arXiv subjects

Rick van Essen

Publications and source records attributed to Rick van Essen.

3 recordsLinked to original sources

arXiv2025

A drone that learns to efficiently find non-uniformly distributed objects in agricultural fields: from simulation to the real world

Drones are promising for data collection in precision agriculture but are limited by battery capacity. Drone paths are usually planned using full coverage planners, even though this is not always required. This paper presents a drone path planner trained with Reinforcement Learning (RL) to detect as many objects as possible with a minimal flight path length. The agent uses low-quality prior knowledge derived from a high-altitude full coverage flight as guidance. The agent was trained in simulation, modeling object distributions, drone movement, field geometry, detection errors, and uncertain prior knowledge. Combined with a flight controller and object-detection network, it controls flight direction, terminates flights, and can be deployed on a real drone. It was evaluated across six levels of realism, from pure simulation to real-world drone flights, to quantify the simulation-to-reality gap. The agent achieved a 57% shorter flight path than a full coverage planner in simulation (13% lower recall) and a 38% shorter flight path on real-world orthomosaic data (21% lower recall). In real-world drone flights, the agent found 73% and 23% of the objects in trials 1 and 2, respectively; the lower real-world performance was mainly attributed to prior knowledge quality. Although framed as a weed-detection task, the approach is expected to generalize to other agricultural tasks with non-uniformly distributed objects and tolerance to false negatives, though further research is needed before practical deployment.

cs.RO↗

arXiv2025

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 a high number of objects. Overall, the presented adaptive path planner allowed finding 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.

cs.RO↗

arXiv2024

UAV-based path planning for efficient localization of non-uniformly distributed weeds using prior knowledge: A reinforcement-learning approach

UAVs are becoming popular in agriculture, however, they usually use time-consuming row-by-row flight paths. This paper presents a deep-reinforcement-learning-based approach for path planning to efficiently localize weeds in agricultural fields using UAVs with minimal flight-path length. The method combines prior knowledge about the field containing uncertain, low-resolution weed locations with in-flight weed detections. The search policy was learned using deep Q-learning. We trained the agent in simulation, allowing a thorough evaluation of the weed distribution, typical errors in the perception system, prior knowledge, and different stopping criteria on the planner's performance. When weeds were non-uniformly distributed over the field, the agent found them faster than a row-by-row path, showing its capability to learn and exploit the weed distribution. Detection errors and prior knowledge quality had a minor effect on the performance, indicating that the learned search policy was robust to detection errors and did not need detailed prior knowledge. The agent also learned to terminate the search. To test the transferability of the learned policy to a real-world scenario, the planner was tested on real-world image data without further training, which showed a 66% shorter path compared to a row-by-row path at the cost of a 10% lower percentage of found weeds. Strengths and weaknesses of the planner for practical application are comprehensively discussed, and directions for further development are provided. Overall, it is concluded that the learned search policy can improve the efficiency of finding non-uniformly distributed weeds using a UAV and shows potential for use in agricultural practice.

cs.RO↗