Accurate robot localization relative to orchard row centerlines is essential for autonomous guidance where satellite signals are often obstructed by foliage.Existing sensor-based approaches rely on various features extracted from images and point clouds. However, any selected features are not available consistently, because the visual and geometrical characteristics of orchard rows change drastically when tree types, growth stages, canopy management practices, seasons, and weather conditions change. In this study, we introduce a novel localization method that does not rely on features; instead, it relies on the concept of a row-sensing template, which is the expected observation of a 3D sensor traveling in an orchard row, when the sensor is anywhere on the centerline and perfectly aligned with it. First, the template is built using a few measurements, provided that the sensor's true pose with respect to the centerline is available. Then, during navigation, the best pose estimate (and its confidence) is estimated by maximizing the match between the template and the sensed point cloud using particle-filtering. The method can adapt to various orchards and conditions by rebuilding the template. Experiments were performed in a vineyard, and in an orchard in different seasons. Results showed that the lateral mean absolute error (MAE) was less than 3.6% of the row width, and the heading MAE was less than 1.72°. Localization was robust, as errors did not increase when less than 75% of measurement points were missing. The results indicate that template-based localization can provide a generic approach for accurate and robust localization in real-world orchards.

Mechanizing the manual harvesting of fresh market fruits constitutes one of the biggest challenges to the sustainability of the fruit industry. During manual harvesting of some fresh-market crops like strawberries and table grapes, pickers spend significant amounts of time walking to carry full trays to a collection station at the edge of the field. A step toward increasing harvest automation for such crops is to deploy harvest-aid collaborative robots (co-bots) that transport empty and full trays, thus increasing harvest efficiency by reducing pickers' non-productive walking times. This study presents the development of a co-robotic harvest-aid system and its evaluation during commercial strawberry harvesting. At the heart of the system lies a predictive stochastic scheduling algorithm that minimizes the expected non-picking time, thus maximizing the harvest efficiency. During the evaluation experiments, the co-robots improved the mean harvesting efficiency by around 10% and reduced the mean non-productive time by 60%, when the robot-to-picker ratio was 1:3. The concepts developed in this study can be applied to robotic harvest-aids for other manually harvested crops that involve walking for crop transportation.

The cover image is based on the Research Article A strawberry harvest-aiding system with crop-transport collaborative robots: Design, development, and field evaluation by Chen Peng et al., https://doi.org/10.1002/rob.22106.

GPS-based navigation in orchards can be unstable because trees may block the GPS signal or introduce multipath errors. Most research on robot navigation without GPS has focused on guidance inside orchard rows; end-of-row detection has not received enough attention. Additionally, navigation between rows relies on reference maps or artificial landmarks. In this work, a novel row-end detection method is presented, which detects drastic changes in the statistical distribution of the sensed point cloud as the robot gets closer to the row’s end. A row-entry method was also implemented that builds a local map that is used by a reactive path tracker. The system was evaluated in a 24-row block in a vineyard. Once the robot was closer than 7 m from the end of a row, the algorithm detected it with a 100% success rate and calculated the distance from it with a mean error of 0.54 m. The system was also evaluated in vineyard configurations with parallel and slanted vine rows in consecutive blocks. The system worked well in all configurations, except where the next block had rows aligned to the rows of the current block and the headland width was closer than 5 m.