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A novel cooperative path planning method based on UCR-FCE and behavior regulation for large-scale multi-robot system

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Abstract

Multi-robot cooperative path planning is a significant research area in the domains of intelligent reconnaissance, transportation, and combat. The complexity of resolving multi-path conflicts in large-scale multi-robot scenarios poses a significant challenge to researchers. To address this issue, this paper proposed a universal conflict resolution mode, collision avoidance strategy in local crossing, and behavior regulation method that allows robots to take intelligent measures to avoid conflicts in scenarios with a large number of robots. Specifically, we introduced a novel algorithm, Universal Conflict Resolution and Free Crossing Emergence (UCR-FCE), that solves the conflict problem emerging in a significant number of local areas. The algorithm includes three extended multi-path resolution algorithms and a mechanism of avoiding Receptor Dodger (RD) from Noumenon Dodger (ND) to the free junction. We provided a completeness proof with Set Theory and Regional Theory to demonstrate that UCR-FCE can solve all conflict scenarios given sufficient free path nodes. Furthermore, a behavior regulation algorithm was developed to reduce the complexity of real-time path conflicts during robot motion. The proposed multi-robot cooperative intelligent planning algorithm is tested through simulation and field experiments. Results illustrate that the system can effectively refer to the traffic rules and intelligently adapt to ever-changing potential conflicts. A comparative simulation is also established to prove the effectiveness of each improvement proposed in this paper and to exhibit the superiority of the proposed method over other methods available in the literature. Results indicate that the proposed method outperforms eight comparative methods, with an absolute increase in the success planning rate of 56\(\%\), 56\(\%\), 44\(\%\), 24\(\%\), 12\(\%\), 22\(\%\) and 18\(\%\) in large-scale multi-robot scenarios, respectively, when the number of robots in ROS-stage simulation environment reaches 400.

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Data Availibility Statement

All data, models generated or used during the study are available from the corresponding author by request.

References

  1. Niu J, Wang Z, Zhang P (2019) Path planning based on optimized ant colony algorithm in multi-machine cooperative operations. J North Univ China (Nat Sci Ed) 40(2):127–142

    Google Scholar 

  2. Hou M, Yang Y, Jing X (2017) Weapon target assignment modeling of multi-uav. Tactical Missile Technology 5:68–73

    Google Scholar 

  3. Yang Y, Xu L, Ai Z (2017) Multi-agent-based simulation of area detection and cooperative combat for unmanned aerial vehicles cluster. Inf Technol 12:161–167

    Google Scholar 

  4. Bennewitz M, Burgard W, Thrun S (2001) Optimizing schedules for prioritized path planning of multi-robot systems. In: Proceedings 2001 ICRA. IEEE international conference on robotics and automation (Cat. No. 01CH37164), IEEE, pp 271–276

  5. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on systems science and cybernetics 4(2):100–107

    Article  Google Scholar 

  6. Guo Y, Parker LE (2002) A distributed and optimal motion planning approach for multiple mobile robots. In: Proceedings 2002 IEEE international conference on robotics and automation (Cat. No. 02CH37292), IEEE, pp 2612–2619

  7. Stentz A (1994) Optimal and efficient path planning for partially-known environments. In: Proceedings of the 1994 IEEE international conference on robotics and automation, IEEE, pp 3310–3317

  8. Li H, Zhao T, Dian S (2022) Prioritized planning algorithm for multi-robot collision avoidance based on artificial untraversable vertex. Appl Intell 52(1):429–451

    Article  Google Scholar 

  9. Serpen G, Dou C (2015) Automated robotic parking systems: real-time, concurrent and multi-robot path planning in dynamic environments. Appl Intell 42:231–251

    Article  Google Scholar 

  10. Okumura K, Tamura Y, Défago X (2021) Iterative refinement for real-time multi-robot path planning. In: 2021 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 9690–9697

  11. Tanner HG, Kumar A (2005) Towards decentralization of multi-robot navigation functions. In: Proceedings of the 2005 IEEE international conference on robotics and automation, IEEE, pp 4132–4137

  12. Wei C, Hindriks KV, Jonker CM (2016) Altruistic coordination for multi-robot cooperative pathfinding. Appl Intell 44:269–281

    Article  Google Scholar 

  13. Russell SJ (2010) Artificial intelligence a modern approach. Pearson Education, Inc

  14. Wurm KM, Stachniss C, Burgard W (2008) Coordinated multi-robot exploration using a segmentation of the environment. In: 2008 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 1160–1165

  15. Thrun S (1998) Learning metric-topological maps for indoor mobile robot navigation. Artif Intell 99(1):21–71

    Article  Google Scholar 

  16. Kavraki LE, Kolountzakis MN, Latombe JC (1998) Analysis of probabilistic roadmaps for path planning. IEEE transactions on robotics and automation 14(1):166–171

