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Diferansiyel Sürüşlü Mobil Robotun Bulanık PI Denetleyici Tabanlı Yol Planlamasının Gerçekleştirilmesi

Year 2024, Volume: 19 Issue: 1, 265 - 277, 28.03.2024
https://doi.org/10.55525/tjst.1423794

Abstract

Bu çalışmada, diferansiyel sürüşlü ve dört tekerden tahrikli otonom mobil robotun, optimum yol planlaması için, konum ve hız kontrolü kaskad bağlantılı fuzzy-PI kontrolör ile gerçekleştirilmiştir. Her bir motorun enkoderinden alınan açısal hız bilgileri ile robotun anlık konum ve açı bilgilerini hesaplanmıştır. Referans noktalar ile bu değerler arasındaki açı ve konum hatası bulanık mantık denetleyiciye giriş sinyali olarak uygulanmıştır. Bulanık mantık çıkışından alınan robot açısal ve lineer hız verileri ise kinematik denklemler ile motorlara uygulanacak olan referans hız değerlerine dönüştürülmüştür. Motorların hız kontrolleri bu referans değerler baz alınarak PI kontrolör ile gerçekleştirilmiştir. Bir ve birden fazla referans koordinatlar için gerçekleştirilen çalışma hem MATLAB programında simulasyonda hem de laboratuvar ortamında deneysel olarak gerçekleştirilmiştir. Deneysel olarak yapılan çalışmada, tasarımı gerçekleştirilen Android uygulama ile referans değerler robota bluetooth aracılığıyla gönderilmiştir. Aynı zamanda robotun anlık verileri de yine aynı uygulama üzerinden android cihazda toplanmıştır. Excel formatında toplanan bu veriler mail yolu ile bilgisayara aktarılarak MATLAB programında grafikleri çizdirilmiştir. Alınan sonuçlar incelendiğinde robotun fuzzy-PI kontrolör ile başarılı bir şekilde hem hız hem de konum kontrolünün gerçekleştirildiği görülmüştür.

Project Number

TEKF.19.07.

