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Mobile robots path planning and mobile multirobots control: A review

Published online by Cambridge University Press:  24 June 2022

Bassem Hichri Open the ORCID record for Bassem Hichri [Opens in a new window] ,
Abir Gallala ,
Francesco Giovannini  and
Slawomir Kedziora
Bassem Hichri*
Affiliation:
Guala Closures Group – University of Luxembourg, Department of Engineering, Foetz3895, Luxembourg
Abir Gallala
Affiliation:
University of Luxembourg, Department of Engineering, Luxembourg 1359, Luxembourg
Francesco Giovannini
Affiliation:
Guala Closures Group, Foetz3895, Luxembourg
Slawomir Kedziora
Affiliation:
University of Luxembourg, Department of Engineering, Luxembourg 1359, Luxembourg
*
*Corresponding author. E-mail: [email protected]
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Abstract

Mobile robots and multimobile robotic system usage for task achievement have been an emerging research area since the last decades. This article presents a review about mobile robot navigation problem and multimobile robotic systems control. The main focus is made on path planning strategies and algorithms in static and dynamic environments. A classification on mobile robots path planning has been defined in the literature and divided to classical and heuristic approaches. Each of them has its own advantages and drawbacks. On the other hand, the control of multimobile robots is presented and the control approaches for a fleet of robots are presented. Scientists found that using more than one robot as opposed to a single one presents many advantages when considering redundant task, dangerous tasks, or a task that scales up or down in time or that requires flexibility. They have defined three main approaches of multiple robots control: behavior-based approach, leader–follower approach, and virtual structure approach. This article addresses these approaches and provides examples from the literature.

Keywords

mobile robotsmultirobot systemcontrol architecturepath planningclassical and Heuristic approachesbehavior-based approachleader–follower approach and virtual structure approach
Type
Review Article
Information
Robotica , Volume 40 , Issue 12 , December 2022 , pp. 4257 - 4270
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

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. Nonlin. 30(13), 4924–4943 (2020).Google Scholar
Raghuwaiya, K., Sharma, B. and Vanualailai, J., “Leader-follower based locally rigid formation control,” J. Adv. Transport. 2018(5), 1–14 (2018).CrossRefGoogle Scholar
Dönmez, E., Kocamaz, A. F. and Dirik, M., “A vision-based real-time mobile robot controller design based on gaussian function for indoor environment,” Arab. J. Sci. Eng. 43(12), 7127–7142 (2018).Google Scholar
Tuba, E., Strumberger, I., Zivkovic, D., Bacanin, N. and Tuba, M., “Mobile Robot Path PLanning By Improved Brain Storm Optimization Algorithm,” In: 2018 IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2018) pp. 1–8.Google Scholar
Ehlert, P., The use of artificial intelligence robots, Report on research project (1999).Google Scholar
Patle, B., G.Babu, L., Pandey, A., Parhi, D. and Jagadeesh, A., “A review: On path planning strategies for navigation of mobile robot,” Defence Technol. 15(4), 582–606 (2019).CrossRefGoogle Scholar
Zafar, M. N. and Mohanta, J., “Methodology for path planning and optimization of mobile robots: A review,” Proc. Comput. Sci. 133(3), 141–152 (2018).CrossRefGoogle Scholar
Conte, G. and Zulli, R., “Hierarchical path planning in a multi-robot environment with a simple navigation function,” IEEE Trans. Syst. Man Cybernet. 25(4), 651–654 (1995).CrossRefGoogle Scholar
Choset, H. and Burdick, J., “Sensor-based exploration: The hierarchical generalized voronoi graph,” Int. J. Robot. Res. 19(2), 96–125 (2000).CrossRefGoogle Scholar
Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5(1), 90–98 (1986).CrossRefGoogle Scholar
