Robot learning module for elementary school students based on wheel robots
Keywords:
Robot; Microcontroller, Logic, Delphi, Learning moduleAbstract
Robotics is a very complex science, so as to introduce the science of robotics to kindergarten and elementary students which will be presented in a way that is easily understood by children. The science of robotics that is easily understood by children is logic as it is introduced in the form of robotic movements forward, backward, turn right, turn left, and others. With the above background, this paper contributes to creating a media that facilitates the learning of elementary school students to make logical robot programmers and download programs to the microcontroller. Logic robotics learning modules used for elementary school children must be easy to use and easy to understand. This learning module uses the AT89S51 microcontroller and Borland delphi visual programming. This module is a very easy lesson, so students can run robot application programs. Applications such as program robots include figures such as the distance the robot travels forward, backward distance, distance, and distance left. After entering the number, then compile the program and enter the program into the mobile robot.
References
L. Malinverni, C. Valero, M. M. Schaper, and I. G. de la Cruz, “Educational Robotics as a boundary object: Towards a research agenda,” Int. J. Child-Computer Interact., vol. 29, p. 100305, Sep. 2021.
M. Pivetti, S. Di Battista, F. Agatolio, B. Simaku, M. Moro, and E. Menegatti, “Educational Robotics for children with neurodevelopmental disorders: A systematic review,” Heliyon, vol. 6, no. 10, p. e05160, Oct. 2020.
M. Lei, I. M. Clemente, and Y. Hu, “Engagement data of robotic students in a synchronous-hybrid course,” Data Br., vol. 24, p. 103822, Jun. 2019.
M. Elbestawi, D. Centea, I. Singh, and T. Wanyama, “SEPT Learning Factory for Industry 4.0 Education and Applied Research,” Procedia Manuf., vol. 23, no. 2017, pp. 249–254, 2018.
M. L. Lupetti, “Shybo. An open-source low-anthropomorphic robot for children,” HardwareX, vol. 2, pp. 50–60, Oct. 2017.
M. Smakman, P. Vogt, and E. A. Konijn, “Moral considerations on social robots in education: A multi-stakeholder perspective,” Comput. Educ., vol. 174, no. August, p. 104317, Dec. 2021.
R. Szczepanowski et al., “Education biases perception of social robots,” Eur. Rev. Appl. Psychol., vol. 70, no. 2, p. 100521, Apr. 2020.
H. Ono, K. Koike, T. Morita, and T. Yamaguchi, “Ontologies-Based Pupil Robot Interaction with Group Discussion,” Procedia Comput. Sci., vol. 159, pp. 2071–2080, 2019.
S. E. Ferguson et al., “Prospective cohort study comparing quality of life and sexual health outcomes between women undergoing robotic, laparoscopic and open surgery for endometrial cancer,” Gynecol. Oncol., vol. 149, no. 3, pp. 476–483, Jun. 2018.
D. Grajewski, F. Gorski, D. Rybarczyk, P. Owczarek, A. Milecki, and P. Bun, “Use of Delta Robot as an Active Touch Device in Immersive Case Scenarios,” Procedia Comput. Sci., vol. 104, no. December 2016, pp. 485–492, 2017.
F. Yu and E. R. da Silva, “Design for robot assembly: challenges of online education,” Procedia CIRP, vol. 100, pp. 482–487, 2021.
M. Jdeed, M. Schranz, and W. Elmenreich, “A study using the low-cost swarm robotics platform spiderino in education,” Comput. Educ. Open, vol. 1, no. November, p. 100017, Dec. 2020.
M. Lanz, R. Pieters, and R. Ghabcheloo, “Learning environment for robotics education and industry-academia collaboration,” Procedia Manuf., vol. 31, pp. 79–84, 2019.
D. Mourtzis, E. Vlachou, G. Dimitrakopoulos, and V. Zogopoulos, “Cyber- Physical Systems and Education 4.0 –The Teaching Factory 4.0 Concept,” Procedia Manuf., vol. 23, no. 2017, pp. 129–134, 2018.
M. Yuko, T. Takeo, O. Takushi, and H. Yasunari, “Ethics of Information Education for Living with Robots,” ORBIT J., vol. 1, no. 1, pp. 1–13, 2017.
