robot and sensor teams[C]// Field and Service Robotics. Springer, Berlin, Heidelberg, 529–538 (2003)

  4. Stormont, D.P.: Autonomous rescue robot swarms for first responders[C]//CIHSPS 2005. Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2005. IEEE, 151–157 (2005)

  5. Din, A., Jabeen, M., Zia, K., et al.: Behavior-based swarm robotic search and rescue using fuzzy controller[J]. Comput. Electr. Eng. 70, 53–65 (2018)

    Article  Google Scholar 

  6. Recchiuto, C.T., Sgorbissa, A.: Post-disaster assessment with unmanned aerial vehicles: A survey on practical implementations and research approaches[J]. Journal of Field Robotics. 35(4), 459–490 (2018)

    Article  Google Scholar 

  7. Acar, E.U., Choset, H., Zhang, Y., et al.: Path planning for robotic demining: Robust sensor-based coverage of unstructured environments and probabilistic methods[J]. Int. J. Robot. Res. 22(7–8), 441–466 (2003)

    Article  Google Scholar 

  8. Nguyen, L.A., Harman, T.L., Fairchild, C.: Swarmathon: a swarm robotics experiment for future space exploration[C]//2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR). IEEE, B1–3-1-B1–3-4 (2019)

  9. Yang, B., Ding, Y., Jin, Y., et al.: Self-organized swarm robot for target search and trapping inspired by bacterial chemotaxis[J]. Robot. Auton. Syst. 72, 83–92 (2015)

    Article  Google Scholar 

  10. Ismail, Z.H., Hamami, M.G.M.: Systematic literature review of swarm robotics strategies applied to target search problem with environment constraints[J]. Appl. Sci. 11(5), 2383 (2021)

    Article  Google Scholar 

  11. Sharma, A., Shoval, S., Sharma, A., et al.: Path planning for multiple targets interception by the swarm of UAVs based on swarm intelligence algorithms: A review[J]. IETE Tech. Rev. 39(3), 675–697 (2022)

    Article  Google Scholar 

  12. Dadgar, M., Jafari, S., Hamzeh, A.: A PSO-based multi-robot cooperation method for target searching in unknown environments[J]. Neurocomputing. 177, 62–74 (2016)

    Article  Google Scholar 

  13. Tang, Q., Ding, L., Yu, F., Zhang, Y., Li, Y., Tu, H.: Swarm Robots Search for Multiple Targets Based on an Improved Grouping Strategy. IEEE/ACM Trans. Comput. Biol. Bioinform. 15(6), 1943–1950 (2018). https://doi.org/10.1109/TCBB.2017.2682161

    Article  Google Scholar 

  14. Li, J., Tan, Y.: A probabilistic finite state machine based strategy for multi-target search using swarm robotics[J]. Appl. Soft Comput. 77, 467–483 (2019)

    Article  Google Scholar 

  15. Du, Y.: A Novel Approach for Swarm Robotic Target Searches Based on the DPSO Algorithm. IEEE Access. 8, 226484–226505 (2020). https://doi.org/10.1109/ACCESS.2020.3045177

    Article  Google Scholar 

  16. He, X., et al.: Multiobjective coordinated search algorithm for swarm of UAVs based on 3D-simplified virtual forced model. Int. J. Syst. Sci. 51(14), 2635–2652 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  17. Tang, Q., Yu, F., Xu, Z., et al.: Swarm robots search for multiple targets[J]. IEEE Access. 8, 92814–92826 (2020)

    Google Scholar 

  18. Wang, M., Zhou, S.W., Zhang, H.Q., Wu, L.H., Zhou, Y., He, X.J.: Multi-target search of swarm robots cooperative control in an unknown environment [J]. Control Theory Appl. 39(04), 750–760 (2022)

    Google Scholar 

  19. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Autonomous robot vehicles, pp. 396–404. Springer, New York (1986)

    Chapter  Google Scholar 

  20. Duhé, J.F., Victor, S., Melchior, P.: Contributions on artificial potential field method for effective obstacle avoidance[J]. Fract. Calc. Appl Anal. 24(2), 421–446 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  21. Tian, J., et al.: Research on Active Obstacle Avoidance of Intelligent Vehicles Based on Improved Artificial Potential Field Method. World Electr Veh J. 13(6), 97 (2022)

    Article  Google Scholar 

  22. Zhang, H.Q., Zhang, J., Zhou, S.W., Zeng, Z.F., Wu, L.H.: Hunting in unknown complex environments by swarm robots based on simplified virtual-force model[J]. Acta Electron. Sin. 43(04), 665–674 (2015)

