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

2025-04-03

AI Summary

To address inefficient UAV search and redundant path planning caused by non-uniform target distribution in farmland, this paper proposes a detection-uncertainty-driven adaptive hierarchical path planning method. It first performs a coarse-grained, high-altitude full-coverage survey, then dynamically triggers low-altitude fine-grained re-inspection based on real-time YOLOv8 detection confidence scores. Crucially, it introduces the first closed-loop integration of detection uncertainty into trajectory decision-making, enabling distribution-aware online path optimization. To enhance robustness, localization errors are explicitly modeled in simulation. Experimental results demonstrate that, compared to exhaustive low-altitude coverage, the proposed method significantly reduces total flight distance by 37.2% on average in non-uniform scenarios, while maintaining comparable detection accuracy (mAP@0.5 degradation <0.8%). The implementation code is publicly available.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Intelligent Robots: Motion and Path Planning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Adaptive UAV path planning for efficient object search in agriculture
• Optimizing detection certainty and flight parameters for varied object distributions
• Robustness against localization errors in dynamic agricultural environments

Innovation

• UAV uses adaptive high-low altitude path planning
• YOLOv8 detects objects with confidence-based inspections
• Optimized parameters for non-uniform object distribution