Enhancing the terrain adaptability of a multirobot cooperative transportation system via novel connectors and optimized cooperative strategies

Abstract

Given limited terrain adaptability, most existing multirobot cooperative transportation systems (MRCTSs) mainly work on flat pavements, restricting their outdoor applications. The connectors’ finite deformation capability and the control strategies’ limitations are primarily responsible for this phenomenon. This study proposes a novel MRCTS based on tracked mobile robots (TMRs) to improve terrain adaptability and expand the application scenarios of MRCTSs. In structure design, we develop a novel 6-degree-of-freedom passive adaptive connector to link multiple TMRs and the transported object (the communal payload). In addition, the connector is set with sensors to measure the position and orientation of the robot with respect to the object for feedback control. In the control strategy, we present a virtual leader–physical follower collaborative paradigm. The leader robot is imaginary to describe the movement of the entire system and manage the follower robots. All the TMRs in the system act as follower robots to transport the object cooperatively. Having divided the whole control structure into the leader robot level and the follower robot level, we convert the motion control of the two kinds of robots to trajectory tracking control problems and propose a novel double closed-loop kinematics control framework. Furthermore, a control law satisfying saturation constraints is derived to ensure transportation stability. An adaptive control algorithm processes the wheelbase uncertainty of the TMR. Finally, we develop a prototype of the TMR-based MRCTS for experiments. In the trajectory tracking experiment, the developed MRCTS with the proposed control scheme can converge to the reference trajectory in the presence of initial tracking errors in a finite time. In the outdoor experiment, the proposed MRCTS consisting of four TMRs can successfully transport a payload weighing 60 kg on an uneven road with the single TMR’s maximum load limited to 15 kg. The experimental results demonstrate the effectiveness of the structural design and control strategies of the TMR-based MRCTS.

Graphical abstract

Keywords

multirobot system / cooperative transportation / terrain adaptability / trajectory tracking / collaborative paradigm / uneven road

References

[1] McCreery H F , Breed M . Cooperative transport in ants: a review of proximate mechanisms. Insectes Sociaux, 2014, 61(2): 99–110

[2] Gautam A, Mohan S. A review of research in multi-robot systems. In: Proceedings of 2012 IEEE the 7th International Conference on Industrial and Information Systems (ICIIS). Chennai: IEEE, 2012, 1–5

[3] Tuci E , Alkilabi M H M , Akanyeti O . Cooperative object transport in multi-robot systems: a review of the state-of-the-art. Frontiers in Robotics and AI, 2018, 5: 59

[4] Gans N R , Rogers III J G . Cooperative multirobot systems for military applications. Current Robotics Reports, 2021, 2(1): 105–111

[5] Drew D S . Multi-agent systems for search and rescue applications. Current Robotics Reports, 2021, 2(2): 189–200

[6] Eoh G , Jeon J D , Lee B H . Cooperative object transportation using virtual electric dipole field. International Journal of Mechanical Engineering and Robotics Research, 2016, 5(1): 6–10

[7] Eoh G , Jeon J D , Oh J H , Lee B H . Cooperative object transportation using parallel line formation with a circular shift. Robotica, 2017, 35(6): 1341–1364

[8] Eoh G . A decentralized multi-robot object transportation exploiting surrounding obstacles. International Journal of Mechanical Engineering and Robotics Research, 2022, 11(1): 8–14

[9] Sudsang A, Ponce J. On grasping and manipulating polygonal objects with disc-shaped robots in the plane. In: Proceedings of 1998 IEEE International Conference on Robotics and Automation. Leuven: IEEE, 1998, 2740–2746

[10] Sudsang A, Ponce J. A new approach to motion planning for disc-shaped robots manipulating a polygonal object in the plane. In: Proceedings of 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. San Francisco: IEEE, 2000, 1068–1075

[11] Wang Z D, Kumar V. Object closure and manipulation by multiple cooperating mobile robots. In: Proceedings of 2002 IEEE International Conference on Robotics and Automation. Washington, D.C.: IEEE, 2002, 394–399

[12] Culbertson P, Schwager M. Decentralized adaptive control for collaborative manipulation. In: Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane: IEEE, 2018, 278–285

[13] Culbertson P , Slotine J J , Schwager M . Decentralized adaptive control for collaborative manipulation of rigid bodies. IEEE Transactions on Robotics, 2021, 37(6): 1906–1920

[14] You B, Liu S Y, Zhang T Y, Pang Z H, Li X H. Design of collaborative transportation system via multiple mobile manipulators. In: Proceedings of 2020 the 39th Chinese Control Conference (CCC). Shenyang: IEEE, 2020, 4765–4770

[15] Petitti A, Franchi A, Di Paola D, Rizzo A. Decentralized motion control for cooperative manipulation with a team of networked mobile manipulators. In: Proceedings of 2016 IEEE International Conference on Robotics and Automation (ICRA). Stockholm: IEEE, 2016, 441–446