References

  • Shabalina K, Sagitov A, & Magid E. Comparative analysis of mobile robot wheels design. In 2018 11th International Conference on Developments in Systems Engineering; 2018; (pp. 175-179)
  • Alatise MB, & Hancke GP. A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access 2020; 8, 39830-39846.
  • Liu L, Wang X, Yang X, Liu H, Li J, & Wang P. Path Planning Techniques for Mobile Robots: Review and Prospect. Expert Systems with Applications 2023; 120254.
  • Jiusheng B, Muye Z, & Shirong G. Underground driverless path planning of trackless rubber tyred vehicle based on improved A* and artificial potential field algorithm . Journal of China Coal Society 2022; 47(03), 1347-1360.
  • Zhou X, Yu X, & Peng X. UAV collision avoidance based on varying cells strategy. IEEE Transactions on Aerospace and Electronic Systems, 2018; 55(4), 1743-1755.
  • Challita U, Saad W, & Bettstetter C. Deep reinforcement learning for interference-aware path planning of cellular-connected UAVs. In 2018 IEEE International Conference on Communications (ICC);2018; (pp. 1-7).
  • Guruprasad KR, & Ranjitha TD. CPC algorithm: Exact area coverage by a mobile robot using approximate cellular decomposition. Robotica 2021; 39(7), 1141-1162.
  • Samaniego F, Sanchis J, García-Nieto S, & Simarro R. Recursive rewarding modified adaptive cell decomposition (RR-MACD): a dynamic path planning algorithm for UAVs. Electronics 2019; 8(3), 306.
  • Jung JW, So BC, Kang JG, Lim DW, & Son Y. Expanded Douglas–Peucker polygonal approximation and opposite angle-based exact cell decomposition for path planning with curvilinear obstacles. Applied Sciences 2019; 9(4), 638.
  • Park J, Karumanchi S, & Iagnemma K. Homotopy-based divide-and-conquer strategy for optimal trajectory planning via mixed-integer programming. IEEE Transactions on Robotics 2015; 31(5), 1101-1115.
  • Wang H, Li G, Hou J, Chen L, & Hu NA path planning method for underground intelligent vehicles based on an improved RRT* algorithm. Electronics 2022; 11(3), 294.
  • Ravankar AA, Ravankar A, Emaru T, & Kobayashi Y. HPPRM: hybrid potential based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots. IEEE Access 2020; 8, 221743-221766.
  • Esposito JM, & Wright JN. Matrix completion as a post-processing technique for probabilistic roadmaps. The International Journal of Robotics Research 2019; 38(2-3), 388-400.
  • Fink W, Baker VR, Brooks AJW, Flammia M, Dohm JM, & Tarbell MA. Globally optimal rover traverse planning in 3D using Dijkstra’s algorithm for multi-objective deployment scenarios. Planetary and Space Science 2019; 179, 104707.
  • Balado J, Díaz-Vilariño L, Arias P, & Lorenzo H. Point clouds for direct pedestrian pathfinding in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing 2019; 148, 184-196.
  • Wang YF, Cao XH, & Guo X. Warehouse AGV path planning method based on improved A* algorithm and system short-term state prediction. Computer Integrated Manufacturing System 2021; 1-22.
  • Lamini C, Benhlima S, & Elbekri A. Genetic algorithm-based approach for autonomous mobile robot path planning. Procedia Computer Science 2018; 127, 180-189.
  • Shivgan R & Dong Z. Energy-efficient drone coverage path planning using genetic algorithm. In 2020 IEEE 21st International Conference on High Performance Switching and Routing 2020; (pp. 1-6).
  • Miao C, Chen G, Yan C, & Wu Y. Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Computers & Industrial Engineering 2021; 156, 107230.
  • Ji Y & Liu B. Research and Implementation of Robot Path Planning Based on Ant Colony Algorithm. In Journal of Physics: Conference Series 2022; (Vol. 2171, No. 1, p. 012074).
  • Chai R, Tsourdos A, Savvaris A, Chai S & Xia Y. Solving constrained trajectory planning problems using biased particle swarm optimization. IEEE Transactions on Aerospace and Electronic Systems 2021;57(3), 1685-1701.
  • Qiuyun T, Hongyan S, Hengwei G & Ping W. Improved particle swarm optimization algorithm for AGV path planning. Ieee Access 2021; 9, 33522-33531.
  • Wang Z, Li H & Zhang X. Construction waste recycling robot for nails and screws: Computer vision technology and neural network approach. Automation in Construction 2019; 97, 220-228.
  • Zhu D & Yang SX. Bio-inspired neural network-based optimal path planning for UUVs under the effect of ocean currents. IEEE Transactions on Intelligent Vehicles 2021; 7(2), 231-239.
  • Zadeh LA. Fuzzy sets. Information and control 1965; 8(3), 338–353.
  • Li M. Mobile robot path planning based on fuzzy control. Hebei University of Technology. 2015
  • Xie YN. The research for the mobile robot path planning algorithm. Xi ’ a University of Architecture and Technology. 2016.
  • Zagradjanin N, Rodic A, Pamucar D & Pavkovic B. Cloud-based multi-robot path planning in complex and crowded environment using fuzzy logic and online learning. Information Technology and Control 2021; 50(2), 357-374.
  • Ntakolia C & Lyridis DV. A swarm intelligence graph-based pathfinding algorithm based on fuzzy logic (SIGPAF): A case study on unmanned surface vehicle multi-objective path planning. Journal of Marine Science and Engineering 2021; 9(11), 1243.
  • Gharajeh MS, & Jond HB. An intelligent approach for autonomous mobile robots path planning based on adaptive neuro-fuzzy inference system. Ain Shams Engineering Journal 2022; 13(1), 101491.
  • Jin X, Chen K, Zhao Y, Ji J, & Jing P. Simulation of hydraulic transplanting robot control system based on fuzzy PID controller. Measurement 2020; 164, 108023.
  • Cao G, Zhao X, Ye C, Yu S, Li B, & Jiang C. Fuzzy adaptive PID control method for multi-mecanum-wheeled mobile robot. Journal of Mechanical Science and Technology 2022; 36(4), 2019-2029.
  • Babunski D, Berisha J, Zaev E, & Bajrami X. Application of fuzzy logic and PID controller for mobile robot navigation. In 2020 9th Mediterranean Conference on Embedded Computing; 2020; (pp. 1-4).
  • Cai C. Autonomous Mobile Robot Obstacle Avoidance Using Fuzzy-PID Controller in Robot’s Varying Dynamics. In 2020 39th Chinese Control Conference; 2020; (pp. 2182-2186). IEEE.
  • Lee K, Im DY, Kwak B, & Ryoo YJ. Design of fuzzy-PID controller for path tracking of mobile robot with differential drive. International Journal of Fuzzy Logic and Intelligent Systems 2018; 18(3), 220-228.
  • Top A, & Gökbulut M. A novel period–based method for the measurement direct current motor velocity using low-resolver encoder. Transactions of the Institute of Measurement and Control 2023; 45(4), 711-722.
  • Pololu 37D Metal Gearmotors Datasheet 18s., www.pololu.com/product/4756/specs, Access: 08.12.2023
  • Sparkfun Monster Moto Shild and VNH2SP30 datasheet https: //www .sparkfun .com/ products / retired/ 10182, Access:20.12.2023
  • HM-10 bluetooth modul, http://www.martyncurrey.com/hm-10-bluetooth-4ble-modules/#HM-10Services_and Character istics, Access: 08.12.2023
  • Arduino Due, https://store.arduino.cc/products/arduino-due, Access: 08.12.2023
  • Top A, & Gökbulut M. Android Application Design with MIT App Inventor for Bluetooth Based Mobile Robot Control. Wireless Personal Communications 2022; 126(2), 1403-1429.
  • Hong S, & Hwang Y. design and implementation for iort-based remote control robot using block-based programming. Issues in Information Systems 2020; 21(4), 317-330.
  • De Moura Oliveira PB. Teaching automation and control with App Inventor applications. In 2015 IEEE Global Engineering Education Conference; 2015; (pp. 879-884). IEEE.
  • Asghar MZ, Sana I, Nasir K, Iqbal H, Kundi FM, & Ismail S. Quizzes: Quiz application development using Android-based MIT APP Inventor platform. International Journal of Advanced Computer Science and Aplications 2016; 7(5).
  • Sullivan D, Chen W, & Pandya A. Design of remote control of home appliances via Bluetooth and Android smartphones. In 2017 IEEE International Conference on Consumer Electronics-Taiwan; 2017; (pp. 371-372).
  • Prayogo SS, Saptariani T, & Salahuddin NS. Rancang Aplikasi Android Pengendali Mobil dan Kamera Menggunakan APP inventor, Seminar Nasional Aplikasi Teknologi Informasi 2015; (Vol. 1, No. 1).
  • Kannapiran S, & Chakrapani A. A novel home automation system using Bluetooth and Arduino, international journal of advances in computer and electronics engineering 2017; 2(2), 41-44.
  • Adiono T, Anindya SF, Fuada S, Afifah K, & Purwanda IG. Efficient android software development using mit app inventor 2 for bluetooth-based smart home. Wireless Personal Communications 2019;105(1), 233-256.
  • Karakus M, Uludag S, Guler E, Turner SW, & Ugur A. Teaching computing and programming fundamentals via App Inventor for Android, 2012 International Conference on Information Technology Based Higher Education and Training; 2012;(pp. 1-8). IEEE.
  • Kushwah M, & Patra A. Tuning PID controller for speed control of DC motor using soft computing techniques-A review. Advance in Electronic and Electric Engineering 2014; 4(2), 141-148.
  • Ang KH, Chong G, & Li Y. PID control system analysis, design, and technology. IEEE transactions on control systems technology 2005; 13(4), 559-576.

Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot

Year 2024, Volume: 19 Issue: 1, 265 - 277, 28.03.2024
https://doi.org/10.55525/tjst.1423794

Abstract

This paper uses a cascade-connected fuzzy-PI controller to control the position and speed of a differential drive and four-wheel drive of an autonomous mobile robot for optimal path planning. The angular speed information obtained from the encoder of each motor and the instantaneous position and angle information of the robot were calculated. The angle and position error between the reference points and these values is applied to the fuzzy logic controller as an input signal. The robot angular and linear speed data obtained from the fuzzy logic output were converted into reference speed values with kinematic equations to be applied to the motors. The speed controls of the motors were carried out with a PI controller based on these reference values. The study was performed both as a simulation in the MATLAB program and experimentally in the laboratory environment for one and more reference coordinates. In the experimental study, reference values were sent to the robot via Bluetooth with the Android application designed. At the same time, the instant data of the robot was also collected on the Android device through the same application. These data collected in Excel format were transferred to the computer via e-mail and the graphics were drawn in the MATLAB program. When the results were examined, it was seen that both speed and position control were successfully implemented with the fuzzy-PI controller for optimum path planning of the robot.

Supporting Institution

Fırat Üniversitesi

Project Number

TEKF.19.07.

Thanks

Bu çalışma Fırat Üniversitesi Bilimsel Araştırma Projeler Birimi Tarafından TEKF.19.07 proje numarası ile desteklenmiştir. Desteklerinden dolayı teşekkürlerimizi sunarız.

