Intelligent Autonomous Robot Model for Selective Surface Cleaning

The paper presents an autonomous intelligent mobile robot for washing surfaces with a high-pressure liquid flow. There are structure, device and operation principle of a robot for selective cleaning of surfaces with a compact laminar jet and its main subsystems functionality considered. These subsystems consist of navigation subsystem in the conditions of additional objects presence in the room, subsystem of searching point or continuous contamination and determining their coordinates, control subsystems for the detergent flow by evaluation the rotation angles of the nozzle holders. Authors introduce the concept and solutions for direct and inverse task of positioning the end point of a fluid flow on a surface. The application of the Denavit-Hartenberg transformation and the parabolic kinematic law of fluid motion while flowing out of a point nozzle with a minimum spray angle is a base for the obtained direct task solution of positioning the end point of fluid flow on the treated surface. An algorithm for the inverse task solving of positioning the end point of a fluid flow on a surface is presented using Newton’s numerical method for solving systems of nonlinear equations. There was constructed a model of the movement kinematics of a laminar fluid flow section in the MSC Adams environment. This article considers a mobile robot model, which makes it possible to simulate the dynamics of the robot’s movement simultaneously with the movement kinematics of the jet cross section. The article noted advantages and prospects for further use of the proposed solutions in various industries.

1. Niloy M. A., Shama A., Chakrabortty R. K., Ryan M. J., Badal F. R., Tasneem Z., Ahamed M. H., Moyeen S. I., Das S. K., Ali M. F. A review: Critical design and control issues of indoor autonomous mobile robots, IEEE Access 2021, no. 9, pp. 35338—35370.

2. Siegwart R., Nourbakhsh I. R., Scaramuzza D. Introduction to autonomous mobile robots, Publishing house of Massachusetts Institute of Technology, 2011, 321 P.

3. Miyazaki R., Paul H., Kominami T., Martinez R. R., Shimonomura K. Flying Washer: Development of High-Pressure Washing Aerial Robot Employing Multirotor Platform with AddOn Thrusters, Drones, 2022, no. 6, p. 286.

4. Patent RU2785769, 01.12.2021. Klimenko V., Kravtsev V. S., Komissarov A. V., Borisov Yu. N. Method and system for cleaning premises using automated devices, Patent RU #2785769 Bul. 35 (in Russian).

5. Patent US10165458.0, 01.27.2005, Tani T., Autonomous mobile robot cleaner, Patent US #2005/0171644A1.

6. Patent EP11/043,194, 10.06.2010, W. R. Chung, J. M. Joo, D. W. Kim, J. H. Lee, J. P. Hong, J. Y. Jung, K. G. Yoo, H. C. Jang, Robot cleaner and control method thereof, Patent EP #2261762A3.

7. Ecovacs robot cleaner (Robot moyshchik Ecovac), available at: https://podberi-pylesos.ru/article/articles/roboty-mojshchiki-ecovacs-dlya-pola (date of access 10.03.2023) (in Russian).

8. ProCleaner robot cleaner (Robot moyshchik ProCleaner), available at: https://topixagro.com/catalog/egebjerg/robot-dlya-moyki-stankovprocleaner-x100 (date of access 11.03.2023) (in Russian).

9. EVO Cleaner robot cleaner (Robot moyshchik EVO Cleaner), available at: https://topixagro.com/catalog/egebjerg/robot-dlya-moyki-stank (date of access 15.03.2023) (in Russian).

10. Li Z., Lu S., Jiang Z., Yu S., Zhan Q. Research on Visual Servo Control System of Substation Insulator Washing Robot, Neural Computing for Advanced Applications, NCAA 2022, Communications in Computer and Information Science, 2021, vol. 1638.

11. Patent RU2022130180, 21.11.2022, Bazhanov A. G., Bushu ev D. A., Porkhalo V. A., Ryazanov S. V., Yunda A. I., Mballa Mballa L., Automatic device for selective surface cleaning, Patent RU #2795807 Bul. 14.

12. Lynch L., Newe T., Cliford J., Coleman J., Walsh J. A Review: Automated Ground Vehicle (AGV) and Sensor Technologies, 12th International Conference on Sensing Technology (ICST), 2018, pp. 347—352.

13. Rubanov V., Bushuev D., Karikov E., Bazhanov A., Alekseevsky S. Development a low-cost navigation technology based on metal line sensors and passive RFID tags for industrial automated guided vehicle, ARPN Journal of Engineering and Applied Sciences, 2020, vol. 15, no. 20, pp. 2291—2297.

14. Liang S., Cao Z., Wang C., Yu J. A novel 3D Lidar slam based on directed geometry point and sparse frame, IEEE Robotics and Automation Letters, 2021, vol. 6, no. 2, pp. 374—381.

15. Karam S., Lehtola V., Vosselman G. Strategies to Integrate Imu and Lidar Slam for Indoor Mapping, ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2020, vol. 5, no. 1, pp. 223—230.

16. Jiang G., Yin L., Jin S., Tian C., Ma X., Ou Y. A Simultaneous Localization and Mapping (SLAM) Framework for 2.5D Map Building Based on Low-Cost LiDAR and Vision Fusion, Applied Sciences, 2019, vol. 9, p. 2105.

17. Dissanayake G., Newman P., Clark S., DurrantWhyte H. F., Csorba M. A. Solution to the Simultaneous Localization and Map Building (SLAM) Problem, IEEE Trans. On Robotics and Automation, 2001, vol. 17, no. 3. pp. 229—241.

18. Salvi J., Petilot Y., Battle E. Visual SLAM for 3D Large— Scale Seabed Acquisition Employing Underwater Vehicles // IEEE International Conference on Intelligent Robots and Systems. 2008. pp. 1011—1016.

19. Smirnov A. V., Bezzubtsev A. Yu. Avoiding obstacles with mobile technical means using stereo vision, Programmnyye sistemy: teoriya i prilozheniya, 2016, 4(31), pp. 331—346 (in Russian).

20. Kulabukhov S. A., Bobyr M. A. Stereo vision device for the navigation system of a mobile robot, Sovremennyye innovatsii v nauke i tekhnike: sb. statey. Yugo-Zapadnyy gosudarstvennyy universitet, Publishing house of YUzGU, 2018, pp. 106—110 (in Russian).

21. Zenkevich S. L., Yushchenko A. S. Fundamentals of manipulation robots control, Moscow, Publishing house of MGTU im. Baumana, 2004, 478 p. (in Russian).

22. Polyak B. T. Newton’s method and its role in optimization and computational mathematics, Trudy Instituta sistemnogo analiza Rossiyskoy akademii nauk, 2006, vol. 28, pp. 44—62 (in Russian).