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Motion Control of a Flock of 1-Trailer Robots with Swarm Avoidance

Published online by Cambridge University Press:  17 February 2021

Jai Raj Open the ORCID record for Jai Raj [Opens in a new window] ,
Krishna Raghuwaiya ,
Bibhya Sharma Open the ORCID record for Bibhya Sharma [Opens in a new window]  and
Jito Vanualailai
Jai Raj*
Affiliation:
School of Computing, Information & Mathematical Sciences, The University of the South Pacific, Suva, Fiji, E-mails: [email protected], [email protected]
Krishna Raghuwaiya
Affiliation:
School of Education, The University of the South Pacific, Suva, Fiji, E-mail: [email protected]
Bibhya Sharma
Affiliation:
School of Education, The University of the South Pacific, Suva, Fiji, E-mail: [email protected]
Jito Vanualailai
Affiliation:
School of Computing, Information & Mathematical Sciences, The University of the South Pacific, Suva, Fiji, E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper addresses the motion planning and control problem of a system of 1-trailer robots navigating a dynamic environment cluttered with obstacles including a swarm of boids. A set of nonlinear continuous control laws is proposed via the Lyapunov-based Control Scheme for collision, obstacle, and swarm avoidances. Additionally, a leader–follower strategy is utilized to allow the flock to split and rejoin when approaching obstacles. The effectiveness of the control laws is demonstrated through numerical simulations, which show the split and rejoin maneuvers by the flock when avoiding obstacles while the swarm exhibits emergent behaviors.

Keywords

Tractor–trailerCollision avoidanceUnmanned vehiclesSwarmMotion controlLyapunov-based Control Scheme
Type
Article
Information
Robotica , Volume 39 , Issue 11 , November 2021 , pp. 1926 - 1951
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Raghuwaiya, K., Sharma, B. and Vanualailai, J., “Leader-follower based locally rigid formation control,” J. Adv. Transp. 2018, 14 (2018), Article ID 5278565. doi: 10.1155/2018/5278565.Google Scholar
Sharma, B., Vanualailai, J. and Prasad, A., “A dø-strategy: Facilitating dual-formation control of a virtually connected team,” J. Adv. Transp. 2017, 17 (2017), Article ID 9213805. doi: 10.1155/2017/9213805.Google Scholar
Lacasa, L., Luque, B., Ballesteros, F., Luque, J. and Carlos Nuño, J., “From time series to complex networks: The visibility graph,” Proc. National Acad. Sci. 105(13), 4972–4975 (2008). doi: 10.1073/pnas.0709247105.CrossRefGoogle ScholarPubMed
Siegwart, R., Nourbakhsh, I. R. and Scaramuzza, D., Introduction to Autonomous Mobile Robots, 2nd edn. (The MIT Press, 2011). ISBN 0262015358, 9780262015356.Google Scholar
Lingelbach, F., “Path Planning Using Probabilistic Cell Decomposition,” IEEE International Conference on Robotics and Automation, vol. 1, New Orleans, LA (2004) pp. 467–472. doi: 10.1109/ROBOT.2004.1307193.CrossRefGoogle Scholar
Niederberger, C., Radovic, D. and Gross, M., “Generic Path Planning for Real-Time Application,” Proceedings of Computer Graphics International (2004) pp. 299–306. doi: 10.1109/CGI.2004.1309225.CrossRefGoogle Scholar
Khatib, O., “Real time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 7(1), 90–98 (1986).CrossRefGoogle Scholar
Sharma, B., Vanualailai, J. and Singh, S., “Tunnel passing maneuvers of prescribed formations,” Int. J. Robust Nonlinear Control 24(5), 876–901 (2014).CrossRefGoogle Scholar