References

  • Shabalina K, Sagitov A, & Magid E. Comparative analysis of mobile robot wheels design. In 2018 11th International Conference on Developments in Systems Engineering; 2018; (pp. 175-179)
  • Alatise MB, & Hancke GP. A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access 2020; 8, 39830-39846.
  • Liu L, Wang X, Yang X, Liu H, Li J, & Wang P. Path Planning Techniques for Mobile Robots: Review and Prospect. Expert Systems with Applications 2023; 120254.
  • Jiusheng B, Muye Z, & Shirong G. Underground driverless path planning of trackless rubber tyred vehicle based on improved A* and artificial potential field algorithm . Journal of China Coal Society 2022; 47(03), 1347-1360.
  • Zhou X, Yu X, & Peng X. UAV collision avoidance based on varying cells strategy. IEEE Transactions on Aerospace and Electronic Systems, 2018; 55(4), 1743-1755.
  • Challita U, Saad W, & Bettstetter C. Deep reinforcement learning for interference-aware path planning of cellular-connected UAVs. In 2018 IEEE International Conference on Communications (ICC);2018; (pp. 1-7).
  • Guruprasad KR, & Ranjitha TD. CPC algorithm: Exact area coverage by a mobile robot using approximate cellular decomposition. Robotica 2021; 39(7), 1141-1162.
  • Samaniego F, Sanchis J, García-Nieto S, & Simarro R. Recursive rewarding modified adaptive cell decomposition (RR-MACD): a dynamic path planning algorithm for UAVs. Electronics 2019; 8(3), 306.
  • Jung JW, So BC, Kang JG, Lim DW, & Son Y. Expanded Douglas–Peucker polygonal approximation and opposite angle-based exact cell decomposition for path planning with curvilinear obstacles. Applied Sciences 2019; 9(4), 638.
  • Park J, Karumanchi S, & Iagnemma K. Homotopy-based divide-and-conquer strategy for optimal trajectory planning via mixed-integer programming. IEEE Transactions on Robotics 2015; 31(5), 1101-1115.
  • Wang H, Li G, Hou J, Chen L, & Hu NA path planning method for underground intelligent vehicles based on an improved RRT* algorithm. Electronics 2022; 11(3), 294.
  • Ravankar AA, Ravankar A, Emaru T, & Kobayashi Y. HPPRM: hybrid potential based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots. IEEE Access 2020; 8, 221743-221766.
  • Esposito JM, & Wright JN. Matrix completion as a post-processing technique for probabilistic roadmaps. The International Journal of Robotics Research 2019; 38(2-3), 388-400.
  • Fink W, Baker VR, Brooks AJW, Flammia M, Dohm JM, & Tarbell MA. Globally optimal rover traverse planning in 3D using Dijkstra’s algorithm for multi-objective deployment scenarios. Planetary and Space Science 2019; 179, 104707.
  • Balado J, Díaz-Vilariño L, Arias P, & Lorenzo H. Point clouds for direct pedestrian pathfinding in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing 2019; 148, 184-196.
  • Wang YF, Cao XH, & Guo X. Warehouse AGV path planning method based on improved A* algorithm and system short-term state prediction. Computer Integrated Manufacturing System 2021; 1-22.
  • Lamini C, Benhlima S, & Elbekri A. Genetic algorithm-based approach for autonomous mobile robot path planning. Procedia Computer Science 2018; 127, 180-189.
  • Shivgan R & Dong Z. Energy-efficient drone coverage path planning using genetic algorithm. In 2020 IEEE 21st International Conference on High Performance Switching and Routing 2020; (pp. 1-6).
  • Miao C, Chen G, Yan C, & Wu Y. Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Computers & Industrial Engineering 2021; 156, 107230.
  • Ji Y & Liu B. Research and Implementation of Robot Path Planning Based on Ant Colony Algorithm. In Journal of Physics: Conference Series 2022; (Vol. 2171, No. 1, p. 012074).
  • Chai R, Tsourdos A, Savvaris A, Chai S & Xia Y. Solving constrained trajectory planning problems using biased particle swarm optimization. IEEE Transactions on Aerospace and Electronic Systems 2021;57(3), 1685-1701.
  • Qiuyun T, Hongyan S, Hengwei G & Ping W. Improved particle swarm optimization algorithm for AGV path planning. Ieee Access 2021; 9, 33522-33531.
  • Wang Z, Li H & Zhang X. Construction waste recycling robot for nails and screws: Computer vision technology and neural network approach. Automation in Construction 2019; 97, 220-228.
  • Zhu D & Yang SX. Bio-inspired neural network-based optimal path planning for UUVs under the effect of ocean currents. IEEE Transactions on Intelligent Vehicles 2021; 7(2), 231-239.
  • Zadeh LA. Fuzzy sets. Information and control 1965; 8(3), 338–353.
  • Li M. Mobile robot path planning based on fuzzy control. Hebei University of Technology. 2015
