Field Robotics in Agriculture and Life Science
Intelligent autonomous spraying robot
-
A typical sprayer sprays a certain amount of pesticide in all directions considering the surrounding environment. As a result, various problems, such as acceleration of soil acidification and economic loss due to pesticide wastage are prevalent. To overcome these problems, we propose a deep learning-based intelligent spraying system that sprays pesticides only on fruit trees. The RGB image acquired by the camera is divided into a 4x4 area image to acquire the volume of a fruit tree in that area, and each mapped nozzle is controlled through an on/off control and variable flow control. Here, variable flow control applies only the optimum spraying amount according to the orchard environment such as the fruit and tree. Specifically, variable flow control maintains the performance of the conventional spraying method (i.e., all nozzles open) and sprays undesired pesticide to a minimum. Also, SLAM was also applied the intelligent spraying system so that the spraying can be self-driving in orchards. The proposed intelligent spraying system was validated in a pear orchard and, task allocation studies are underway to apply this system to multiple robots to increase spraying efficiency in large orchards.
Autonomous Fruit-Vegetable Harvesting Robot
-
Agricultural work requires a lot of manual labor, and therefore, research on harvesting robots to automate harvesting is active. Research on manual labor replacement with robots is usually focused on the robot’s ability for crop recognition and approaching the crop for harvesting. However, it is also important to develop an end-effector that can harvest a variety of fruits and vegetables. In this research, we propose the end-effector of a mechanism that can be stable during a harvest without additional and complex control for the various fruit and vegetables. To achieve the process of catching and cutting crops like humans, an end-effector was developed to suck fruits with suction cups and cut them with scissors or saws, in addition to research conducted on a plucking-type soft gripper that grabs and picks fruits. We designed an end-effector that includes structures that allow a stable catch for collecting falling crops after detachment from the pedicel, reducing the harvesting time and improving productivity
Autonomous UAV-based Active Tracking and Mapping of Small Insect
-
Tracking micro-sized insects to preserve ecosystems and biodiversity is one of the main challenges. To track the location of insects active in a wide area, robot-based active and autonomous tracking systems are required to improve the performance of traditional human-based passive tracking. Therefore, we propose a UAV-based active tracking and mapping system based on radio telemetry. This study introduces a total of three methods of tracking systems consisting of rotational UAV (RU), multi-antenna (MA), and rotational antenna (RA). First, the RU system estimates the relative distance and direction of the target based on the radio signal strength by fixing the directional Yagi-antenna to the rotating UAV. To address the low precision and slow speed of RU-based tracking, we developed the MA tracking system that estimates the three-dimensional position of the target using three omnidirectional whip antennas, consisting of two terrestrial and one aerial type. Finally, we designed the RA-based approach to simplify the tracking system while addressing the tracking distance, precision, and speed. This study covers how the system architecture was configured, which radio propagation models were used considering the insect tracking environments, which filters were designed to reduce measurement error, how radio signals from multiple antennas were synthesized, which control algorithms were developed for autonomous tracking of mobile robots, and which mechanisms were applied to rotational systems. The effectiveness of the proposed system for localization and autonomously tracking the target was verified and evaluated via dynamic simulation and field tests. This study presents a novel approach based on a mobile robot for active and autonomous tracking of micro-sized insects.
Autonomous UAV-based Active Tracking and Mapping of Small Insect
-
Korean cabbage is a head-type plant, similar to cabbage. And therefore, prior research has been conducted on single-row type harvester similar to a cabbage harvester; however, it is difficult to find a commercialized model based on this research. The reason is that while the stem position of a cabbage is located outside the head, meaning that there is a low possibility of damage to the head due to cutting, Korean cabbage has a stem position inside the head. Therefore, Korean cabbage must be cut at an accurate position, as there is a higher possibility of damage to the head. However, farmland environment is highly volatile and difficult to predict. In this research, a mechanism was designed for independently-controlled cutting devices during harvest. The proposed mechanism can move the roll and pitch angle and the x-axis length; non-linear control methods, such as backstepping control, are applied to reduce the degradation of hydraulic systems due to their dynamic properties. To verify the proposed system, a field test was conducted in an actual Korean cabbage field. We harvested sixty heads in total, and as a result, high cutting performance was confirmed with 90.7 out of 100 points. Furthermore, it was confirmed that attitude control was adjusted according to the cabbage field environment. The cutting attitude was maintained within the optimal cutting position range. The proposed backstepping-based attitude control system allows accurate Korean cabbage harvesting and is expected to improve the harvest success rate.