Real-Time Digital Simulator Design for Differential Drive Mobile Robot using FPGA

Yıl 2024, Cilt: 12 Sayı: 1, 84 - 89, 01.03.2024

Mehmet Sarac , Ömür Aydoğmuş

https://doi.org/10.17694/bajece.1414730

Cited By: 1

Öz

This paper presents a real-time simulation of a differential drive mobile robot (DDMR). The permanent magnet DC motors that drive the robot’s left and right wheels were modeled and executed in real-time on a Field Programmable Gate Array (FPGA) based co-simulator platform, interfacing with the Webots robot simulator, which simulates the DDMR on the PC side. The electrical parameters, which are not available in robot simulators, were simulated and measured by the proposed co-simulator system in real-time under various environmental conditions and trajectories of the robot. Parameters such as current, voltage, and torque were measured instantaneously, enabling a more realistic simulation. Additionally, the cycle time of the robot simulator was determined to be 32 ms, and the developed FPGA-based simulation operated at approximately 2000 times the speed of the robot simulator. The results demonstrate the applicability of the developed platform in robotic applications.

Anahtar Kelimeler

Co-simulator, Digital Twin, FPGA, Mobile robot, Real-time digital simulator

Kaynakça

• [1] M. U. Farooq, A. Eizad, and H.-K. Bae, “Power solutions for autonomous mobile robots: A survey,” Robotics and Autonomous Systems, vol. 159, p. 104285, 2023.
• [2] G. Fragapane, D. Ivanov, M. Peron, F. Sgarbossa, and J. O. Strandhagen, “Increasing flexibility and productivity in industry 4.0 production networks with autonomous mobile robots and smart intralogistics,” Annals of operations research, vol. 308, no. 1-2, pp. 125–143, 2022.
• [3] G. Fragapane, H.-H. Hvolby, F. Sgarbossa, and J. O. Strandhagen, “Autonomous mobile robots in hospital logistics,” in IFIP International Conference on Advances in Production Management Systems. Springer, 2020, pp. 672–679.
• [4] L. Emmi, E. Le Fl´echer, V. Cadenat, and M. Devy, “A hybrid representation of the environment to improve autonomous navigation of mobile robots in agriculture,” Precision Agriculture, vol. 22, pp. 524–549, 2021.
• [5] F. Luo, Q. Zhou, J. Fuentes, W. Ding, and C. Gu, “A soar-based space exploration algorithm for mobile robots,” Entropy, vol. 24, no. 3, p. 426, 2022.
• [6] P. K. Panigrahi and S. K. Bisoy, “Localization strategies for autonomous mobile robots: A review,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 8, pp. 6019–6039, 2022.
• [7] G. Boztas and O. Aydogmus, “Implementation of pure pursuit algorithm for nonholonomic mobile robot using robot operating system,” Balkan Journal of Electrical and Computer Engineering, vol. 9, no. 4, pp. 337– 341, 2021.
• [8] O. Aydogmus and M. Yilmaz, “Comparative analysis of reinforcement learning algorithms for bipedal robot locomotion,” IEEE Access, pp. 1–1, 2023.
• [9] S. Zhang, J.-t. Yao, Y.-b. Wang, Z.-s. Liu, Y.-d. Xu, and Y.-s. Zhao, “Design and motion analysis of reconfigurable wheel-legged mobile robot,” Defence Technology, vol. 18, no. 6, pp. 1023–1040, 2022.
• [10] L. Tagliavini, G. Colucci, A. Botta, P. Cavallone, L. Baglieri, and G. Quaglia, “Wheeled mobile robots: state of the art overview and kinematic comparison among three omnidirectional locomotion strategies,” Journal of Intelligent & Robotic Systems, vol. 106, no. 3, p. 57, 2022.
• [11] J. Yoon, B. Son, and D. Lee, “Comparative study of physics engines for robot simulation with mechanical interaction,” Applied Sciences, vol. 13, no. 2, p. 680, 2023.
• [12] I. Tejado, J. Serrano, E. P´erez, D. Torres, and B. M. Vinagre, “Low-cost hardware-in-the-loop testbed of a mobile robot to support learning in automatic control and robotics,” IFAC-PapersOnLine, vol. 49, no. 6, pp. 242–247, 2016.
• [13] Y. Chen, S. Chen, T. Zhang, S. Zhang, and N. Zheng, “Autonomous vehicle testing and validation platform: Integrated simulation system with hardware in the loop,” in 2018 IEEE Intelligent Vehicles Symposium (IV), 2018, pp. 949–956.
• [14] A. Hadizadeh, M. Hashemi, M. Labbaf, and M. Parniani, “A matrixinversion technique for fpga-based real-time emt simulation of power converters,” IEEE Transactions on Industrial Electronics, vol. 66, no. 2, pp. 1224–1234, 2019.
• [15] C. Qi, F. Gao, X. Zhao, A. Ren, and Q. Wang, “A force compensation approach toward divergence of hardware-in-the-loop contact simulation system for damped elastic contact,” IEEE Transactions on Industrial Electronics, vol. 64, no. 4, pp. 2933–2943, 2017.
• [16] A. Fekik, H. Khati, A. T. Azar, M. L. Hamida, H. Denoun, I. A. Hameed, and N. A. Kamal, “Fpga in the loop implementation of the puma 560 robot based on backstepping control,” IET Control Theory & Applications, 2023.
• [17] S. Dereli and R. K¨oker, “Hardware design of fpga-based embedded heuristic optimization technique for solving a robotic problem: Ic-pso,” Arabian Journal for Science and Engineering, pp. 1–15, 2023.
• [18] E. Mor´eac, E. M. Abdali, F. Berry, D. Heller, and J.-P. Diguet, “Hardware-in-the-loop simulation with dynamic partial fpga reconfiguration applied to computer vision in ros-based uav,” in 2020 International Workshop on Rapid System Prototyping (RSP). IEEE, 2020, pp. 1–7.
• [19] H. W. Dommel, “Digital computer solution of electromagnetic transients in single-and multiphase networks,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-88, no. 4, p. 388–399, April 1969.
• [20] Cyclone V Device Overview, May 2018.