Optimization Shortest One-Way Path for Energy Saving Auto Robot Collecting Floating Garbage using Fast Approximate Nearest Neighbor Search

Sumitra Nuanmeesri, Lap Poomhiran


Floating garbage is a global problem that needs to be cleared and disposed of from the sea and rivers or canals. Mostly the floating plastic garbage directly affects the ecology and the aquatic animals. This paper proposes to develop a low-cost auto robot from waste materials in the household, school, or agriculture, then combines with the Internet of Things technology and focusing on the power energy saving with the highest accuracy in operation of floating garbage collection.  ESP32 development kit board is the primary controller with a camera module and ultrasonic sensor for detecting the floating garbage while moving on the water’s surface in the boundary of the specified area, including the small canal, rectangle area, and no rectangle area. The mixed mode is a new method to define the shortest one-way path using a fast approximate nearest neighbor search for the auto robot to detect and collect the floating garbage on the water surface. It was combined between the stationary and the cover model. The mixed mode can eliminate the blind spot area that occurs when using the stationary mode and cover mode. As a result, the auto robot system’s accuracy in mixed mode is 98.3%. It was found that the development of the shortest one-way path for the auto robot collecting floating garbage by mixed mode provides the highest efficiency in detecting floating garbage and also helps to save overall costs, and the resource consumption is nearly half of cover mode consumption.


Auto robot; energy saving; floating garbage; image processing; internet of things; low-cost; shortest one-way path.

Full Text:



World economic forum. (2019). We must stop choking the ocean with plastic waste. Here’s how. [Online]. Available: https://www.weforum.org/agenda/2019/01/we-can-stop-choking-our-oceans-with-plastic-waste-heres-how/

Surfers against sewage. (2019). Plastic pollution- facts and figures. [Online]. Available: https://www.sas.org.uk/our-work/plastic-pollution/plastic-pollution-facts-figures/

J. Rahlff et al., “Oxygen profiles across the sea-surface microlayer–effects of diffusion and biological activity,” Frontiers in Marine Science, vol. 6, pp. 11, 2019, doi: 10.3389/fmars.2019.00011.

F. M. Windsor et al., “A catchment-scale perspective of plastic pollution,” Global Change Biology, vol. 25, no. 4, pp. 1207–1221, 2019, doi: 10.1111/gcb.14572.

C. Romera-Castillo, M. Pinto, T. M. Langer, X. A. Álvarez-Salgado, and G. J. Herndl, “Dissolved organic carbon leaching from plastics stimulates microbial activity in the ocean,” Nature Communications, vol. 9, pp. 1430, 2018, doi: 10.1038/s41467-018-03798-5.

L. Zhu, S. Zhao, T. B. Bittar, A. Stubbins, and D. Li, “Photochemical dissolution of buoyant microplastics to dissolved organic carbon: Rates and microbial impacts,” Journal of Hazardous Materials, vol. 383, pp. 121065, 2020, doi: 10.1016/j.jhazmat.2019.121065.

T. van Emmerik and A. Schwarz, “Plastic debris in rivers,” WIREs Water, vol. 7, no. 1, 2019, doi: 10.1002/wat2.1398.

D. Ó. Conchubhair et al., “Joint effort among research infrastructures to quantify the impact of plastic debris in the ocean,” Environmental Research Letters, vol. 14, no. 6, pp. 065001, 2019, doi: 10.1088/1748-9326/ab17ed.

X. Li et al., “Internet of Things to network smart devices for ecosystem monitoring,” Sci. Bull., vol. 64, no. 17, pp. 1234–1245, 2019, doi: 10.1016/j.scib.2019.07.004.

S. Liu et al., “The Heihe integrated observatory network: A Basin-scale land surface processes observatory in China,” Vadose Zo. J., vol. 17, no. 1, p. 180072, Jan. 2018, doi: 10.2136/vzj2018.04.0072.

J. Kang, R. Jin, X. Li, C. Ma, J. Qin, and Y. Zhang, “High spatio-temporal resolution mapping of soil moisture by integrating wireless sensor network observations and MODIS apparent thermal inertia in the Babao River Basin, China,” Remote Sens. Environ., vol. 191, pp. 232–245, 2017, doi: 10.1016/j.rse.2017.01.027.

D. Pasetto et al., “Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends,” Methods Ecol. Evol., vol. 9, no. 8, pp. 1810–1821, Aug. 2018, doi: 10.1111/2041-210X.13018.

X. Luo and J. Yang, “Water pollution detection based on hypothesis testing in sensor networks,” J. Sensors, vol. 2017, p. 3829894, 2017, doi: 10.1155/2017/3829894.

H. Zemrane, Y. Baddi, and A. Hasbi, “Internet of things Ad Hoc drones ecosystem,” Procedia Comput. Sci., vol. 175, pp. 716–722, 2020, doi: 10.1016/j.procs.2020.07.106.

M. S. U. Chowdury et al., “IoT based real-time river water quality monitoring system,” Procedia Comput. Sci., vol. 155, pp. 161–168, 2019, doi: 10.1016/j.procs.2019.08.025.

