Model Predictive Control of Unmanned Aerial Vehicle for Locust Detection and Bio-pesticide Spraying
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Date
2021-11
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Addis Ababa University
Abstract
Swarm of Locust are very harmful for food security, quality and quantity of agriculture products.
Ethiopia is one of the countries which is extensively affected by locust invasion. The locust
swarms have destroyed large swaths of food and pasture in Ethiopia which lead to famine and displacing
thousands of people from their home. Ethiopia battled the swarms by spraying pesticides
from air using helicopters leased from FAO. With this consideration, precise locust detection and
bio-pesticide spraying is significant for preventing locust plagues.
This thesis is going to focus on the design of Model Predictive Control of UAV for locust detection
and bio-pesticide Spraying. To accomplish this design: First the dynamics of the system was
understood then the mathematical model of the system was done and it was based on an agriculture
spray drone (JMR-X1400). The Newton-Euler formalism was used to model the dynamic system
and verified in Simulink. The flight controller is designed and MPC is implemented for this thesis.
For this non-linear dynamic system of a quad-copter NMPC (non-linear MPC) is chosen. Multiple
shooting method is selected to transform the optimal control problem to nonlinear program (NLP).
To solve the NLP, CasADi in MATLAB is used and the solver is Ipopt (Interior Point Optimizer).
The NMPC was able to control the quad-copter, which means the quad-copter was able to follow
the given reference trajectory with minimum control effort. Since the quad-copter is used to spray
pesticide, there will be a change in mass when it sprays. For this reason the Recursive Least Square
Estimation (RLSE) is used to estimate the mass change and the model can be updated using the
estimation. The proposed method works adequately. The RLSE was able to estimate the mass
change and the quad-copter was still able to track the reference.
Manual monitoring is a labor-intensive job and expensive for large farms. To tackle this problem,
image recognition have provided a promising solution for detecting pests. So for this thesis
Image recognition system is developed to detect and recognize the Locust swarm. Since it is an
Image classification, CNN is chosen and the programming language is in python. After passing
through different procedures the final training accuracy of the machine is 95:19%.
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Keywords
Locust Swarm, UAV, MPC, Multiple Shooting, Mass Variation, RLSE, Image Recognition, CNN