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Haptic Feedback Remote Control System for Electric Mechanical Assembly Vehicle Developed to Avoid Obstacles

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Abstract

In recent decades, various solutions have been proposed for the navigation of vehicles in unknown environments to avoid obstacles in order to perform safer driving. In this article, a designed electric mechanical assembly vehicle provided by Mecanum 4-omnidirectional wheels holonomic on the corners of a rectangular base has been investigated with the ability to detect obstacles by combining the developed algorithms and sensing system.The vehicle is equipped with LiDAR and communication is guaranteed by WiFi. The interface with the mechanical and electrical devices of four wheeled Mecanum vehicle is well-executed by haptic feedback remote control using an analog joystick that guarantees the driving movements. Experimental tests were conducted to avoid obstacles and detect collision by the electric vehicle, in case of hazard detection. In this investigation, the obstacle is placed at the distance of 2 m away from the vehicle. If the resistance decreases, it means that the vehicle is approaching an obstacle. The recorded speed of vehicle passes from 350 mm/s to less than 10 mm/s, on the contrast, the resistance increases to reach 100%.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Choi, C., Kang, Y.: Simultaneous braking and steering control method based on nonlinear model predictive control for emergency driving support. Int. J. Control Automat. Syst. 15(1), 345–353 (2017)

    Article  Google Scholar 

  2. Ju, C., Son, H.I.: Evaluation of haptic feedback in the performance of a teleoperated unmanned ground vehicle in an obstacle avoidance scenario. Int. J. Control Automat. Syst. 17(1), 168–180 (2019)

    Article  MathSciNet  Google Scholar 

  3. Duan, H., Liu, S.: Unmanned air/ground vehicles heterogeneous cooperative techniques: current status and prospects. Sci. China Technol. Sci. 53(5), 1349–1355 (2010)

    Article  Google Scholar 

  4. Roberts, R., Barajas, M., Rodriguez-Leal, E., Gordillo, J.L.: Haptic feedback and visual servoing of teleoperated unmanned aerial vehicle for obstacle awareness and avoidance. Int. J. Adv. Robot. Syst. 14(4), 1729881417716365 (2017)

    Article  Google Scholar 

  5. Hou, X.: Haptic teleoperation of a multirotor aerial robot using path planning with human intention estimation. Intell. Serv. Robot. 14(1), 33–46 (2021)

    Article  Google Scholar 

  6. Han, J., Cho, K., Jang, I., Ju, C., Il Son, H., Yang, G.-H.: Development of a shared controller for obstacle avoidance in a teleoperation system. Int. J. Control Automat. Syst. 18(11), 2974–2982 (2020)

    Article  Google Scholar 

  7. Kanellakis, C., Nikolakopoulos, G.: Survey on computer vision for uavs: current developments and trends. J. Intell. Robot. Syst. 87(1), 141–168 (2017)

    Article  Google Scholar 

  8. Chicaiza, F.A., Slawiñski, E., Salinas, L.R., Mut, V.A.: Evaluation of path planning with force feedback for bilateral teleoperation of unmanned rotorcraft systems. J. Intell. Robot. Syst. 105(2), 1–17 (2022)

    Article  Google Scholar 

  9. Ijeh, I.C.: A collision-avoidance system for an electric vehicle: a drive-by-wire technology initiative. SN Applied Sciences 2(4), 1–20 (2020)

    Article  Google Scholar 

  10. Xiong, L., Fu, Z., Zeng, D., Qian, Z., Leng, B.: A path planning and tracking framework based on model predictive control for autonomous vehicle obstacle avoidance. In: The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks, pp 1137–1143. Springer (2022)

  11. Li, G., Li, G., Chen, P.: The method of detecting nearest distance between obstacles and vehicle tail based on binocular vision system. In: 2016 IEEE Vehicle Power and Propulsion Conference (VPPC), pp 1–5. IEEE (2016)