A. Gerosa, V. Koleszar, G. Tejera, L. Gómez-Sena, and A. Carboni, “Cognitive abilities and computational thinking at age 5: Evidence for associations to sequencing and symbolic number comparison,” Comput. Educ. Open, vol. 2, p. 100043, Dec. 2021.
E. A. Konijn and J. F. Hoorn, “Robot tutor and pupils’ educational ability: Teaching the times tables,” Comput. Educ., vol. 157, no. July, p. 103970, Nov. 2020.
C. Goulart, C. Valadão, E. Caldeira, and T. Bastos, “Brain signal evaluation of children with Autism Spectrum Disorder in the interaction with a social robot,” Biotechnol. Res. Innov., vol. 3, no. 1, pp. 60–68, Jan. 2019.
V. Toivonen, M. Lanz, H. Nylund, and H. Nieminen, “The FMS Training Center - a versatile learning environment for engineering education,” Procedia Manuf., vol. 23, no. 2017, pp. 135–140, 2018.
W. Budiharto, A. D. Cahyani, P. C. B. Rumondor, and D. Suhartono, “EduRobot: Intelligent Humanoid Robot with Natural Interaction for Education and Entertainment,” Procedia Comput. Sci., vol. 116, pp. 564–570, 2017.
Y. Purnama, F. A. Herman, J. Hartono, Neilsen, D. Suryani, and G. Sanjaya, “Educational Software as Assistive Technologies for Children with Autism Spectrum Disorder,” Procedia Comput. Sci., vol. 179, no. 2019, pp. 6–16, 2021.
H. Melkas, L. Hennala, S. Pekkarinen, and V. Kyrki, “Impacts of robot implementation on care personnel and clients in elderly-care institutions,” Int. J. Med. Inform., vol. 134, no. January 2019, p. 104041, Feb. 2020.
S. E. Forsström, “Role of teachers in students’ mathematics learning processes based on robotics integration,” Learn. Cult. Soc. Interact., vol. 21, no. December 2018, pp. 378–389, Jun. 2019.
J. Bharatharaj, L. Huang, C. Krägeloh, M. R. Elara, and A. Al-Jumaily, “Social engagement of children with autism spectrum disorder in interaction with a parrot-inspired therapeutic robot,” Procedia Comput. Sci., vol. 133, pp. 368–376, 2018.
S. Akashiba et al., “Implementation of Teacher-Robot Collaboration Lesson Application in PRINTEPS,” Procedia Comput. Sci., vol. 112, pp. 2299–2308, 2017.
G. Fragapane, R. de Koster, F. Sgarbossa, and J. O. Strandhagen, “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” Eur. J. Oper. Res., vol. 294, no. 2, pp. 405–426, Oct. 2021.
S. Kumar, H. Wöhrle, J. de Gea Fernández, A. Müller, and F. Kirchner, “A survey on modularity and distributivity in series-parallel hybrid robots,” Mechatronics, vol. 68, no. September 2018, p. 102367, Jun. 2020.
W. LI and Q. ZHAN, “Kinematics-based four-state trajectory tracking control of a spherical mobile robot driven by a 2-DOF pendulum,” Chinese J. Aeronaut., vol. 32, no. 6, pp. 1530–1540, Jun. 2019.
H. Peel, S. Luo, A. G. Cohn, and R. Fuentes, “Localisation of a mobile robot for bridge bearing inspection,” Autom. Constr., vol. 94, no. June, pp. 244–256, Oct. 2018.
G. A. Di Luna, P. Flocchini, S. Gan Chaudhuri, F. Poloni, N. Santoro, and G. Viglietta, “Mutual visibility by luminous robots without collisions,” Inf. Comput., vol. 254, pp. 392–418, Jun. 2017.
F. A. Bernardini, M. T. da Silva, and J. M. Abe, “Application of Paraconsistent Annotated Evidential Logic Eτ for a Terrestrial Mobile Robot to Avoid Obstacles,” Procedia Comput. Sci., vol. 192, pp. 1821–1830, 2021.
J. Chen et al., “Influence of the intelligent standing mobile robot on lower extremity physiology of complete spinal cord injury patients,” Med. Nov. Technol. Devices, vol. 7, no. 2209, p. 100045, Sep. 2020.