Chen, P., Hwang, Y. and Sandros, A., “Motion Planner with Performance Proportional to Task Difficulty,” In: IEEE International Conference on Robotics and Automation , France (1992).Google Scholar
Janglová, D., “Neural networks in mobile robot motion,” Int. J. Adv. Robot. Syst. 1(1), 2 (2004).CrossRefGoogle Scholar
Bremermann, H. J.. The Evolution of Intelligence: The Nervous System as A Model of Its Environment (University of Washington, Department of Mathematics, 1958).Google Scholar
Eberhart, R. and Kennedy, J., “A New Optimiser Using Particle Swarm Theory in Proceedings of the Sixth International Symposium on Micro Machine and Human Science,” In: Proc. Sixth International Symposium on Micro Machine and Human Science (1995) pp. 39–43.Google Scholar
Hichri, B., Design and control of collaborative, cross and carry mobile robots: C3Bots. PhD thesis, Université Blaise Pascal-Clermont-Ferrand II (2015).Google Scholar
Hichri, B., Fauroux, J., Adouane, L., Doroftei, I. and Mezouar, Y., “Design of cooperative mobile robots for co-manipulation and transportation tasks,” Robot. Comput.-Intgr. Manuf. 57(3), 412–421 (2019).CrossRefGoogle Scholar
Hichri, B., Adouane, L., Fauroux, J.-C., Mezouar, Y. and Doroftei, I., “Flexible co-manipulation and transportation with mobile multi-robot system,” Assembly Autom. 39(3), 422–431 (2019).CrossRefGoogle Scholar
Adouane, L. and Fort-Piat, L., “Hybrid Behavioral Control Architecture for the Cooperation of Minimalist Mobile Robots,” In: 2004 IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04 (IEEE, vol. 4, 2004) pp. 3735–3740.Google Scholar
Abou-Samah, M. and Krovi, V., “Optimal Configuration Selection for A Cooperating System of Mobile Manipulators,” In: ASME, 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (American Society of Mechanical Engineers, 2002) pp. 1299–1306.CrossRefGoogle Scholar
Kernbach, S., Meister, E., Schlachter, F., Jebens, K., Szymanski, M., Liedke, J., Laneri, D., Winkler, L., Schmickl, T., Thenius, R., et al., “Symbiotic Robot Organisms: Replicator and Symbrion Projects,” In: Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems (ACM, 2008) pp. 62–69.Google Scholar
Arai, T., Pagello, E. and Parker, L. E., “Editorial: Advances in multi-robot systems,” IEEE Trans. Robotic. Autom. 18(5), 655–661 (2002).Google Scholar
Simonin, O., Liégeois, A. and Rongier, P., “An Architecture for Reactive Cooperation of Mobile Distributed Robots,” In: Distributed Autonomous Robotic Systems 4 (Springer, 2000) pp. 35–44.CrossRefGoogle Scholar
Lucidarme, P., Simonin, O. and et al., “Implementation and Evaluation of A satisfaction/altruism Based Architecture for Multi-Robot Systems,” In: Proceedings of IEEE International Conference on Robotics and Automation, ICRA 2002., (IEEE, vol. 1, 2002) pp. 1007–1012.Google Scholar
Sadowska, A. D., Formation control of nonholonomic mobile robots: the virtual structure approach. PhD thesis, Queen Mary University of London (2012).Google Scholar
Guechi, E.-H., Suivi de trajectoires d’un robot mobile non holonome: approche par modéle flou de Takagi-Sugeno et prise en compte des retards. PhD thesis, Université de Valenciennes et du Hainaut-Cambresis (2010).Google Scholar
Adouane, L., Architectures de contrôle comportementales et réactives pour la coopération d’un groupe de robots mobiles. PhD thesis, Université de Franche-Comté (2005).Google Scholar
Kanjanawanishkul, K., “Formation control of mobile robots: Survey,” In: Eng. Ubu. Ac. Th. (2005) pp. 50–64.Google Scholar
Raghuwaiya, K. S., Singh, S. and Vanualailai, J., “Formation control of mobile robots,” Int. J. Mech. Mechatron. Eng. 5(12), 2697–2702 (2011).Google Scholar
van den Broek, T. H., van de Wouw, N. and Nijmeijer, H., “Formation Control of Unicycle Mobile Robots: A Virtual Structure Approach,” In: Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009 (IEEE, 2009) pp. 8328–8333.Google Scholar