Sharma, B. N., Raj, J. and Vanualailai, J., “Navigation of carlike robots in an extended dynamic environment with swarm avoidance,” Int. J. Robust Nonlinear Control 28(2), 678–698 (2018).CrossRefGoogle Scholar
Prasad, A., Sharma, B., Vanualailai, J. and Kumar, S. A., “A geometric approach to target convergence and obstacle avoidance of a nonstandard tractor-trailer robot,” Int. J. Robust Nonlinear Control 30(13), 4924–4943 (2020).CrossRefGoogle Scholar
Raj, J., Raghuwaiya, K. and Vanualailai, J., “Collision avoidance of 3D rectangular planes by multiple cooperating autonomous agents,” J. Adv. Transp. 2020, 13 (2020), Article ID 4723687. doi: 10.1155/2020/4723687.Google Scholar
Raj, J., Raghuwaiya, K. S. and Vanualailai, J., “Novel lyapunov-based autonomous controllers for quadrotors,” IEEE Access 8(1), 47393–47406 (2020).CrossRefGoogle Scholar
Xie, L., Stol, K. and Xu, W., “Energy-optimal motion trajectory of an omni-directional mecanum-wheeled robot via polynomial functions,” Robotica 38(8), 1400–1414 (2020).CrossRefGoogle Scholar
Arslan, O. and Koditschek, D. E., “Sensor-based reactive navigation in unknown convex sphere worlds,” Int. J. Robot. Res. 38(2–3)196–223 (2019).CrossRefGoogle Scholar
Della Corte, B., Andreasson, H., Stoyanov, T. and Grisetti, G., “Unified motion-based calibration of mobile multi-sensor platforms with time delay estimation,” IEEE Robot. Autom. Lett. 4(2), 902–909 (2019).CrossRefGoogle Scholar
Zhang, Y. and Valavanis, K. P., “Sensor-based 2-d potential panel method for robot motion planning,” Robotica 1(1), 81–89 (1996).CrossRefGoogle Scholar
Hoy, M., Matveev, A. S. and Savkin, A. V., “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey,” Robotica 33(3), 463–497 (2015).CrossRefGoogle Scholar
Shojaei, K., “Neural network formation control of a team of tractor-trailer systems,” Robotica 36(1), 39–56 (2018).CrossRefGoogle Scholar
Raj, J., Raghuwaiya, K., Vanualailai, J. and Sharma, B., “Navigation of Car-Like Robots in Three-Dimensional Space,” Proceedings of the 2018 5th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (2018) pp. 271–275.Google Scholar
Siciliano, B. and Khatib, O., Springer Handbook of Robotics, 2nd edn. (Springer Publishing Company, Incorporated, 2016).CrossRefGoogle Scholar
Matveev, A. S., Savkin, A. V., Hoy, M. and Wang, C., “3 - Survey of Algorithms for Safe Navigation of Mobile Robots in Complex Environments,” In: Safe Robot Navigation Among Moving and Steady Obstacles (Matveev, A. S., Savkin, A. V., Hoy, M. and Wang, C., eds.) (Butterworth-Heinemann, 2016) pp. 21–49.CrossRefGoogle Scholar
Latombe, J.-C., Robot Motion Planning (Kluwer Academic Publishers, USA, 1991).CrossRefGoogle Scholar
Xu, D., Zhang, X., Zhu, Z., Chen, C. and Yang, P., “Behavior-based formation control of swarm robots,” Math. Problems Eng. 2014, 13 (2014), Article ID 205759. doi: 10.1155/2014/205759.CrossRefGoogle Scholar
Do, K. D., “Formation tracking control of unicycle-type mobile robots with limited sensing ranges,” IEEE Trans. Control Syst. Tech. 16(3), 527–538 (2008).CrossRefGoogle Scholar
Raghuwaiya, K. and Singh, S., “Formation types of multiple steerable 1-trailer mobile robots via split/rejoin maneuvers,” New Zealand J. Math. 43, 7–21 (2013).Google Scholar
