Currently, an agricultural method called SynecocultureTM has been receiving attention as a means for multiple crop production and recovering from environmental degradation; it helps in regreening the environment and establishing an augmented ecosystem with high biodiversity. In this method, several types of plants are grown densely, and their management relies mainly on manual labor, since conventional agricultural machines and robots cannot be applied in complex vegetation. To improve work efficiency and boost regreening by scaling-up Synecoculture, we developed a robot that can sow, prune, and harvest in dense and diverse vegetation that grows under solar panels, towards the achievement of compatibility between food and energy production on a large scale. We adopted a four-wheel mechanism with sufficient ability to move on uneven terrain, and a two orthogonal axes mechanism with adjusted tool positioning while performing management tasks. In the field experiment, the robot could move straight on shelving slopes and overcome obstacles, such as small steps and weeds, and succeeded in harvesting and weeding with human operation, using the tool maneuver mechanism based on the recognition of the field situation through camera image.

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Currently, as measures against environmental destruction, an agricultural method called synecoculture TM has been received attention. However, since multiple types of plants grow densely in this method, conventional machines and robots can't intervene. Therefore, work efficiency is low. To improve work efficiency, we developed a robot with a wheel and linear mechanism. The wheel mechanism can move on uneven terrain, and the linear mechanism with two orthogonal axes can adjust tool position during task. In the field experiment, the robot moved on the field, and succeeded in harvesting and weeding by operating the linear mechanism based on the camera image.