Sadowska, A., van den Broek, T., Huijberts, H., van de Wouw, N., Kostić, D. and Nijmeijer, H., “A virtual structure approach to formation control of unicycle mobile robots using mutual coupling,” Int. J. Cont. 84(11), 1886–1902 (2011).CrossRefGoogle Scholar
Benzerrouk, A., Adouane, L., Lequievre, L. and Martinet, P., “Navigation of Multi-Robot Formation in Unstructured Environment Using Dynamical Virtual Structures,” In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2010) pp. 5589–5594.Google Scholar
Dehghani, M. A., Menhaj, M. B. and Ghaderi, H., “A hardware in the loop simulation testbed for vision-based leader-follower formation flight,” Mechatronics 47, 223–232 (2017).Google Scholar
Das, B., Subudhi, B. and Pati, B. B., “Employing nonlinear observer for formation control of AUVs under communication constraints,” International Journal of Intelligent Unmanned Systems 3(2/3), 122–155 (2015).Google Scholar
Peng, Z., Formation Control of Multiple Nonholonomic Wheeled Mobile Robots. PhD thesis, Ecole Centrale de Lille, (2013).Google Scholar
Okumuş, F., Dönmez, E. and Kocamaz, A. F., “A cloudware architecture for collaboration of multiple agvs in indoor logistics: Case study in fabric manufacturing enterprises,” Electronics 9(12), 2023(2020).Google Scholar
Siegwart, R., Nourbakhsh, I. R. and Scaramuzza, D.. Introduction to Autonomous Mobile Robots (MIT Press,2011).Google Scholar
Lulu, L. and Elnagar, A., “A Comparative Study Between Visibility-Based Roadmap Path PLanning Algorithms,” In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2005) pp. 3263–3268.CrossRefGoogle Scholar
Choset, H. M., Lynch, K. M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L., Thrun, S. and Arkin, R. C., Principles of Robot Motion: Theory, Algorithms, and Implementation (MIT Press, 2005).Google Scholar
Takahashi, O. and Schilling, R. J., “Motion planning in a plane using generalized voronoi diagrams,” IEEE Trans. Robot. Autom. 5(2), 143–150 (1989).CrossRefGoogle Scholar
Ó’Dúnlaing, C. and Yap, C. K., “A “retraction” method for planning the motion of a disc,” J. Algor. 6(1), 104–111 (1985).CrossRefGoogle Scholar
Noborio, H., Naniwa, T. and Arimoto, S., “A quadtree-based path-planning algorithm for a mobile robot,” J. Robot. Syst. 7(4), 555–574 (1990).Google Scholar
Šeda, M., “Roadmap Methods vs. Cell Decomposition in Robot Motion Planning,” In: Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation (2007) pp. 127–132.Google Scholar
Regli, W., Robot Lab: Robot Path Planning. Lectures Notes of Department of Computer Science (Drexel University, 2007).Google Scholar
Barbehenn, M. and Hutchinson, S., “Toward an Exact Incremental Geometric Robot Motion PLanner,” In: Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots (IEEE, vol. 3, 1995) pp. 39–44.Google Scholar
Zhu, D. and Latombe, J.-C., New heuristic algorithms for efficient hierarchical path planning, Tech. Rep., Stanford Univ, CA, Dept of Computer Science, (1989).Google Scholar
Lingelbach, F., “Path PLanning Using Probabilistic Cell Decomposition,” In: Proceedings of the IEEE International Conference on Robotics and Automation, ICRA’04 (IEEE, vol. 1, 2004) pp. 467–472 Google Scholar
Garibotto, G. and Masciangelo, S., “Path Planning Using the Potential Field Approach for Navigation,” In: Fifth International Conference on Advanced Robotics’ Robots in Unstructured Environments (IEEE, 1991) pp. 1679–1682.Google Scholar
Kim, J.-O. and Khosla, P., Real-Time Obstacle Avoidance Using Harmonic Potential Functions (1992).Google Scholar
Borenstein, J. and Koren, Y., “Real-time obstacle avoidance for fast mobile robots,” IEEE Trans. Syst. Man Cybern. 19(5), 1179–1187 (1989).CrossRefGoogle Scholar
Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential field method,” Auton. Robot. 13(3), 207–222 (2002).Google Scholar