    Google Scholar 

  23. Akkaya, S., Akbati, O., Ergenc, A.F.: Consensus Control of Mobile Agents with Obstacle Avoidance using Collision Cone Approach. 2018 6th International Conference on Control Engineering & Information Technology (CEIT), pp. 1–5 (2018) https://doi.org/10.1109/CEIT.2018.8751824

  24. Chakravarthy, A., Ghose, D.: Collision cone-based net capture of a swarm of unmanned aerial vehicles[J]. J. Guid. Control. Dyn. 43(9), 1688–1710 (2020)

    Article  Google Scholar 

  25. Gnanasekera, M., Katupitiya, J.: Collision Cone Based Collision Avoidance for Hexa-Rotors. 2022 8th International Conference on Control, Automation and Robotics (ICCAR). IEEE, (2022)

  26. Zhong, X., Peng, X., Zhou, J.: Dynamic collision avoidance of mobile robot based on velocity obstacles[C]//Proceedings 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE). IEEE, 2410–2413 (2011)

  27. Huang, Y., Chen, L., Van Gelder, P.: Generalized velocity obstacle algorithm for preventing ship collisions at sea[J]. Ocean Eng. 173, 142–156 (2019)

    Article  Google Scholar 

  28. Eberhart, R., Kennedy, J.: Particle swarm optimization. Proceedings of the IEEE international conference on neural networks. Vol. 4. (1995)

  29. Du, Y.: A novel approach for swarm robotic target searches based on the DPSO algorithm[J]. IEEE Access. 8, 226484–226505 (2020)

    Article  Google Scholar 

  30. Garg, V., Shukla, A., Tiwari, R.: AERPSO-An adaptive exploration robotic PSO based cooperative algorithm for multiple target searching[J]. Expert Syst. Appl. 209, 118245 (2022)

    Article  Google Scholar 

  31. Yong, L., Yu, L., Yipei, G., et al.: Cooperative path planning of robot swarm based on ACO[C]//2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 1428–1432 (2017)

  32. Song, B., Miao, H., Lin, X.: Path planning for coal mine robot via improved ant colony optimization algorithm. Syst Sci Control Eng. 9(1), 283–289 (2021)

    Article  Google Scholar 

  33. Tian, H.: Research on robot optimal path planning method based on improved ant colony algorithm. Int. J. Comput. Sci. Math. 13(1), 80–92 (2021)

    Article  MathSciNet  Google Scholar 

  34. Yang, C., Tu, X., Chen, J.: Algorithm of marriage in honey bees optimization based on the wolf pack search. The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007). IEEE, (2007)

  35. Wang, Z., Zhang, J.: A task allocation algorithm for a swarm of unmanned aerial vehicles based on bionic wolf pack method[J]. Knowl.-Based Syst. 250, 109072 (2022)

    Article  Google Scholar 

  36. Hu, J., Husheng, W., Zhan, R.: Wolf Pack Intelligence: From Biological Intelligence to Cooperative Control for Swarm Robotics. In: Advances in Guidance, Navigation and Control, pp. 4943–4955. Springer, Singapore (2022)

    Chapter  Google Scholar 

  37. Chen, X., Huang, J.: Odor source localization algorithms on mobile robots: A review and future outlook[J]. Robot. Auton. Syst. 112, 123–136 (2019)

    Article  Google Scholar 

  38. Chakravarthy, A., Ghose, D.: Obstacle avoidance in a dynamic environment: A collision cone approach. IEEE Trans Syst Man Cybern A Syst Hum. 28(5), 562–574 (1998)

    Article  Google Scholar 

  39. Pugh, J., Martinoli, A.: Inspiring and modeling multi-robot search with particle swarm optimization[C]// 2007 IEEE swarm intelligence symposium. IEEE, 332–339 (2007)

  40. Zhou, S.W., Zhang, X., Zhang, H.Q., et al.: Coordinated control of swarm robots for multi-target search based on a simplified virtual-force model [J]. Robot. 38(06), 641–650 (2016)

    Google Scholar 

  41. Zhang, Y.Z., Xue, S.D., Zeng, J.C.: Dynamic task allocation with closed-loop adjusting in swarm robotic search for multiple targets[J]. Robot. 36(01), 57–68 (2014)

    Article  Google Scholar 

  42. Goss, J., Rajvanshi, R., Subbarao, K.: Aircraft conflict detection and resolution using mixed geometric and collision cone approaches[C]//AIAA Guidance, Navigation, and Control Conference and Exhibit. 4879 (2004)

