A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV's Operation Scenario  
[PERSON]  
1 School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;  
[PERSON] [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (F.A. ); [EMAIL_ADDRESS] (H.X)  
[PERSON]  
1 School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;  
[PERSON] [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (F.A. ); [EMAIL_ADDRESS] (H.X)  
[PERSON]  
1 School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;  
[PERSON] [EMAIL_ADDRESS] (S.A.); [EMAIL_ADDRESS] (F.A. ); [EMAIL_ADDRESS] (H.X)  
[PERSON]  
4 School of Aerospace Engineering, University of Michigan, 1320 Beal Avenue, Ann Arbor, MI 48109, USA;  
srin1 School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China;
promising significant cost reduction, and increasing crop yield [20; 21]. However, in order for IoT sprayer UAVs to be able to perform these tasks, they demand certain characteristics that differ significantly from other applications [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. For instance, pesticide spraying with a manned UAV, especially at night or dark, mainly relies on the observation and judgement of a human pilot. Since the judgement of a manned pilot is often prone to a larger error margin, the fundamental objectives of the operation (which is precise spraying) may not be achieved, and the procedure may at the same time be hazardous. To mitigate this limitation, autonomous sprayer UAVs have been widely employed. Nonetheless, during sprayer UAV maneuvering in such unfavorable environments, the presence of obstacles, such as heavy-duty tools, mobile vehicles, and robots, have to be considered in order to avoid collisions [36], while still maintaining \"precise spraying\" and \"safety\" of the UAV. The fundamental task of the sprayer UAV, thus, becomes that of \"intelligent technology operation\", whereby the UAV identifies the obstacles autonomously and finishes the specified collision avoidance actions with a smooth maneuver. In other words, the sprayer UAV system should autonomously and efficiently avoid all types of obstacles in the flight path, ensure spray coverage, prevent accidents caused by heavy load oscillation, avoid autonomous flight catastrophes, and efficiently reduce the loss of property and human fatality. How to effectively perform the abovementioned task is mainly dependent on the obstacle detection and collision avoidance algorithms incorporated into the sprayer UAV system. Hence, this article seeks to analyze the latest developments on all correlated branches of the obstacle avoidance scenarios of agricultural sprayer UAVs and includes the following main contributions. 1. The most relevant obstacle detection and collision avoidance techniques are reviewed and discussed, as well as their application perspective with agricultural sprayer UAVs. 2. The latest path planning algorithms that are used in agricultural UAVs and the structural challenge of sprayer UAVs are described. 3. The operational pattern, detection sensors, obstacles in agricultural farmlands, and control architecture for collision avoidance are thoroughly highlighted to pave the way for future researchers to design their own agricultural sprayer UAV systems. 4. The core open challenges and recent technical limitations associated with agricultural sprayer UAVs are enumerated. The rest of this article is organized as follows. Section 1.1 introduces the general background and related work on obstacle detection and collision avoidance of UAVs. Section 2 analyses the constraints of agricultural sprayer UAVs. A review of the operational pattern and the general spraying architecture of sprayer UAVs, including farmland obstacles, is also presented in this section. In Section 3, details of the various obstacle detection and collision avoidance algorithms, including the various sensing techniques, are presented.