An autonomous task assignment and decision-making method for coverage path planning of multiple pesticide spraying UAVs

Abstract

As the approaching of Agriculture 4.0 era and advancements of new technologies of Unmanned Aerial Vehicles (UAVs) and particularly quadcopter UAVs, plant protection quadcopters are becoming increasingly popular and practical in pesticide spraying, fertilization, pollination, seeding, and other agricultural activities. One of the main problems for plant protection quadcopters is completing planning tasks efficiently and quickly. Therefore, this paper proposes an autonomous task assignment and decision-making method for coverage path planning by multiple cooperative quadcopters. The Sequential Quadratic Programming (SQP) method is adopted to acquire the optimal solution for the proposed problem. Then, a simulation platform by MATLAB Graphical User Interfaces (GUIs) is established using the Stateflow technique, and multiple ZY-UAV-680 quadrotor UAVs are employed to carry out the actual flight tests. Finally, simulations and actual flight tests are conducted to demonstrate the effectiveness of the autonomous optimal task assignment and decision-making method. The final results show that: the proposed method can divide multiple UAVs reasonably to several blocks; the time differences between the simulation test and real flight test are only 39.8 sec and 20.6 sec, and accounts for 6.6% and 3.9% of the spraying time spent on real flight test by three cooperative quadrotors, respectively; the optimal scheme can save 60.8 sec and 80.0 sec in the simulation tests and the real flight tests, which accounts for 10.8% and 13.2% of the time spent on average scheme, respectively. Therefore, it can be concluded that the simulation results can match the real flight test results quite well regardless of average scheme or optimal scheme, and the optimal scheme is more efficient and timesaving than the average scheme.