  43. Xu, X., Pan, W., Huang, Y., et al.: Dynamic collision avoidance algorithm for unmanned surface vehicles via layered artificial potential field with collision cone[J]. J Navig. 73(6), 1306–1325 (2020)

    Article  Google Scholar 

  44. Lin, Z., Castano, L., Mortimer, E., et al.: Fast 3D collision avoidance algorithm for fixed wing UAS[J]. J. Intell. Robot. Syst. 97(3), 577–604 (2020)

    Article  Google Scholar 

  45. Pu, H.Y., Ding, F., Li, X.M., Luo, J., Peng, Y.: Maritime autonomous obstacle avoidance in a dynamic environment based on collision cone of ellipse[J]. Chin. J. Sci. Instrum. 38(07), 1756–1762 (2017)

    Google Scholar 

  46. Yang, X.X., Zhang, Y., Zhou, W.W.: Autonomous obstacle avoidance algorithm for UAV in dynamic uncertain environment[J]. Syst Eng Electron. 39(11), 2546–2552 (2017)

    Google Scholar 

  47. Pang, B., Song, Y., Zhang, C., et al.: Effect of random walk methods on searching efficiency in swarm robots for area exploration[J]. Appl. Intell. 51(7), 5189–5199 (2021)

    Article  Google Scholar 

  48. Grima, C.I., Marquez, A., Ortega, L.: A new 2D tessellation for angle problems: The polar diagram[J]. Comput. Geom. 34(2), 58–74 (2006)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Notes on Contributions

Xiaohui Bian, 1997, Fuyang City, Anhui Province, Master’s degree, research direction: swarm intelligence optimization algorithm, swarm robot system, Multi target search of swarm robots.

Shaowu Zhou, 1964, Xiangtan City, Hunan Province, Doctor, second-level professor, doctoral supervisor, research direction: Robust control of complex systems, robotics.

Hongqiang Zhang, 1979, Yanjin City, Henan Province, Doctor, lecture, Master supervisor, Research direction: swarm robotics, swarm intelligence optimization algorithm.

Lianghong Wu, 1977, Changsha City, Hunan Province, Doctor, professor, doctoral supervisor, Researcher direction: swarm intelligence optimization algorithm, Intelligent scheduling.

Mao Wang, 1996, Xiangtan City, Hunan Province, Master’s degree, research direction: swarm robot system, swarm UAV system, swarm intelligence optimization algorithm.

Xi Wang, 1989, Doctor, research direction: robots, UAVs, swarm agents, artificial intelligence.

Zhaohua Liu, 1983, Doctor, professor, doctoral supervisor, Researcher direction: Intelligent control and system security of wind power equipment, artificial intelligence and information processing, fault diagnosis and fault-tolerant control.

Lei Chen, 1986, Meishan city, Sichuan Province, Doctor, lecture, Master supervisor, Researcher direction: date mining, web mining, graph mining, cloud computing, and big data scheduling and analysis.

Funding

This work is supported by the National Natural Science Foundation of China (62271199,52104192, 62103143, 61603132, 61672226, 61972443), the National Natural Science Foundation of Hunan Province (2021JJ30280), and the Science and Technology Innovation Program of Hunan Province (2017XK2302). It is also sponsored by the Postgraduate Scientific Research Innovation Project of Hunan Province (CX20210999), the Outstanding Youth Project of Education Department of Hunan Province of China (19B200) and the National Defense Basic Research Program of China (JCKY2019403D006). All these supports are highly appreciated.

Author information

Authors and Affiliations

  1. College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China

    Xiaohui Bian, Shaowu Zhou, Hongqiang Zhang, Lianghong Wu, Mao Wang, Xi Wang, Zhaohua Liu & Lei Chen

Authors
  1. Xiaohui Bian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaowu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lianghong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhaohua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongqiang Zhang.

Ethics declarations

Ethical Approval

The article has the approval of all the authors.

Consent to Participate

All the authors gave their consent to participate in this article.

Consent to Publish

The authors gave their authorization for the publishing of this article.

Competing Interests

There is no conflict of interest or competing interest.

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

Bian, X., Zhou, S., Zhang, H. et al. Swarm Robot Multitarget Search Strategy Based on Triangular Cones in a Complex Dynamic Nonconvex Obstacle Environment. J Intell Robot Syst 108, 80 (2023). https://doi.org/10.1007/s10846-023-01929-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-023-01929-9

Keywords

Search

Navigation