    Article  Google Scholar 

  17. Sanchez G, Latombe JC (2002) Using a prm planner to compare centralized and decoupled planning for multi-robot systems. In: Proceedings 2002 IEEE international conference on robotics and automation (Cat. No. 02CH37292), IEEE, pp 2112–2119

  18. Saha M, Isto P (2006) Multi-robot motion planning by incremental coordination. In: 2006 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 5960–5963

  19. LaValle SM et al (1998) Rapidly-exploring random trees: A new tool for path planning

  20. Bruce J, Veloso M (2002) Real-time randomized path planning for robot navigation. In: IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 2383–2388

  21. Wagner G, Kang M, Choset H (2012) Probabilistic path planning for multiple robots with subdimensional expansion. In: 2012 IEEE international conference on robotics and automation, IEEE, pp 2886–2892

  22. Li J, Deng G, Luo C et al (2016) A hybrid path planning method in unmanned air/ground vehicle (uav/ugv) cooperative systems. IEEE transactions on vehicular technology 65(12):9585–9596

    Article  Google Scholar 

  23. Wu Y, Low KH, Lv C (2020) Cooperative path planning for heterogeneous unmanned vehicles in a search-and-track mission aiming at an underwater target. IEEE transactions on vehicular technology 69(6):6782–6787

    Article  Google Scholar 

  24. Wang N, Xu H (2020) Dynamics-constrained global-local hybrid path planning of an autonomous surface vehicle. IEEE transactions on vehicular technology 69(7):6928–6942

    Article  Google Scholar 

  25. Ji J, Khajepour A, Melek WW et al (2016) Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints. IEEE transactions on vehicular technology 66(2):952–964

    Article  Google Scholar 

  26. Wu Y, Low KH, Pang B et al (2021) Swarm-based 4d path planning for drone operations in urban environments. IEEE transactions on vehicular technology 70(8):7464–7479

    Article  Google Scholar 

  27. Washington D (1966) Programming languages, nato ad-vanced study institute, villard-de-lans, france. september 28-30, 1966-workshop on de-sign automation, applications in design

  28. Binder B, Beck F, König F et al (2019) Multi robot route planning (mrrp): Extended spatial-temporal prioritized planning. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 4133–4139

  29. Jager M, Nebel B (2001) Decentralized collision avoidance, deadlock detection, and deadlock resolution for multiple mobile robots. In: Proceedings 2001 IEEE/RSJ international conference on intelligent robots and systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180), IEEE, pp 1213–1219

  30. Marcolino LS, Chaimowicz L (2009a) Traffic control for a swarm of robots: Avoiding group conflicts. In: 2009 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 1949–1954

  31. Marcolino LS, Chaimowicz L (2009b) Traffic control for a swarm of robots: Avoiding target congestion. In: 2009 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 1955–1961

  32. Luna R, Bekris KE (2011) Efficient and complete centralized multi-robot path planning. In: 2011 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 3268–3275

  33. Koenig S, Likhachev M (2005) Fast replanning for navigation in unknown terrain. IEEE transactions on robotics 21(3):354–363

    Article  Google Scholar 

  34. Barer M, Sharon G, Stern R et al (2014) Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem. In: Proceedings of the international symposium on combinatorial Search, pp 19–27

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Funding

This work was supported by Advanced Jet Propulsion Creativity Center, AEAC (Project ID. HKCX2020-02-019).

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Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Friendship West Road, Xi’an, 710072, Shannxi, China

    Zeyu Zhou, Wei Tang, Jingxi Zhang & Xiongwei Wu

  2. School of Power and Energy, Northwestern Polytechnical University, Friendship West Road, Xi’an, 710072, Shannxi, China

    Mingyang Li

Authors
  1. Zeyu Zhou
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  2. Wei Tang
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  3. Mingyang Li
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  4. Jingxi Zhang
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  5. Xiongwei Wu
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Contributions

Conceptualization, Z.Z.; methodology, Z.Z. and W.T.; software, Z.Z.; validation, Z.Z. and M.L.; formal analysis, Z.Z. and W.T.; investigation, Z.Z. and M.L.; data curation, J.Z.; writing and original draft preparation, Z.Z.; writing-review and editing, M.L.; visualization, X.W.; supervision, W.T.; project administration, W.T.; funding acquisition, W.T. All authors have read and agreed to the current version of the manuscript.

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Correspondence to Wei Tang.

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Zhou, Z., Tang, W., Li, M. et al. A novel cooperative path planning method based on UCR-FCE and behavior regulation for large-scale multi-robot system. Appl Intell 53, 30706–30745 (2023). https://doi.org/10.1007/s10489-023-05152-9

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