  12. Sakthivel, K., Thavamani, P., Chandrasekar, R., Sheik mydeen Ismail, M., Govindaraj, V., et al.: A literature review: a novel human vital sign based wheel chair cum stretcher for disabled person. In: 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 1494–1500. IEEE (2020)

  13. Antipin, A., Frizen, V., Sannikov, P., Volhin, M.S.: Application of the drive systems through the stepper motors in the process equipment, manipulators and pushers without feedback. In: 2018 20th International Symposium on Electrical Apparatus and Technologies (SIELA), pp. 1–4. IEEE (2018)

  14. Lee, J., Choi, S.: Nonlinear model predictive control for path tracking in high-speed corner entry situations. Int. J. Automot. Technol. 23(5), 1373–1381 (2022)

    Article  Google Scholar 

  15. Liu, J., Jayakumar, P., Stein, J.L., Ersal, T.: Combined speed and steering control in high-speed autonomous ground vehicles for obstacle avoidance using model predictive control. IEEE Trans. Veh. Technol. 66(10), 8746–8763 (2017)

    Article  Google Scholar 

  16. Cetin, O., Zagli, I., Yilmaz, G.: Establishing obstacle and collision free communication relay for uavs with artificial potential fields. J. Intell. Robot. Syst. 69(1), 361–372 (2013)

    Article  Google Scholar 

  17. Qian, Z., Lyu, W., Dai, Y., Xu, J.: A consensus-based model predictive control with optimized line-of-sight guidance for formation trajectory tracking of autonomous underwater vehicles. J. Intell. Robot. Syst. 106(1), 1–13 (2022)

    Article  Google Scholar 

  18. Alonso-Mora, J., Naegeli, T., Siegwart, R., Beardsley, P.: Collision avoidance for aerial vehicles in multi-agent scenarios. Auton. Robot. 39(1), 101–121 (2015)

    Article  Google Scholar 

  19. Liu, Z., Zhang, Y., Yuan, C., Ciarletta, L., Theilliol, D.: Collision avoidance and path following control of unmanned aerial vehicle in hazardous environment. J. Intell. Robot. Syst. 95(1), 193–210 (2019)

    Article  Google Scholar 

  20. Li, J., Bao, H., Han, X., Pan, F., Pan, W., Zhang, F., Wang, D.: Real-time self-driving car navigation and obstacle avoidance using mobile 3d laser scanner and gnss. Multimed. Tools Appl. 76 (21), 23017–23039 (2017)

    Article  Google Scholar 

  21. Lin, Z., Castano, L., Mortimer, E., Xu, H.: Fast 3d collision avoidance algorithm for fixed wing uas. J. Intell. Robot. Syst. 97(3), 577–604 (2020)

    Article  Google Scholar 

  22. Sezer, V.: An optimized path tracking approach considering obstacle avoidance and comfort. J. Intell. Robot. Syst. 105(1), 1–14 (2022)

    Article  Google Scholar 

  23. Dentler, J., Rosalie, M., Danoy, G., Bouvry, P., Kannan, S., Olivares-Mendez, M.A., Voos, H.: Collision avoidance effects on the mobility of a uav swarm using chaotic ant colony with model predictive control. J. Intell. Robot. Syst. 93(1), 227–243 (2019)

    Article  Google Scholar 

  24. Xu, W., Yan, C., Jia, W., Ji, X., Liu, J.: Analyzing and enhancing the security of ultrasonic sensors for autonomous vehicles. IEEE Internet Things J. 5(6), 5015–5029 (2018)

    Article  Google Scholar 

  25. Tang, J., Duan, H., Lao, S.: Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review. Artif. Intell. Rev. 1–33 (2022)

  26. Mayetin, U., Kucuk, S.: Design and experimental evaluation of a low cost, portable, 3-dof wrist rehabilitation robot with high physical human–robot interaction. J. Intell. Robot. Syst. 106(3), 1–22 (2022)

    Article  Google Scholar 

  27. Laghmara, H., Boudali, M.-T., Laurain, T., Ledy, J., Orjuela, R., Lauffenburger, J.-P., Basset, M.: Obstacle avoidance, path planning and control for autonomous vehicles. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 529–534. IEEE (2019)

