SIMULASI KESTABILAN PROTOTYPE UAV-SPRAYER BERBASIS QUADCOPTER TERHADAP PENAMBAHAN SEKAT PADA PENAMPUNG CAIRAN

Kris Hariyanto, Bangga Dirgantara

Submitted : 2022-04-15, Published : 2022-07-01.

Abstract

The use of UAVs has begun to penetrate the world of agriculture. One of the functions of UAVs in agriculture is to spray pesticides. The pesticides used are liquid so that when the UAV is airborne and maneuvering, the fluid experiences fluid motion or sloshing. Sloshing that occurs can cause the balance of the UAV to be disturbed. To overcome this, a bulkhead or baffle is needed in the reservoir in order to reduce fluid movement. In the case of the research studied, the simulation of sloshing in the reservoir with the presence of baffles and without the presence of baffles. This research uses different reservoir variations and different water levels, namely 55 mm, 35 mm and 15 mm. Simulations are carried out during cruising and maneuvering flights at a speed of 2 m/s. The container modeling uses the Catia V5R20 software and the simulation uses the Ansys 14.5 software. The simulation results show that the effect of baffle placement is more visible if the baffle is placed in the xy plane, while for the baffle placement in the yz plane, the force caused by sloshing is greater. In spraying the UAV-Sprayer will more often fly forward (cruise), while for maneuvers (right or left) it is only done occasionally/not too often. So that giving baffles is more effective in the xy plane because it can reduce sloshing better than the baffles in the yz plane.

Keywords

Container; Sloshing; Ansys CFX; UAV-Sprayer

Full Text:

PDF Plagiarism check

References

Sanca, A.S.; Alsina, P.J.; Jés de Jesus, F.C. Dynamic modelling of a quadrotor aerial vehicle with nonlinear inputs. In Proceedings

of the 2008 IEEE Latin American Robotic Symposium, Salvador, Brazil, 29–30 October 2008; IEEE: Piscataway, NJ, USA, 2008; pp.143–148.

Ryll, M.; Bülthoff, H.H.; Giordano, P.R. A novel overactuated quadrotor unmanned aerial vehicle: Modeling, control, and experimental validation. IEEE Trans. Control Syst. Technol. 2014, 23, 540–556.

Marino, S.; Alvino, A. Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices. Agronomy 2019, 9, 226.

Surový, P.; Almeida Ribeiro, N.; Panagiotidis, D. Estimation of positions and heights from UAV-sensed imagery in tree plantations in agrosilvopastoral systems. Int. J. Remote Sens. 2018, 39, 4786–4800.

Cilia, C.; Panigada, C.; Rossini, M.; Meroni, M.; Busetto, L.; Amaducci, S.; Boschetti, M.; Picchi, V.; Colombo, R. Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery. Remote Sens. 2014, 6, 6549–6565.

Zaman-Allah, M.; Vergara, O.; Araus, J.L.; Tarekegne, A.; Magorokosho, C.; Zarco-Tejada, P.J.; Hornero, A.; Alba, A.H.; Das, B.; Craufurd, P.; et al. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods 2015, 11, 35.

Chang, A.; Jung, J.; Maeda, M.M.; Landivar, J. Crop height monitoring with digital imagery from Unmanned Aerial System (UAS). Comput. Electron. Agric. 2017, 141, 232–237.

Honkavaara, E.; Kaivosoja, J.; Mäkynen, J.; Pellikka, I.; Pesonen, L.; Saari, H.; Salo, H.; Hakala, T.; Marklelin, L.; Rosnell, T. Hyperspectral reflectance signatures and point clouds for precision agriculture by light weight UAV imaging system. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci 2012, 7, 353–358.

Pflanz, M.; Nordmeyer, H.; Schirrmann, M. Weed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier. Remote Sens. 2018, 10, 1530.

Rasmussen, J.; Nielsen, J.; Garcia-Ruiz, F.; Christensen, S.; Streibig, J.C.; Lotz, B. Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Res. 2013, 53, 242–248.

Rahnemoonfar, M.; Sheppard, C. Deep Count: Fruit Counting Based on Deep Simulated Learning. Sensors 2017, 17, 905.

Lou, Z.; Xin, F.; Han, X.; Lan, Y.; Duan, T.; Fu, W. Effect of Unmanned Aerial Vehicle Flight Height on Droplet Distribution, Drift and Control of Cotton Aphids and Spider Mites. Agronomy 2018, 8, 187.

Xiao, Q.; Xin, F.; Lou, Z.; Zhou, T.;Wang, G.; Han, X.; Lan, Y.; Fu,W.J.A. Effect of aviation spray adjuvants on defoliant droplet deposition and cotton defoliation efficacy sprayed by unmanned aerial vehicles. Agronomy 2019, 9, 217.

Liu, A.; Zhang, H.; Llao, C.; Zhang, Q.; Cenglin, X.; Juying, H.; Zhang, J.; Yan, H.; Ll, J.; Xiwen, L.J.A.S. Technology, Effects of Supplementary Pollination by Single-rotor Agricultural Unmanned Aerial Vehicle in Hybrid Rice Seed Production. Agric. Sci. Technol. 2017, 18, 543–552.

Chen, S.; Lan, Y.; Li, J.; Xu, X.; Wang, Z.; Peng, B. Evaluation and test of effective spraying width of aerial spraying on plant protection UAV. Trans. Chin. Soc. Agric. Eng. 2017, 33, 82–90.

Wang, C.; He, X.; Wang, X.; Wang, Z.; Pan, H.; He, Z. Testing method of spatial pesticide spraying deposition quality balance for unmanned aerial vehicle. Trans. Chin. Soc. Agric. Eng. 2016, 32, 54–61.

Ahmed, S.; Qiu, B.; Ahmad, F.; Kong, C.-W.; Xin, H. A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario. Agronomy 2021, 11, 1069.

Ming, D. Study on the Equivalent Mechanical Model for Large Amplitude Slosh. J. Astronaut. 2016, 37, 631.

JIYI K++ Flight Controller. 16 June 2021. Available online: https://support.jiyiuav.com/docs/skning/skning-1c8jtpfthji51 (accessed on 12 December 2021).

Article Metrics

Abstract view: 240 times
Download     : 149   times Download     : 1   times

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

  • There are currently no refbacks.