Abstract
The cleaning of large-scale indoor public spaces is currently performed by workers driving cleaning machines. However, labor shortages are becoming more acute, resulting in human labor being substituted with robotics solutions for the cleaning of large public facilities (e.g., airport terminals, stations, and underground commercial streets). With the rapid development of sensing and automation technologies, various types of large-scale cleaning robots have been proposed. However, due to difficulty in precise indoor positioning, cleaning along a preplanned path is still challenging for robots. Therefore, this study proposes a cleaning robot that maps large indoor spaces using laser scanning and can avoid obstacles. The chassis of the robot has two independent primary wheels and two auxiliary wheels as power and support; direct current (DC) is employed to power the vacuum cleaner and the robot’s drive motor, to prevent power loss due to DC–alternating current conversion. An indoor facility undergoes three-dimensional laser scanning, and the cleaning space is then mapped through matrix graphics; subsequently, a path is planned using the boustrophedon method. The distance to the wall, measured by the laser rangefinder, is employed as a reference for the correction of the robot’s movement. The powerful vacuum cleaner design of the proposed cleaning robot was experimentally confirmed as having the ability to suction trash. In addition, the proposed robot was shown to clearly identify obstacles through laser scanning and successfully avoid them during the cleaning process to complete the cleaning task along the preplanned cleaning route.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig1_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig3_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig10_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig11_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig12_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig13_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig14_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig15_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig16_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig17_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig18_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig19_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig20_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig21_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig22_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig23_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig24_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00542-018-4048-2/MediaObjects/542_2018_4048_Fig25_HTML.png)
Similar content being viewed by others
References
Araujo R, de Almeida AT (1999) Learning sensor-based navigation of a real mobile robot in unknown worlds. IEEE Trans Syst Man Cybern Part B (Cybernetics) 29(2):164–178
Berglund T, Brodnik A, Jonsson H, Staffanson M, Soderkvist I (2010) Planning smooth and obstacle-avoiding B-spline paths for autonomous mining vehicles. IEEE Trans Autom Sci Eng 7(1):167–172
Bretl T, Hutchinson S (2013) Robust coverage by a mobile robot of a planar workspace. In: 2013 IEEE international conference on robotics and automation (ICRA), pp 4582–4587
Čelan V, Stančić I, Musić J (2016) Cleaning up smart cities—localization of semi-autonomous floor scrubber. In: 2016 international multidisciplinary conference on computer and energy science (SpliTech), pp 1–6
Chen P, Gu Z, Zhang G, Liu H (2014) Ceiling vision localization with feature pairs for home service robots. In: 2014 IEEE international conference on robotics and biomimetics (ROBIO 2014), pp 2274–2279
Chung H-Y, Hou C-C, Chen Y-S (2015) Indoor intelligent mobile robot localization using fuzzy compensation and Kalman filter to fuse the data of gyroscope and magnetometer. IEEE Trans Ind Electron 62(10):6436–6447
Gutmann J-S, Eade E, Fong P, Munich ME (2012) Vector field SLAM—localization by learning the spatial variation of continuous signals. IEEE Trans Robot 28(3):650–667
Gutmann J-S, Fong P, Chiu L, Munich ME (2013) Challenges of designing a low-cost indoor localization system using active beacons. In: 2013 IEEE conference on technologies for practical robot applications (TePRA), pp 1–6
Hagele M (2016) Robots conquer the world (Turning Point). IEEE Robot Autom Mag 23(1):118–120
Hagele M (2017) Double-digit growth highlights a boom in robotics (industrial activities). IEEE Robot Autom Mag 24(1):12–14
Hirukawa H, Inoue H (2007) Expo 2005 Robotics Project. In: Robotics Research. Springer, Berlin, Heidelberg, pp 567–580
Hwang S-Y, Song J-B (2011) Monocular vision-based SLAM in indoor environment using corner, lamp, and door features from upward-looking camera. IEEE Trans Ind Electron 58:4804–4812. http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=600567410
Jones JL (2006) Robots at the tipping point: the road to iRobot Roomba. IEEE Robot Autom Mag 13(1):76–78
Kong CS, Peng NA, Rekleitis I (2006) Distributed coverage with multi-robot system. In: Proceedings 2006 IEEE international conference on robotics and automation, pp 2423–2429
Lawitzky G (2000) A Navigation system for cleaning robots. J Auton Robots 9(3):255–260
Lee S, Lee S (2013) Embedded visual SLAM: applications for low-cost consumer robots. IEEE Robot Autom Mag 20(4):83–95
Miah MS, Knoll J, Hevrdejs K (2018) Intelligent range-only mapping and navigation for mobile robots. IEEE Trans Ind Inform 14(3):1164–1174
Nishida T, Takemura Y, Fuchikawa Y, Kurogi S, Ito S, Hiratsuka MON, Miyagawa H, Watanabe Y, Suehiro T, Kawamura Y, Ohkawa F (2006) Development of a sensor system for outdoor service robot. In: International joint conference on SICE-ICASE, pp 2687–2691
Noertjahyana A, Wijayanto IA, Andjarwirawan J (2017) Development of mobile indoor positioning system application using Android and Bluetooth low energy with trilateration method. In: 2017 international conference on soft computing, intelligent system and information technology (ICSIIT), pp 185–189
Phillip W (2005) Mobile Robots in Japan. DTI international technology promoter, pp 1–9
Reinstein M, Hoffmann M (2013) Dead reckoning in a dynamic quadruped robot based on multimodal proprioceptive sensory information. IEEE Trans Robot 29(2):563–571
Roman HT (1993) Robotic applications in PSE&G’s nuclear and fossil power plants. IEEE Trans Energy Convers 8(3):584–592
Zampella F, Ruiz ARJ, Granja FS (2015) Indoor positioning using efficient map matching, RSS measurements, and an improved motion model. IEEE Trans Veh Technol 64(4):1304–1317
Acknowledgements
The authors would like to thank The Ministry of Science and Technology (MOST) of Taiwan for supporting this research under project number MOST 106-2622-E-151-018-CC3
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liao, TI., Chen, SS., Lien, CC. et al. Development of a high-endurance cleaning robot with scanning-based path planning and path correction. Microsyst Technol 27, 1061–1074 (2021). https://doi.org/10.1007/s00542-018-4048-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00542-018-4048-2