Dönmez, E. and Kocamaz, A. F., “Design of mobile robot control infrastructure based on decision trees and adaptive potential area methods,” Iran. J. Sci. Technol. Trans. Electric. Eng. 44(1), 431–448 (2020).Google Scholar
Dijkstra, E. W. et al, “A note on two problems in connexion with graphs,” Numer. Math. 1(1), 269–271 (1959).Google Scholar
Holland, J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (MIT press, 1992).Google Scholar
Shibata, T. and Fukuda, T., “Intelligent Motion PLanning By Genetic Algorithm with Fuzzy Critic,” In: Proceedings of 8th IEEE International Symposium on Intelligent Control (IEEE, 1993) pp. 565–570.Google Scholar
Shing, M.-T. and Parker, G. B., “Genetic Algorithms for the Development of Real-Time Multi-Heuristic Search Strategies,” In: ICGA (Citeseer, 1993) pp. 565–572.Google Scholar
Xiao, J., Michalewicz, Z., Zhang, L. and Trojanowski, K., “Adaptive evolutionary planner/navigator for mobile robots,” IEEE Trans. Evol. Comput. 1(1), 18–28 (1997).Google Scholar
Kang, X., Yue, Y., Li, D. and Maple, C., “Genetic algorithm based solution to dead-end problems in robot navigation,” Int. J. Comput. Appl. Technol. 41(3-4), 177–184 (2011).Google Scholar
Shi, P. and Cui, Y., “Dynamic Path Planning for Mobile Robot Based on Genetic Algorithm in Unknown Environment,” In: 2010 Chinese Control and Decision Conference (IEEE, 2010) pp. 4325–4329.Google Scholar
Zadeh, L. A., Klir, G. J. and Yuan, B., Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers (World Scientific, 1996).Google Scholar
Raguraman, S., Tamilselvi, D. and Shivakumar, N., “Mobile Robot Navigation Using Fuzzy Logic Controller,” In: 2009 International Conference on Control, Automation, Communication and Energy Conservation (IEEE, 2009) pp. 1–5.Google Scholar
Omrane, H., Masmoudi, M. S. and Masmoudi, M., “Fuzzy logic based control for autonomous mobile robot navigation,” Comput. Intel. Neurosci. 2016(2), 1–10 (2016).CrossRefGoogle ScholarPubMed
Jinglun, L. Y. Y. and Yuancheng, S., “The path planning of mobile robot by neural networks and hierarchical reinforcement learning,” Front. Neurorobot. 14, 63 (2020).Google Scholar
Xang, X., Nature inspired meta-heuristic algorithm (2008).Google Scholar
Hidalgo-Paniagua, A., Vega-Rodríguez, M. A., Ferruz, J. and Pavón, N., “Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach,” Soft Comput. 21(4), 949–964 (2017).Google Scholar
Tang, X.-l, Li, L.-m and Jiang, B.-j, “Mobile robot slam method based on multi-agent particle swarm optimized particle filter,” J. China Univ. Posts Telecommun. 21(6), 78–86 (2014).CrossRefGoogle Scholar
Dorigo, M. and Gambardella, L. M., “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evol. Comput 1(1), 53–66 (1997).CrossRefGoogle Scholar
Guan-Zheng, T., Huan, H. and Sloman, A., “Ant colony system algorithm for real-time globally optimal path planning of mobile robots,” Acta Automat. Sin 33(3), 279–285 (2007).Google Scholar
Zhu, K. and Zhang, T., “Deep reinforcement learning based mobile robot navigation: A review,” Tsinghua Sci. Technol. 26(5), 674–691 (2021).CrossRefGoogle Scholar
Altuntaş, N., Imal, E., Emanet, N. and Öztürk, C. N., “Reinforcement learning-based mobile robot navigation,” Turkish J. Electric. Eng. Comput. Sci. 24(3), 1747–1767 (2016).Google Scholar
Xin, J., Zhao, H., Liu, D. and Li, M., “Application of Deep Reinforcement Learning in Mobile Robot Path PLanning,” In: 2017 Chinese Automation Congress (CAC) (2017) pp. 7112–7116.Google Scholar
Wang, B., Liu, Z., Li, Q. and Prorok, A., “Mobile robot path planning in dynamic environments through globally guided reinforcement learning,” IEEE Robot. Automat. Lett. 5(4), 6932–6939 (2020).Google Scholar
Surmann, H., Jestel, C., Marchel, R., Musberg, F., Elhadj, H. and Ardani, M., “Deep reinforcement learning for real autonomous mobile robot navigation in indoor environments,” CoRR, vol. abs/2005.13857 (2020).Google Scholar