H. Jindal, S. Saxena, and S. S. Kasana, “A sustainable multi-parametric sensors network topology for river water quality monitoring,” Wirel. Networks, vol. 24, no. 8, pp. 3241–3265, 2018, doi: 10.1007/s11276-017-1532-z.

H. Cao, Z. Guo, S. Wang, H. Cheng, and C. Zhan, “Intelligent wide-area water quality monitoring and analysis system exploiting unmanned surface vehicles and ensemble learning,” Water, vol. 12, no. 3, pp. 681, 2020, doi: 10.3390/w12030681.

L. Kuski, E. Maia, P. Moura, N. Caetano, and C. Felgueiras, “Development of a decentralized monitoring system of domestic water consumption,” Energy Reports, vol. 6, pp. 856–861, 2020, doi: 10.1016/j.egyr.2019.11.019.

K. Saravanan, E. Anusuya, R. Kumar, and L. H. Son, “Real-time water quality monitoring using Internet of Things in SCADA,” Environ. Monit. Assess., vol. 190, no. 9, p. 556, 2018, doi: 10.1007/s10661-018-6914-x.

S. Nuanmeesri and L. Poomhiran, “Improvement of smart farm by using IoT for ornamental fishes and aquatic animals store,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 3, pp. 2201–2206, 2020, doi: 10.35940/ijitee.C8962.019320.

W. Chen et al., “Farm ponds in southern China: Challenges and solutions for conserving a neglected wetland ecosystem,” Sci. Total Environ., vol. 659, pp. 1322–1334, 2019, doi: 10.1016/j.scitotenv.2018.12.394.

X. Wang, L. Ma, and H. Yang, “Online water monitoring system based on ZigBee and GPRS,” Procedia Eng., vol. 15, pp. 2680–2684, 2011, doi: 10.1016/j.proeng.2011.08.504.

LINE Corporation. (2020). LINE. [Online]. Available: https://line.me/en/

LINE Corporation. (2020). LINE Notify API Document. [Online]. Avaliable: https://notify-bot.line.me/doc/en/

TaTaTaTan. (2019, July 25). Life on LINE 2019. [Online]. Available: https://www.whatphone.net/news/pr/line-converge-thailand-2019-life-on-line/

P. Vriend et al., “Rapid assessment of floating macroplastic transport in the Rhine,” Frontiers in Marine Science, vol. 7, pp. 10, 2020, doi: 10.3389/fmars.2020.00010.

N. K. A. Malik et al., “Variation of floatable litter load and its compositions captured at floating debris boom (FDB) structure,” Journal of Material Cycles and Waste Management, vol. 22, no. 6, pp. 1744–1767, 2020, doi: 10.1007/s10163-020-01065-8.

A. Xing, J. Fang, M. Gao, and C. Zhang, “Design of an unmanned boat system for floating garbage salvage and water quality monitoring based on OneNET,” Journal of Physics: Conference Series, vol. 1607, pp. 012062, 2020, doi: 10.1088/1742-6596/1607/1/012062.

M. Abrams. (2018). Remote robot cleans trash from water. [Online]. Available: https://www.asme.org/topics-resources/content/remote-robot-cleans-trash-water/

India Block. (2019). The ocean cleanup launches system to catch plastic waste in rivers. [Online]. Available: https://www.dezeen.com/2019/10/29/ocean-cleanup-interceptor-river-plastic-pollution/

L. Lebreton et al., “Evidence that the Great Pacific Garbage Patch is rapidly accumulating plastic,” Nature Research, vol. 8, pp. 4666, 2018, doi: 10.1038/s41598-018-22939-w.

J. Ma, Y. Liu, S. Zang, and L. Wang, “Robot path planning based on Genetic Algorithm fused with Continuous Bezier Optimization,” Comput. Intell. Neurosci., vol. 2020, p. 9813040, 2020, doi: 10.1155/2020/9813040.

B. Li, H. Liu, and W. Su, “Topology optimization techniques for mobile robot path planning,” Appl. Soft Comput., vol. 78, pp. 528–544, 2019, doi: 10.1016/j.asoc.2019.02.044.

J. Lee, A. S. Ab Ghafar, N. Mohd Nordin, F. A. Saparudin, N. Katiran, “Autonomous multi-function floor cleaning robot with zig zag algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 15, no. 3, pp. 1653, 2019, doi: 10.11591/ijeecs.v15.i3.pp1653-1661.

T. B. Asafa, T. M. Afonja, E. A. Olaniyan, and H. O. Alade, “Development of a vacuum cleaner robot,” Alexandria Eng. J., vol. 57, no. 4, pp. 2911–2920, 2018, doi: 10.1016/j.aej.2018.07.005.

S. Yatmono, M. Khairudin, H. S. Pramono, and A. Asmara, “Development of intelligent floor cleaning robot,” J. Phys. Conf. Ser., vol. 1413, p. 12014, 2019, doi: 10.1088/1742-6596/1413/1/012014.

