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A path planning algorithm for plant protection UAV for avoiding multiple obstruction areas

conference contribution
posted on 2023-05-23, 13:40 authored by Liu, Y, Xu, Z, Li, N, Shuxiang XuShuxiang Xu, Gang, Y
China's farmland environment is complex. The planting area often contains some irregular obstacles. It is hard for plant protection UAV (Unmanned Aerial Vehicle) to get good planning results to avoid obstacles automatically in the autonomous mode or only through sensors. In order to improve the application scope for plant UAVs with autonomous operation mode and to obtain good operation results, this paper proposes Multi-Obstacle Area Avoidance (MOAA) algorithm which is a path planning algorithm for avoiding obstacle areas. This algorithm is proposed based on the method of cattle ploughing reciprocation. According to the information of heading, spouting, operating areas and obstacle areas, the internal route of the spraying area is obtained. The order of lines and waypoints are determined by choosing the shortest route. And this algorithm uses the Ray method to avoid the polygon obstacles and multi-obstacle areas. Operating results of different obstacle zones and heading angles are tested through simulation experiments. The best optimization radio is 14.2% when obstacle area is 800m². Then aiming at the problem of over-dispatching of routes in some cases, MOAA algorithm is further optimized. Actual optimization length in field experiments is 75 meters (optimization radio is 7.7%) when the heading angles is 315°. Field experimental results show that MOAA algorithm can select the best path scheme based on information such as obstacle height, area, etc.and it can improve the applicability of the plant protection UAV with autonomous operations.


Publication title

Proceedings of the 6th IFAC Conference on Bio-Robotics (BIOROBOTICS 2018)








School of Information and Communication Technology


Elsevier BV

Place of publication


Event title

6th IFAC Conference on Bio-Robotics (BIOROBOTICS 2018)

Event Venue

Beijing, China

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Date of Event (End Date)


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Copyright © 2018 IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

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Information systems, technologies and services not elsewhere classified

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