Mitić, M. and Miljković, Z., “Bio-inspired approach to learning robot motion trajectories and visual control commands,” Exp. Syst. Appl. 42(5), 2624–2637 (2015).CrossRefGoogle Scholar
Argall, B. D., Chernova, S., Veloso, M. and Browning, B., “A survey of robot learning from demonstration,” Robot. Auton. Syst. 57(5), 469–483 (2009).CrossRefGoogle Scholar
Eusuff, M. M. and Lansey, K. E., “Optimization of water distribution network design using the shuffled frog leaping algorithm,” J. Water Res. Plan. Manage. 129(3), 210–225 (2003).Google Scholar
Ni, J., Yin, X., Chen, J. and Li, X., “An Improved Shuffled Frog Leaping Algorithm for Robot Path PLanning,” In: 2014 10th International Conference on Natural Computation (ICNC) (IEEE, 2014) pp. 545–549.Google Scholar
Hidalgo-Paniagua, A., Vega-Rodríguez, M. A., Ferruz, J. and Pavón, N., “MOSFLA-MRPP: Multi-objective shuffled frog-leaping algorithm applied to mobile robot path planning,” Eng. Appl. Artif. Intel. 44(7), 123–136 (2015).Google Scholar
Mohanty, P. K. and Parhi, D. R., “Optimal path planning for a mobile robot using cuckoo search algorithm,” J. Exp. Theor. Artif. Intell. 28(1-2), 35–52 (2016).CrossRefGoogle Scholar
Mohanty, P. K. and Parhi, D. R., “A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach,” Memet. Comput. 7(4), 255–273 (2015).CrossRefGoogle Scholar
Karaboga, D., An idea based on honey bee swarm for numerical optimization. Tech. Rep., Technical report-tr06, Erciyes university, engineering faculty, computer (2005).Google Scholar
Contreras-Cruz, M. A., Ayala-Ramirez, V. and Hernandez-Belmonte, U. H., “Mobile robot path planning using artificial bee colony and evolutionary programming,” Appl. Soft Comput. 30(1), 319–328 (2015).CrossRefGoogle Scholar
Saffari, M. and Mahjoob, M., “Bee Colony Algorithm for Real-Time Optimal Path PLanning of Mobile Robots,” In: 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control (IEEE, 2009) pp. 1–4.Google Scholar
Ma, Q. and Lei, X., “Dynamic Path PLanning of Mobile Robots Based on ABC Algorithm,” In: International conference on artificial intelligence and computational intelligence (Springer, 2010) pp. 267–274.Google Scholar
Mohanty, P. K. and Parhi, D. R., “A new efficient optimal path planner for mobile robot based on invasive weed optimization algorithm,” Front. Mech. Eng. 9(4), 317–330 (2014).CrossRefGoogle Scholar
Sengupta, A., Chakraborti, T., Konar, A. and Nagar, A., “Energy Efficient Trajectory PLanning By A Robot Arm Using Invasive Weed Optimization Technique,” In: 2011 Third World Congress on Nature and Biologically Inspired Computing (IEEE, 2011) pp. 311–316.CrossRefGoogle Scholar
Ghalenoei, M. R., Hajimirsadeghi, H. and Lucas, C., “Discrete Invasive Weed Optimization Algorithm: Application to Cooperative Multiple Task Assignment of Uavs,” In: Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference (IEEE, 2009) pp. 1665–1670.Google Scholar
Kundu, S. and Parhi, D. R., “Navigation of underwater robot based on dynamically adaptive harmony search algorithm,” Memet. Comput. 8(2), 125–146 (2016).CrossRefGoogle Scholar
Tangpattanakul, P., Meesomboon, A. and Artrit, P., “Optimal Trajectory of Robot Manipulator Using Harmony Search Algorithms,” In: Recent Advances in Harmony Search Algorithm (Springer, 2010) pp. 23–36.Google Scholar
Guo, J., Gao, Y. and Cui, G., “The path planning for mobile robot based on bat algorithm,” Int. J. Automat. Cont. 9(1), 50–60 (2015).Google Scholar
Ghosh, S., Panigrahi, P. K. and Parhi, D. R., “Analysis of FPA and BA meta-heuristic controllers for optimal path planning of mobile robot in cluttered environment,” IET Sci. Meas. Technol. 11(7), 817–828 (2017).CrossRefGoogle Scholar
Wang, G.-G., Chu, H. E. and Mirjalili, S., “Three-dimensional path planning for UCAV using an improved bat algorithm,” Aerosp. Sci. Technol. 49, 231–238 (2016).CrossRefGoogle Scholar
Chakraborty, J., Konar, A., Chakraborty, U. K. and Jain, L. C., “Distributed Cooperative Multi-Robot Path PLanning Using Differential Evolution,” In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) (IEEE, 2008) pp. 718–725.Google Scholar