  • Xie YN. The research for the mobile robot path planning algorithm. Xi ’ a University of Architecture and Technology. 2016.
  • Zagradjanin N, Rodic A, Pamucar D & Pavkovic B. Cloud-based multi-robot path planning in complex and crowded environment using fuzzy logic and online learning. Information Technology and Control 2021; 50(2), 357-374.
  • Ntakolia C & Lyridis DV. A swarm intelligence graph-based pathfinding algorithm based on fuzzy logic (SIGPAF): A case study on unmanned surface vehicle multi-objective path planning. Journal of Marine Science and Engineering 2021; 9(11), 1243.
  • Gharajeh MS, & Jond HB. An intelligent approach for autonomous mobile robots path planning based on adaptive neuro-fuzzy inference system. Ain Shams Engineering Journal 2022; 13(1), 101491.
  • Jin X, Chen K, Zhao Y, Ji J, & Jing P. Simulation of hydraulic transplanting robot control system based on fuzzy PID controller. Measurement 2020; 164, 108023.
  • Cao G, Zhao X, Ye C, Yu S, Li B, & Jiang C. Fuzzy adaptive PID control method for multi-mecanum-wheeled mobile robot. Journal of Mechanical Science and Technology 2022; 36(4), 2019-2029.
  • Babunski D, Berisha J, Zaev E, & Bajrami X. Application of fuzzy logic and PID controller for mobile robot navigation. In 2020 9th Mediterranean Conference on Embedded Computing; 2020; (pp. 1-4).
  • Cai C. Autonomous Mobile Robot Obstacle Avoidance Using Fuzzy-PID Controller in Robot’s Varying Dynamics. In 2020 39th Chinese Control Conference; 2020; (pp. 2182-2186). IEEE.
  • Lee K, Im DY, Kwak B, & Ryoo YJ. Design of fuzzy-PID controller for path tracking of mobile robot with differential drive. International Journal of Fuzzy Logic and Intelligent Systems 2018; 18(3), 220-228.
  • Top A, & Gökbulut M. A novel period–based method for the measurement direct current motor velocity using low-resolver encoder. Transactions of the Institute of Measurement and Control 2023; 45(4), 711-722.
  • Pololu 37D Metal Gearmotors Datasheet 18s., www.pololu.com/product/4756/specs, Access: 08.12.2023
  • Sparkfun Monster Moto Shild and VNH2SP30 datasheet https: //www .sparkfun .com/ products / retired/ 10182, Access:20.12.2023
  • HM-10 bluetooth modul, http://www.martyncurrey.com/hm-10-bluetooth-4ble-modules/#HM-10Services_and Character istics, Access: 08.12.2023
  • Arduino Due, https://store.arduino.cc/products/arduino-due, Access: 08.12.2023
  • Top A, & Gökbulut M. Android Application Design with MIT App Inventor for Bluetooth Based Mobile Robot Control. Wireless Personal Communications 2022; 126(2), 1403-1429.
  • Hong S, & Hwang Y. design and implementation for iort-based remote control robot using block-based programming. Issues in Information Systems 2020; 21(4), 317-330.
  • De Moura Oliveira PB. Teaching automation and control with App Inventor applications. In 2015 IEEE Global Engineering Education Conference; 2015; (pp. 879-884). IEEE.
  • Asghar MZ, Sana I, Nasir K, Iqbal H, Kundi FM, & Ismail S. Quizzes: Quiz application development using Android-based MIT APP Inventor platform. International Journal of Advanced Computer Science and Aplications 2016; 7(5).
  • Sullivan D, Chen W, & Pandya A. Design of remote control of home appliances via Bluetooth and Android smartphones. In 2017 IEEE International Conference on Consumer Electronics-Taiwan; 2017; (pp. 371-372).
  • Prayogo SS, Saptariani T, & Salahuddin NS. Rancang Aplikasi Android Pengendali Mobil dan Kamera Menggunakan APP inventor, Seminar Nasional Aplikasi Teknologi Informasi 2015; (Vol. 1, No. 1).
  • Kannapiran S, & Chakrapani A. A novel home automation system using Bluetooth and Arduino, international journal of advances in computer and electronics engineering 2017; 2(2), 41-44.
  • Adiono T, Anindya SF, Fuada S, Afifah K, & Purwanda IG. Efficient android software development using mit app inventor 2 for bluetooth-based smart home. Wireless Personal Communications 2019;105(1), 233-256.
  • Karakus M, Uludag S, Guler E, Turner SW, & Ugur A. Teaching computing and programming fundamentals via App Inventor for Android, 2012 International Conference on Information Technology Based Higher Education and Training; 2012;(pp. 1-8). IEEE.
  • Kushwah M, & Patra A. Tuning PID controller for speed control of DC motor using soft computing techniques-A review. Advance in Electronic and Electric Engineering 2014; 4(2), 141-148.
  • Ang KH, Chong G, & Li Y. PID control system analysis, design, and technology. IEEE transactions on control systems technology 2005; 13(4), 559-576.
There are 51 citations in total.