V.-S. Ionescu et al., “DynFloR: A Flow Approach for Data Delivery Optimization in Multi-Robot Network Patrolling,” Procedia Comput. Sci., vol. 159, pp. 97–106, 2019.
J. P. A and A. Tiwari, “Optimization approach for a climbing robot with target tracking in WSNs,” J. Ocean Eng. Sci., vol. 3, no. 4, pp. 282–287, Dec. 2018.
K. Nakajima, C. Premachandra, and K. Kato, “3D environment mapping and self-position estimation by a small flying robot mounted with a movable ultrasonic range sensor,” J. Electr. Syst. Inf. Technol., vol. 4, no. 2, pp. 289–298, Sep. 2017.
A. K. Ramasubramanian and N. Papakostas, “Operator - mobile robot collaboration for synchronized part movement,” Procedia CIRP, vol. 97, pp. 217–223, 2021.
A. P. Moreira, P. Costa, and J. Lima, “New Approach for Beacons Based Mobile Robot Localization using Kalman Filters,” Procedia Manuf., vol. 51, pp. 512–519, 2020.
T. Tsuruta, K. Miura, and M. Miyaguchi, “Mobile robot for marking free access floors at construction sites,” Autom. Constr., vol. 107, no. July, p. 102912, Nov. 2019.
A. K. Rath, D. R. Parhi, H. C. Das, M. K. Muni, and P. B. Kumar, “Analysis and use of fuzzy intelligent technique for navigation of humanoid robot in obstacle prone zone,” Def. Technol., vol. 14, no. 6, pp. 677–682, Dec. 2018.
C.-C. Liu, J.-R. Cheng, and J.-F. Huang, “Integrating Correlation Acquisition with Location Optimization for Accurate Indoor Lightwave Robot Positioning,” Procedia Comput. Sci., vol. 110, pp. 304–311, 2017.
S. Zhang, J. Yao, Y. Wang, Z. Liu, Y. Xu, and Y. Zhao, “Design and motion analysis of reconfigurable wheel-legged mobile robot,” Def. Technol., no. xxxx, Apr. 2021.
G. Caiza, C. A. Garcia, J. E. Naranjo, and M. V Garcia, “Flexible robotic teleoperation architecture for intelligent oil fields,” Heliyon, vol. 6, no. 4, p. e03833, Apr. 2020.
B. K. Patle, G. Babu L, A. Pandey, D. R. K. Parhi, and A. Jagadeesh, “A review: On path planning strategies for navigation of mobile robot,” Def. Technol., vol. 15, no. 4, pp. 582–606, Aug. 2019.
R. Mathew and S. S. Hiremath, “Control of Velocity-Constrained Stepper Motor-Driven Hilare Robot for Waypoint Navigation,” Engineering, vol. 4, no. 4, pp. 491–499, Aug. 2018.
C. Sabo, R. Chisholm, A. Petterson, and A. Cope, “A lightweight, inexpensive robotic system for insect vision,” Arthropod Struct. Dev., vol. 46, no. 5, pp. 689–702, Sep. 2017.
I. Pérez-Hurtado, D. Orellana-Martín, M. Á. Martínez-del-Amor, and L. Valencia-Cabrera, “A membrane computing framework for social navigation in robotics,” Comput. Electr. Eng., vol. 95, no. October 2020, p. 107408, Oct. 2021.
M. R. Panda, P. Das, and S. Pradhan, “Hybridization of IWO and IPSO for mobile robots navigation in a dynamic environment,” J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 9, pp. 1020–1033, Nov. 2020.
M. Łaganowska, “Application of vision systems to the navigation of mobile robots using markers,” Transp. Res. Procedia, vol. 40, pp. 1449–1452, 2019.
K. Chatzilygeroudis, V. Vassiliades, and J. Mouret, “Reset-free Trial-and-Error Learning for Robot Damage Recovery,” Rob. Auton. Syst., vol. 100, pp. 236–250, Feb. 2018.
F. Qian et al., “Ground robotic measurement of aeolian processes,” Aeolian Res., vol. 27, pp. 1–11, Aug. 2017.