Adaptive path planning for efficient object search by UAVs in agricultural fields

This paper presents an adaptive path planner for object search in agricultural fields using UAVs. The path planner uses a high-altitude coverage flight path and plans additional low-altitude inspections when the detection network is uncertain. The path planner was evaluated in an offline simulation environment containing real-world images. We trained a YOLOv8 detection network to detect artificial plants placed in grass fields to showcase the potential of our path planner. We evaluated the effect of different detection certainty measures, optimized the path planning parameters, investigated the effects of localization errors and different numbers of objects in the field. The YOLOv8 detection confidence worked best to differentiate between true and false positive detections and was therefore used in the adaptive planner. The optimal parameters of the path planner depended on the distribution of objects in the field, when the objects were uniformly distributed, more low-altitude inspections were needed compared to a non-uniform distribution of objects, resulting in a longer path length. The adaptive planner proved to be robust against localization uncertainty. When increasing the number of objects, the flight path length increased, especially when the objects were uniformly distributed. When the objects were non-uniformly distributed, the adaptive path planner yielded a shorter path than a low-altitude coverage path, even with high number of objects. Overall, the presented adaptive path planner allowed to find non-uniformly distributed objects in a field faster than a coverage path planner and resulted in a compatible detection accuracy. The path planner is made available at https://github.com/wur-abe/uav_adaptive_planner.

Learning UAV-based path planning for efficient localization of objects using prior knowledge

UAV's are becoming popular for various object search applications in agriculture, however they usually use time-consuming row-by-row flight paths. This paper presents a deep-reinforcement-learning method for path planning to efficiently localize objects of interest using UAVs with a minimal flight-path length. The method uses some global prior knowledge with uncertain object locations and limited resolution in combination with a local object map created using the output of an object detection network. The search policy could be learned using deep Q-learning. We trained the agent in simulation, allowing thorough evaluation of the object distribution, typical errors in the perception system and prior knowledge, and different stopping criteria. When objects were non-uniformly distributed over the field, the agent found the objects quicker than a row-by-row flight path, showing that it learns to exploit the distribution of objects. Detection errors and quality of prior knowledge had only minor effect on the performance, indicating that the learned search policy was robust to errors in the perception system and did not need detailed prior knowledge. Without prior knowledge, the learned policy was still comparable in performance to a row-by-row flight path. Finally, we demonstrated that it is possible to learn the appropriate moment to end the search task. The applicability of the approach for object search on a real drone was comprehensively discussed and evaluated. Overall, we conclude that the learned search policy increased the efficiency of finding objects using a UAV, and can be applied in real-world conditions when the specified assumptions are met.