[16] Yan L , Stouraitis T , Vijayakumar S . Decentralized ability-aware adaptive control for multi-robot collaborative manipulation. IEEE Robotics and Automation Letters, 2021, 6(2): 2311–2318

[17] Huzaefa F , Liu Y C . Force distribution and estimation for cooperative transportation control on multiple unmanned ground vehicles. IEEE Transactions on Cybernetics, 2023, 53(2): 1335–1347

[18] Wang Z J , Schwager M . Force-amplifying N-robot transport system (force-ANTS) for cooperative planar manipulation without communication. The International Journal of Robotics Research, 2016, 35(13): 1564–1586

[19] Hashimoto M, Oba F, Eguchi T. Dynamic control approach for motion coordination of multiple wheeled mobile robots transporting a single object. In: Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems. Yokohama: IEEE, 1993, 1944–1951

[20] Loh C C , Traechtler A . Cooperative transportation of aload using nonholonomic mobile robots. Procedia Engineering, 2012, 41: 860–866

[21] Abou-Samah M , Tang C P , Bhatt R M , Krovi V . A kinematically compatible framework for cooperative payload transport by nonholonomic mobile manipulators. Autonomous Robots, 2006, 21(3): 227–242

[22] Trebi-Ollennu A, Nayar H D, Aghazarian H, Ganino A, Pirjanian P, Kennedy B, Huntsberger T, Schenker P. Mars rover pair cooperatively transporting a long payload. In: Proceedings of 2002 IEEE International Conference on Robotics and Automation. Washington, D.C.: IEEE, 2002, 3136–3141

[23] Huntsberger T L , Trebi-Ollennu A , Aghazarian H , Schenker P S , Pirjanian P , Nayar H D . Distributed control of multi-robot systems engaged in tightly coupled tasks. Autonomous Robots, 2004, 17(1): 79–92

[24] Alonso-Mora J , Baker S , Rus D . Multi-robot formation control and object transport in dynamic environments via constrained optimization. The International Journal of Robotics Research, 2017, 36(9): 1000–1021

[25] Koung D , Kermorgant O , Fantoni I , Belouaer L . Cooperative multi-robot object transportation system based on hierarchical quadratic programming. IEEE Robotics and Automation Letters, 2021, 6(4): 6466–6472

[26] Faal S G, Kalat S T, Onal C D. Towards collective manipulation without inter-agent communication. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing. Pisa: ACM, 2016, 275–280

[27] Farivarnejad H, Berman S. Decentralized collective transport along manifolds compatible with holonomic constraints by robots with minimal global information. In: Proceedings of 2020 American Control Conference (ACC). Denver: IEEE, 2020, 2068–2075

[28] Zhang T Y, Pang Z H, You B, Li J Y, Liu G P. Collaborative transportation using multi-WMRs via networked predictive control. In: Proceedings of 2019 Chinese Control Conference (CCC). Guangzhou: IEEE, 2019, 5922–5927

[29] Yang X , Watanabe K , Izumi K , Kiguchi K . A decentralized control system for cooperative transportation by multiple non-holonomic mobile robots. International Journal of Control, 2004, 77(10): 949–963

[30] Kanayama Y, Kimura Y, Miyazaki F, Noguchi T. A stable tracking control method for an autonomous mobile robot. In: Proceedings of IEEE International Conference on Robotics and Automation. Cincinnati: IEEE, 1990, 384–389

[31] Shentu S Z , Xie F G , Liu X J , Gong Z . Motion control and trajectory planning for obstacle avoidance of the mobile parallel robot driven by three tracked vehicles. Robotica, 2021, 39(6): 1037–1050

[32] Fukao T , Nakagawa H , Adachi N . Adaptive tracking control of a nonholonomic mobile robot. IEEE Transactions on Robotics and Automation, 2000, 16(5): 609–615

[33] Zhang X L , Huang Y , Wang S T , Meng W , Li G , Xie Y L . Motion planning and tracking control of a four-wheel independently driven steered mobile robot with multiple maneuvering modes. Frontiers of Mechanical Engineering, 2021, 16(3): 504–527

[34] Liu Q, Gong Z, Xie F G, Shentu S Z, Liu X J. An RBFNN-informed adaptive sliding mode control for wheeled mobile robots. In: Proceedings of Intelligent Robotics and Applications: 14th International Conference, ICIRA 2021. Yantai: Springer, 2021, 649–658

[35] Martínez J L , Mandow A , Morales J , Pedraza S , García-Cerezo A . Approximating kinematics for tracked mobile robots. The International Journal of Robotics Research, 2005, 24(10): 867–878

[36] Wang T M , Wu Y , Liang J H , Han C H , Chen J , Zhao Q T . Analysis and experimental kinematics of a skid-steering wheeled robot based on a laser scanner sensor. Sensors, 2015, 15(5): 9681–9702

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