Rumex and Urtica detection in grassland by UAV

. Previous work (Binch & Fox, 2017) used autonomous ground robotic platforms to successfully detect Urtica (nettle) and Rumex (dock) weeds in grassland, to improve farm productivity and the environment through precision herbicide spraying. It assumed that ground robots swathe entire fields to both detect and spray weeds, but this is a slow process as the slow ground platform must drive over every square meter of the field even where there are no weeds. The present study examines a complimentary approach, using unmanned aerial vehicles (UAVs) to perform faster detections, in order to inform slower ground robots of weed location and direct them to spray them from the ground. In a controlled study, it finds that the existing state-of-the-art (Binch & Fox, 2017) ground detection algorithm based on local binary patterns and support vector machines is easily re-usable from a UAV with 4K camera despite large differences in camera type, distance, perspective and motion, without retraining. The algorithm achieves 83-95% accuracy on ground platform data with 1-3 independent views, and improves to 90% from single views on aerial data. However this is only attainable at low altitudes up to 8 feet, speeds below 0.3m/s, and a vertical view angle, suggesting that autonomous or manual UAV swathing is required to cover fields, rather than use of a single high-altitude photograph. This demonstrates for the first time that combined aerial detection with ground spraying system is feasible for Rumex and Urtica in grassland, using UAVs to replace the swathing and detection of weeds then dispatching ground platforms to spray them at the detection sites (as spraying by UAV is illegal in EU countries). This reduces total time requires to spray as the UAV performs the survey stage faster than a ground platform

Context Dependant Iterative Parameter Optimisation for Robust Robot Navigation

Progress in autonomous mobile robotics has seen significant advances in the development of many algorithms for motion control and path planning. However, robust performance from these algorithms can often only be expected if the parameters controlling them are tuned specifically for the respective robot model, and optimised for specific scenarios in the environment the robot is working in. Such parameter tuning can, depending on the underlying algorithm, amount to a substantial combinatorial challenge, often rendering extensive manual tuning of these parameters intractable. In this paper, we present a framework that permits the use of different navigation actions and/or parameters depending on the spatial context of the navigation task, while considering the respective navigation algorithms themselves mostly as a "black box", and find suitable parameters by means of an iterative optimisation, improving for performance metrics in simulated environments. We present a genetic algorithm incorporated into the framework and empirically show that the resulting parameter sets lead to substantial performance improvements in both simulated and real-world environments in the domain of agricultural robots