P. S. Adithya, R. Tejas, V. Sai Varun, and B. N. Prashanth, “Design and development of automatic cleaning and mopping robot,” IOP Conf. Ser. Mater. Sci. Eng., vol. 577, p. 12126, 2019, doi: 10.1088/1757-899x/577/1/012126.

B. R. Chang, H.-F. Tsai, J.-L. Lyu, and T.-K. Yin, “Smart trash can robot system with integration of internet of things and mobile applications,” Sensors and Materials, vol. 31, no. 11, pp. 3495–3516, 2019, doi: 10.18494/SAM.2019.2563.

J. Bai, S. Lian, Z. Liu, K. Wang, and D. Liu, “Deep learning based robot for automatically picking up garbage on the grass,” IEEE Trans. Consum. Electron., vol. 64, no. 3, pp. 382–389, 2018, doi: 10.1109/TCE.2018.2859629.

B. Murdyantoro, D. S. Eka Atmaja, and H. Rachmat, “Application design of farmbot based on Internet of Things (IoT),” Int. J. Adv. Sci. Eng. Inf. Technol. Vol. 9 No. 4, pp. 1163–1170, 2019, doi: 10.18517/ijaseit.9.4.9483.

A. Anitha, “Garbage monitoring system using IoT,” IOP Conf. Ser. Mater. Sci. Eng., vol. 263, p. 42027, 2017, doi: 10.1088/1757-899x/263/4/042027.

D. V. B. Pragna, D. L. Reddy, and SVS Prasad, “IoT driven automated object detection algorithm for urban surveillance system in smart city,” International Journal of Engineering and Advanced Technology, vol. 8, no. 6S3, pp. 1687–1691, 2019, doi: 10.35940/ijeat.F1317.0986S319.

S. Nuanmeesri, “Development of low-cost auto robot for plastic floating garbage collection using IoT,” International Journal of Engineering and Advanced Technology, vol. 9, no. 2, pp. 3727–3732, 2019, doi:10.35940/ijeat.B4557.129219.

Espressif Systems. (2019). Espressif Documentation. [Online]. Available: https://www.espressif.com/en/support/documents/technical-documents/

M. Muja and D. G. Lowe, “Fast approximate nearest neighbors with automatic algorithm configuration,” Proc. of the Fourth International Conference on Computer Vision Theory and Applications, 2009, vol. 1, pp. 331–340.

K. Hajebi, Y. Abbasi-Yadkori, H. Shahbazi, and H. Zhang, “Fast approximate nearest-neighbor search with k-nearest neighbor graph,” Proc. of the Twenty-Second International Joint Conference on Artificial Intelligence, 2000, pp. 1312–1317.

D. A. Suju and H. Jose, “FLANN: Fast approximate nearest neighbour search algorithm for elucidating human-wildlife conflicts in forest areas,” Proc. of 2017 Fourth International Conference on Signal Processing, Communication and Networking, 2017, pp. 1–6.

S. An et al., “Quarter-point product quantization for approximate nearest neighbor search,” Pattern Recognit. Lett., vol. 125, pp. 187–194, 2019, doi: 10.1016/j.patrec.2019.04.017.

C. Fu, C. Xiang, C. Wang, and D. Cai, “Fast Approximate Nearest Neighbor search with the navigating spreading-out graph,” Proc. VLDB Endow., vol. 12, no. 5, pp. 461–474, 2019, doi: 10.14778/3303753.3303754.

J. Vargas Muñoz, M. A. Gonçalves, Z. Dias, and R. da S. Torres, “Hierarchical clustering-based graphs for large scale Approximate Nearest Neighbor search,” Pattern Recognit., vol. 96, p. 106970, 2019, doi: 10.1016/j.patcog.2019.106970.

A. Sengupta, V. Varma, M. S. Kiran, A. Johari, and R. Marimuthu, “Cost-effective autonomous garbage collecting robot system using Iot and sensor fusion,” International Journal of Engineering and Advanced Technology, vol. 9, no. 1, pp. 1–7, 2019, doi: 10.35940/ijitee.a3880.119119.

M. Muja and D. G. Lowe, “Scalable Nearest Neighbor algorithms for high dimensional Data,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 11, pp. 2227–2240, 2014, doi: 10.1109/TPAMI.2014.2321376.

L. Ai, J. Yu, Z. Wu, Y. He, and T. Guan, “Optimized residual vector quantization for efficient approximate nearest neighbor search,” Multimed. Syst., vol. 23, no. 2, pp. 169–181, 2017, doi: 10.1007/s00530-015-0470-9.

B. Fan, Q. Kong, B. Zhang, H. Liu, C. Pan, and J. Lu, “Efficient nearest neighbor search in high dimensional hamming space,” Pattern Recognit., vol. 99, p. 107082, 2020, doi: 10.1016/j.patcog.2019.107082.

X. Bai, C. Yan, H. Yang, L. Bai, J. Zhou, and E. R. Hancock, “Adaptive hash retrieval with kernel based similarity,” Pattern Recognit., vol. 75, pp. 136–148, 2018, doi: 10.1016/j.patcog.2017.03.020.

DOI: http://dx.doi.org/10.18517/ijaseit.11.2.13191


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development