Sharma, B., Singh, S., Vanualailai, J. and Prasad, A., “Globally rigid formation of n-link doubly nonholonomic mobile manipulators,” Robot. Auto. Syst. 105, 69–84 (2018).CrossRefGoogle Scholar
Yang, Z., Zhang, Q. and Chen, Z., “Formation control of multi-agent systems with region constraint,” Complexity 2019, 6 (2019), Article ID 8481060. doi: 10.1155/2019/8481060.Google Scholar
Toyota, R. and Namerikawa, T., “Formation Control of Multi-Agent System Considering Obstacle Avoidance,” 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) (2017) pp. 446–451.Google Scholar
Raj, J., Raghuwaiya, K., Vanualailai, J. and Sharma, B., “Path Planning of Multiple Mobile Robots in a Dynamic 3d environment,” In: Advances in Computer, Communication and Computational Sciences (Bhatia, S. K., Tiwari, S., Ruidan, S., Trivedi, M. C. and Mishra, K. K., eds.) (Springer, Singapore, 2021) pp. 209–219.CrossRefGoogle Scholar
Yi, X., Wei, J., Dimarogonas, D. V and Johansson, K. H., “Formation control for multi-agent systems with connectivity preservation and event-triggered controllers**this work was supported by the knut and alice wallenberg foundation, the swedish foundation for strategic research, and the swedish research council,” IFAC-PapersOnLine 50(1), 9367–9373 (2017). 20th IFAC World Congress.CrossRefGoogle Scholar
Raghuwaiya, K., Vanualailai, J. and Raj, J., “3D Cylindrical Obstacle Avoidance Using the Minimum Distance Technique,” In: Advances in Computer, Communication and Computational Sciences (Bhatia, S. K., Tiwari, S., Ruidan, S., Trivedi, M. C. and Mishra, K. K., eds.) (Springer, Singapore, 2021) pp. 199–208.CrossRefGoogle Scholar
Vanualailai, J., Raj, J. and Raghuwaiya, K., “Autonomous Quadrotor Maneuvers in a 3D Complex Environment,” In: Advances in Computer, Communication and Computational Sciences (Bhatia, S. K., Tiwari, S., Ruidan, S., Trivedi, M. C. and Mishra, K. K., eds.) (Springer, Singapore, 2021) pp. 221–231.CrossRefGoogle Scholar
Raj, J., Raghuwaiya, K., Singh, S., Sharma, B. and Vanualailai, J., “Swarming Intelligence of 1-Trailer Systems,” In: Advanced Computer and Communication Engineering Technology (Sulaiman, H. A., Othman, M. A., Othman, M. F. I., Rahim, Y. A. and Pee, N. C., eds.) (Springer International Publishing, Cham, 2016) pp. 251–264.Google Scholar
de Saxe, C. and Cebon, D., “Estimation of trailer off-tracking using visual odometry,” Vehicle Syst. Dyn. 57(5), 752–776 (2019).CrossRefGoogle Scholar
Prasad, A., Sharma, B. and Vanualailai, J., “A Geometric Approach to Motion Control of a Standard Tractor-Trailer Robot,” 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (2016) pp. 53–59.Google Scholar
Michalek, M., “Non-minimum-phase property of n-trailer kinematics resulting from off-axle interconnections,” Int. J. Control 86(4), 740–758 (2013).CrossRefGoogle Scholar
Divelbiss, A. W. and Wen, J., “A path space approach to nonholonomic motion planning in the presence of obstacles,” IEEE Trans. Robot. Autom. 13(3), 443–451 (1997).CrossRefGoogle Scholar
Bolzern, P., DeSantis, R. M. and Locatelli, A., “An input-output linearisation approach to the control of an n body articulated vehicle,” J. Dyn. Syst. Meas. Control 123(3), 309–316 (2001).CrossRefGoogle Scholar
Lee, M. K. J.-H., Chung, W. and Song, J., “A passive multiple trailer system with off-axle hitching,” Int. J. Control Autom. Syst. 2(3), 289–297 (2004).Google Scholar
