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.

翻译：无人机在农业等领域的各类目标搜索应用中日益普及，但通常采用耗时的逐行扫描飞行路径。本文提出一种深度强化学习方法，用于规划无人机飞行路径，以最短航迹长度高效定位感兴趣目标。该方法结合了目标位置不确定且分辨率有限的全局先验知识，以及通过目标检测网络输出构建的局部目标地图。搜索策略可通过深度Q学习进行训练。我们在仿真环境中训练智能体，从而全面评估目标分布、感知系统典型误差、先验知识误差及不同终止准则。当目标在田间非均匀分布时，智能体较逐行扫描路径能更快发现目标，表明其学会了利用目标分布特性。检测误差与先验知识质量对性能影响较小，说明习得的搜索策略对感知系统误差具有鲁棒性，且无需详尽的先验知识。在无先验知识情况下，习得策略的性能仍与逐行扫描路径相当。最后，我们证明了该方法能够学习确定终止搜索任务的最佳时机。本文全面讨论并评估了该方案在真实无人机目标搜索任务中的适用性。总体而言，学习得到的搜索策略提升了无人机目标搜索效率，在满足特定假设条件下可应用于实际场景。