Agricultural UAV Obstacle Avoidance System Based on a Depth Image Inverse Projection Algorithm and B-Spline Curve Trajectory Optimization Algorithm

Abstract

As multirotor plant protection drones evolve, using them for pest control has emerged as a mainstream trend. Nevertheless, lemon orchards often feature trees planned in an unstructured manner, with irregularly distributed obstacles such as utility poles posing safety threats to the operational tasks of unmanned aerial vehicles (UAVs) used for plant protection. To address such issues, this study proposes a novel UAV obstacle avoidance system designed for lemon orchards. The system has two components: a sensing and mapping subsystem using a depth image inverse projection algorithm, and a path planning subsystem that utilizes B-spline curve trajectory optimization. The system comprises a hardware description and software integration of the UAV, a map construction algorithm to sense obstacles in front of the UAV, and a path planning algorithm for obstacle avoidance. Two experimental scenarios were developed to evaluate the system’s flight performance: a flight test using the Gazebo simulation platform and a real-world test in a lemon orchard. In the simulation results, the flight trajectory’s average deviation from the original path was 2.77 m, and the maximum yaw angular velocity reached 1.001 rad/s. In the real-flight experiments, the flight trajectory’s average deviation from the original path was 2.90 m, and the maximum yaw angular velocity reached 1.545 rad/s. Both the simulation and real-flight experiments demonstrate that the system is effective in avoiding obstacles and planning paths in lemon orchards, providing a safe, smooth, and stable flight trajectory that meets the operational safety requirements for plant protection UAVs.