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LOGISWARM: A low-cost multi-robot testbed for cooperative transport research

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Abstract

An increased demand in the market for various commodities, especially FMCGs (Fast Moving Consumer Goods) requires warehouses to stock up an increased number of items which keep renewing rapidly. Conventional methods of warehouse logistics employ mixed use of human labor and Automated Guided Vehicles (AGVs) which involve a lot of cost for running and maintenance, and come with a number of limitations. Hence, technological innovations such as a group of robots, can be used to assist warehouse logistics in a novel and efficient manner. This paper presents a low-cost multi-robot testbed - LOGISWARM, a model which can be used to study how a group of robots can be employed for warehouse logistics and related applications. Each robot in the system is called a BudgeBOT, which is a two-wheeled differential drive robot. A feedback system consisting of overhead camera(s) is also used to constantly monitor and correct error(s) which arise during the transportation of payload. LOGISWARM can act as a research platform which can be used to develop and study multi-robot cooperative transport mechanisms and their behavior for different payload form factors, robot configurations, and applications; alongside being inexpensive, easy to develop and resistant to overall system failure.

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References

  1. Beinschob P, Meyer M, Reinke C, Digani V, Secchi C, Sabattini EL (2017) Semi-automated map creation for fast deployment of AGV fleets in modern logistics. Rob Auton Syst 87:281–295

    Article  Google Scholar 

  2. Bonani M, Raemy X, Pugh J, Mondana F, Cianci C, Klaptocz A, Magnenat S, Zufferey JC, Floreano D, Martinoli A (2009) The epuck, a robot designed for education in engineering. Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions 1:59–65

    Google Scholar 

  3. Cardarelli E, Digani V, Sabattini L, Secchi C, Fantuzzi EC (2017) Cooperative cloud robotics architecture for the coordination of multi-AGV systems in industrial warehouses. Mechatronics 45:1–13

    Article  Google Scholar 

  4. Cashmore M, Fox M, Long D, Magazzeni D, Ridder B, Carrera A, Palomeras N, Hurtos N, Carreras M (2015) Rosplan: Planning in the robot operating system. In: Proceedings of the international conference on automated planning and scheduling, 25(1)

  5. Chen J, Gauci M, Li W, Kolling A, Groß R (2015) Occlusion-based cooperative transport with a swarm of miniature mobile robots. IEEE Trans Robot 31(2):307–321. https://doi.org/10.1109/TRO.2015.2400731

    Article  Google Scholar 

  6. Dudek G, Jenkin M (2010) Computational principles of mobile robotics. Cambridge University, Cambridge

    Book  Google Scholar 

  7. Egerstedt M, Martini S, Cao M, Camlibel K, Bicchi A (2012) Interacting with networks: How does structure relate to controllability in Single-Leader, Consensus Networks?. IEEE Control Syst Mag 32(4):66–73. https://doi.org/10.1109/MCS.2012.2195411

    Article  MathSciNet  Google Scholar 

  8. Hwang KS, Ling JL, Wang W (2014) Adaptive reinforcement learning in box-pushing robots. In: 2014 IEEE International Conference on Automation Science and Engineering (CASE), Taipei. https://doi.org/10.1109/CoASE.2014.6899476, pp 1182–1187

  9. Karpe K, Chatterjee A, Srinivas P, Samiappan D, Ramamoorthy K, Sabattini L (2019) SPRINTER: A Discrete Locomotion Robot For Precision Swarm Printing. In: 2019 19th International Conference on Advanced Robotics (ICAR), Belo Horizonte Brazil. https://doi.org/10.1109/ICAR46387.2019.8981621, pp 733–738

  10. Khan D et al (2018) Robust Tracking Through the Design of High Quality Fiducial Markers: An Optimization Tool for ARToolKit. In: IEEE Access. https://doi.org/10.1109/ACCESS.2018.2801028, vol 6, pp 22421–22433

  11. Krogius M, Haggenmiller A, Olson E (2019) Flexible layouts for fiducial tags. In: IROS, pp 1898–1903

  12. Lightbody P, Krajník T, Hanheide M (2017) An efficient visual fiducial localisation system. ACM SIGAPP Appl Comput Rev 17(3):28–37