Details

Primary Language English
Subjects Intelligent Robotics, Control Engineering, Simulation, Modelling, and Programming of Mechatronics Systems
Journal Section TJST
Authors

Ahmet Top 0000-0001-6672-2119

Muammer Gökbulut 0000-0003-1870-1772

Project Number TEKF.19.07.
Publication Date March 28, 2024
Submission Date January 22, 2024
Acceptance Date March 26, 2024
Published in Issue Year 2024 Volume: 19 Issue: 1

Cite

APA Top, A., & Gökbulut, M. (2024). Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot. Turkish Journal of Science and Technology, 19(1), 265-277. https://doi.org/10.55525/tjst.1423794
AMA Top A, Gökbulut M. Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot. TJST. March 2024;19(1):265-277. doi:10.55525/tjst.1423794
Chicago Top, Ahmet, and Muammer Gökbulut. “Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot”. Turkish Journal of Science and Technology 19, no. 1 (March 2024): 265-77. https://doi.org/10.55525/tjst.1423794.
EndNote Top A, Gökbulut M (March 1, 2024) Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot. Turkish Journal of Science and Technology 19 1 265–277.
IEEE A. Top and M. Gökbulut, “Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot”, TJST, vol. 19, no. 1, pp. 265–277, 2024, doi: 10.55525/tjst.1423794.
ISNAD Top, Ahmet - Gökbulut, Muammer. “Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot”. Turkish Journal of Science and Technology 19/1 (March 2024), 265-277. https://doi.org/10.55525/tjst.1423794.
JAMA Top A, Gökbulut M. Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot. TJST. 2024;19:265–277.
MLA Top, Ahmet and Muammer Gökbulut. “Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot”. Turkish Journal of Science and Technology, vol. 19, no. 1, 2024, pp. 265-77, doi:10.55525/tjst.1423794.
Vancouver Top A, Gökbulut M. Realization of Fuzzy-PI Controller-Based Path Planning of Differential Drive Mobile Robot. TJST. 2024;19(1):265-77.