Deyun, F. Q. Z., “Time optimal path planning based on differential evolution algorithm,” Comput. Eng. Appl. 12, 021 (2005).Google Scholar
Parhi, D. R. and Kundu, S., “Navigational control of underwater mobile robot using dynamic differential evolution approach,” Proc. Inst. Mech. Eng. M: J. Eng. Marit. Environ. 231(1), 284–301 (2017).Google Scholar
Savkin, A. V. and Wang, C., “A simple biologically inspired algorithm for collision-free navigation of a unicycle-like robot in dynamic environments with moving obstacles,” Robotica 31(6), 993–1001 (2013).CrossRefGoogle Scholar
Thanh, H. L. N. N., Phi, N. N. and Hong, S. K., “Simple nonlinear control of quadcopter for collision avoidance based on geometric approach in static environment,” Int. J. Adv. Robot. Syst. 15(2), 1729881418767575 (2018).CrossRefGoogle Scholar
Thanh, H. L. N. N. and Hong, S. K., “Completion of collision avoidance control algorithm for multicopters based on geometrical constraints,” IEEE Access 6, 27111–27126 (2018).Google Scholar
Beard, R. W., Lawton, J., Hadaegh, Y. F., et al., “A coordination architecture for spacecraft formation control,” IEEE Trans. Contr. Syst. Technol. 9(6), 777–790 (2001).CrossRefGoogle Scholar
Consolini, L., Morbidi, F., Prattichizzo, D. and Tosques, M., “Leader–follower formation control of nonholonomic mobile robots with input constraints,” Automatica 44(5), 1343–1349 (2008).Google Scholar
Wu, J. and Jiang, Z., “Formation control of multiple mobile robots via switching strategy,” Int. J. Inform. Syst. Sci. 5(6), 210–218 (2009).Google Scholar
Vilca, J., Adouane, L., Mezouar, Y. and Lebraly, P., “An Overall Control Strategy Based on Target Reaching for the Navigation of an Urban Electric Vehicle,” In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2013) pp. 728–734.Google Scholar
Balch, T. and Arkin, R. C., “Behavior-based formation control for multirobot teams,” IEEE Trans . Robot. Automat. 14(6), 926–939 (1998).Google Scholar
Ghommam, J., Mehrjerdi, H. and Saad, M., “Robust formation control without velocity measurement of the leader robot,” Cont. Eng. Pract. 21(8), 1143–1156 (2013).Google Scholar
Ren, W. and Sorensen, N., “Distributed coordination architecture for multi-robot formation control,” Robot. Auton. Syst. 56(4), 324–333 (2008).Google Scholar
S.-C., L. I. U., D.-L., T. A. N. and G.-J., L. I. U., “Robust leader-follower formation control of mobile robots based on a second order kinematics model,” Acta Automat. Sin. 33(9), 947–955 (2007).Google Scholar
Mehrjerdi, H., Ghommam, J. and Saad, M., “Nonlinear coordination control for a group of mobile robots using a virtual structure,” Mechatronics 21(7), 1147–1155 (2011).Google Scholar
van den Broek, T., van de Wouw, N. and Nijmeijer, H., A virtual structure approach to formation control of unicycle mobile robots, Eindhoven University of Technology, the Netherlands, Tech. Rep. DCT (2009).Google Scholar
Kambayashi, Y., Yajima, H., Shyoji, T., Oikawa, R. and Takimoto, M., “Formation control of swarm robots using mobile agents,” Vietnam J. Comput. Sci. 6(02), 193–222 (2019).Google Scholar
Kim, B. and Tsiotras, P., “Experimental comparison of control laws for unicycle-type mobile robots,” SAT 1(2), 9.Google Scholar
Deng, M., Inoue, A., Sekiguchi, K. and Jiang, L., “Two-wheeled mobile robot motion control in dynamic environments,” Robot Comput.-Intgr. Manuf. 26(3), 268–272 (2010).CrossRefGoogle Scholar
Pourboghrat, F., “Exponential stabilization of nonholonomic mobile robots,” Comput. Electr. Eng. 28(5), 349–359 (2002).CrossRefGoogle Scholar
Kanayama, Y., Kimura, Y., Miyazaki, F. and Noguchi, T., “A Stable Tracking Control Method for A Non-Holonomic Mobile Robot,” In: Proceedings of the IEEE/RSJ International Workshop on Intelligent Robots and Systems’, IROS’91.’ (IEEE, 1991) pp. 1236–1241.Google Scholar