Kayacan, E., Ramon, H. and Saeys, W., “Robust trajectory tracking error model-based predictive control for unmanned ground vehicles,” IEEE/ASME Trans. Mechatron. 21(2), 806–814 (2016).CrossRefGoogle Scholar
Ding, X., He, Y., Ren, J. and Sun, T., “A Comparative Study of Control Algorithms for Active Trailer Steering Systems of Articulated Heavy Vehicles,” Proceedings of the American Control Conference Montreal, Canada (2012) pp. 3617–3622.Google Scholar
Astolfi, A., Bolzern, P. and Locatelli, A., “Path-tracking of a tractor-trailer vehicle along rectilinear and circular paths: A Lyapunov-based approach,” IEEE Trans. Robot. Autom. 20(1), 154–160 (2004).CrossRefGoogle Scholar
Elhaki, O. and Shojaei, K., “Observer-based neural adaptive control of a platoon of autonomous tractor-trailer vehicles with uncertain dynamics,” IET Control Theory Appl. 14(14), 1898–1911 (2020).Google Scholar
Liu, J., Dong, X., Wang, J., Ljungqvist, O., Lu, C., Zhao, X. and Wang, X., “A novel EPT autonomous motion control framework for an off-axle hitching tractor-trailer system with drawbar,” IEEE Trans. Intell. Vehicles, (2020). doi: 10.1109/TIV.2020.3033115.CrossRefGoogle Scholar
Guevara, L., Michałek, M. M. and Auat Cheein, F., “Headland turning algorithmization for autonomous n-trailer vehicles in agricultural scenarios,” Comput. Electron. Agricul. 175, 105541 (2020).CrossRefGoogle Scholar
Prasad, A., Sharma, B. and Vanualailai, J., “A solution to the motion planning and control problem of a car-like robot via a single-layer perceptron,” Robotica 32(6), 935–952 (2014).CrossRefGoogle Scholar
Prasad, A., Sharma, B. and Vanualailai, J., “A new stabilizing solution for motion planning and control of multiple robots,” Robotica 34(5), 1071–1089 (2016).CrossRefGoogle Scholar
Esfandyari, M., Fanaei, M. A. and Zohreie, H., “Adaptive fuzzy tuning of PID controllers,” Neural Comput. Appl. 23(1), 19–28 (2013).CrossRefGoogle Scholar
Sharma, B., Vanualailai, J., Raghuwaiya, K. and Prasad, A., “New potential field functions for motion planning and posture control of 1-trailer systems,” Int. J. Math. Comput. Sci. 3(1), 45–71 (2008).Google Scholar
Reynolds, C. W., “Flocks, Herds, and Schools: A Distributed Behavioral Model, in Computer Graphics,” Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, New York, USA, vol. 21(4) (1987) pp. 25–34.Google Scholar
Mogilner, A., Edelstein-Keshet, L., Bent, L. and Spiros, A., “Mutual interactions, potentials, and individual distance in a social aggregation,” J. Math. Biol. 47(4), 352–389 (2003).CrossRefGoogle Scholar
Gazi, V. and Passino, K. M., “A class of attractions/repulsion functions for stable swarm aggregations,” Int. J. Control 77(18), 1567–1579 (2004).CrossRefGoogle Scholar
Reynolds, C. W., “Steering Behaviors for Autonomous Characters,” Proceedings of Game Developers Conference (Miller Freeman Game Group, San Francisco, California, USA, 1999) pp. 763–782.Google Scholar
Vanualailai, J., “Stable emergent formations for a swarm of autonomous car-like vehicles,” Int. J. Adv. Robot. Syst. 16(5), 1–17 (2019).CrossRefGoogle Scholar
Forgoston, E. and Schwartz, I. B., “Delay-induced instabilities in self-propelling swarms,” Phys. Rev. E 77(3), 035203 (2008).CrossRefGoogle ScholarPubMed
Wagdy, A. and Khamis, A., “Adaptive group formation in multirobot systems,” Adv. Artif. Intell. 2013, 15 (2013), Article ID 692658. doi: 10.1155/2013/692658.Google Scholar