  28. Shibata, K., Shibata, N., Nonaka, K., Sekiguchi, K.: Model predictive obstacle avoidance control for vehicles with automatic velocity suppression using artificial potential field. IFAC-PapersOnLine 51(20), 313–318 (2018)

    Article  Google Scholar 

  29. Labayrade, R., Royere, C., Gruyer, D., Aubert, D.: Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner. Auton. Robot. 19(2), 117–140 (2005)

    Article  Google Scholar 

  30. Perumal, D.G., Srinivasan, S., Subathra, B., Saravanakumar, G., Ayyagari, R.: Milp based autonomous vehicle path-planning controller for unknown environments with dynamic obstacles. Int. J. Heavy Vehicle Syst. 23(4), 350–369 (2016)

    Article  Google Scholar 

  31. Daza, I.G., Izquierdo, R., Martínez, L.M., Benderius, O., Llorca, D.F.: Sim-to-real transfer and reality gap modeling in model predictive control for autonomous driving. Applied Intelligence 1–17 (2022)

  32. Cao, M., Wang, J.: Obstacle detection for autonomous driving vehicles with multi-lidar sensor fusion. J. Dyn. Syst. Meas. Contr. 142(2), 021007 (2020)

    Article  Google Scholar 

  33. Ohn-Bar, E., Trivedi, M.M.: Looking at humans in the age of self-driving and highly automated vehicles. IEEE Transactions on Intelligent Vehicles 1(1), 90–104 (2016)

    Article  Google Scholar 

  34. Rosique, F., Navarro, P.J., Fernández, C., Padilla, A.: A systematic review of perception system and simulators for autonomous vehicles research. Sensors 19(3), 648 (2019)

    Article  Google Scholar 

  35. Chun, C., Suh, S., Lee, S., Roh, C.-W., Kang, S., Kang, Y.: Autonomous navigation of kuve (kist unmanned vehicle electric. J. Inst. Control Robot. Syst. 16(7), 617–624 (2010)

    Article  Google Scholar 

  36. Bong, J.H., Choi, S., Hong, J., Park, S.: Force feedback haptic interface for bilateral teleoperation of robot manipulation. Microsyst. Technol. 28(10), 2381–2392 (2022)

    Article  Google Scholar 

  37. Wagner, A., Peterson, J., Donnelly, J., Chourey, S., Kochersberger, K.: Online aerial 2.5 d terrain mapping and active aerial vehicle exploration for ground robot navigation. J. Intell. Robot. Syst. 106 (3), 1–18 (2022)

    Article  Google Scholar 

  38. Yuk, D.-G., Sohn, J.W.: User independent hand motion recognition for robot arm manipulation. J. Mech. Sci. Technol. 36(6), 2739–2747 (2022)

    Article  Google Scholar 

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Acknowledgements

The authors would like to express their gratitude for the reliable support and scientific developments provided by Kamil Daniel, Michał Staroń, Marcin Olszowy, Daniel Liwiński and Jakub Otrza̧sek during the research activity. The authors wish to thank for her contribution and support Agata Abela.

Funding

This research was supported by the European Union from the European Social Fund in the framework of the project “Silesian University of Technology as a Center of Modern Education based on research and innovation” POWR.03.05.00-00-Z098/17.

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by P.K., P.N., W.B., P.B. and G.L.S. Conceptualization: P.K., P.N., W.B. ; Methodology: P.K., P.N., W.B.; Formal analysis and investigation: P.K., P.N., W.B., P.B.; Supervision: P.K., P.N., W.B. and G.L.S.. The draft of the manuscript was written by all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Grazia Lo Sciuto.

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Kowol, P., Nowak, P., Banaś, W. et al. Haptic Feedback Remote Control System for Electric Mechanical Assembly Vehicle Developed to Avoid Obstacles. J Intell Robot Syst 107, 41 (2023). https://doi.org/10.1007/s10846-023-01824-3

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