    Article  Google Scholar 

  13. Makita Satoshi, Weiwei W (2018) A Survey of Robotic Caging and Its Applications. 36. 316-326. https://doi.org/10.7210/jrsj.36.316

  14. Medina O, Hacohen S, Shvalb N (2020) Robotic swarm motion planning for load carrying and manipulating. IEEE Access 8:53141–53150. https://doi.org/10.1109/ACCESS.2020.2979929

    Article  Google Scholar 

  15. Mehami J, Nawi M, Zhong ERY (2018) Smart automated guided vehicles for manufacturing in the context of Industry 4.0. Procedia Manuf 26:1077–1086

    Article  Google Scholar 

  16. Parra-Gonzalez EF, Ramirez-Torres JG, Toscano-Pulido G (2009) A new object path planner for the box pushing problem. In: 2009 Electronics, Robotics and Automotive Mechanics Conference (CERMA), Cuernavaca, Morelos. https://doi.org/10.1109/CERMA.2009.54, pp 119–124

  17. Pickem D, Lee M, Egerstedt M (2015) The gritsbot in its natural habitat-a multi-robot testbed. In: Robotics and Automation (ICRA) IEEE International Conference on. IEEE, p 2015

  18. Qin J, Li M, Shi Y, Ma Q, Zheng WX (2019) Optimal Synchronization Control of Multiagent Systems With Input Saturation via Off-Policy Reinforcement Learning. IEEE Trans Neural Netw Learn Syst 30(1):85–96. https://doi.org/10.1109/TNNLS.2018.2832025

    Article  MathSciNet  Google Scholar 

  19. Rubenstein M, Ahler C, Nagpal R (2012) Kilobot: A low cost scalable robot system for collective behaviors. In: Robotics and automation(ICRA), 2012 IEEE International Conference on. IEEE, pp 3293–3298

  20. Sagitov A, Shabalina K, Sabirova L, Li H, Magid E (2017) ARTAg, AprilTag and CALTag Fiducial Marker Systems: Comparison in a Presence of Partial Marker Occlusion and Rotation. In: ICINCO (2), pp 182–191

  21. Wang Y, De Silva CW (2006) Multi-robot Box-pushing: Single-agent Q-Learning vs. Team Q-Learning. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems Beijing. https://doi.org/10.1109/IROS.2006.281729, pp 3694–3699

  22. Wang J, Olson E (2016) Apriltag 2: Efficient and robust fiducial detection. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Daejeon. https://doi.org/10.1109/IROS.2016.7759617, pp 4193–4198

  23. Wurman PR, D’Andrea R, Mountz M (2008) Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Mag 29(1):9

    Google Scholar 

  24. Zhang F, Zhang S, Zhang Y, Guo H, Zhang A (2010) Consensus-based distributed control of multi-agent systems. In: 2010 First international conference on pervasive computing, signal processing and applications, Harbin. https://doi.org/10.1109/PCSPA.2010.60, pp 216–219

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Acknowledgements

The authors would like to thank SRM Institute of Science and Technology for funding this project. We are also thankful to our teammates at Beeclust Multi-Robot Systems lab for their constant support.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, India

    Shreshtha Gupta, Shashank Shekhar, Kedar Karpe, Gautham JS, Pranav Srinivas, Mayank Kumar, Preshit Sharma, Avinash Sinha, Kumar Ramamoorthy & Samiappan Dhanalakshmi

  2. Department of Computer Science Engineering, SRM Institute of Science and Technology, Kattankulathur, India

    Aninda Ghosh & Kushagra Singh

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  1. Shreshtha Gupta
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  2. Shashank Shekhar
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  3. Kedar Karpe
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  5. Gautham JS
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  11. Kumar Ramamoorthy
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  12. Samiappan Dhanalakshmi
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Correspondence to Kumar Ramamoorthy.

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Gupta, S., Shekhar, S., Karpe, K. et al. LOGISWARM: A low-cost multi-robot testbed for cooperative transport research. Multimed Tools Appl 81, 27339–27362 (2022). https://doi.org/10.1007/s11042-022-12689-3

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  • DOI: https://doi.org/10.1007/s11042-022-12689-3

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