An autonomous vehicle control system includes one or more sensors configured for coupling with an agricultural vehicle, the one or more sensors configured to determine kinematics of the agricultural vehicle relative to a crop row. The system includes a guidance control module configured to coordinate steering of one or more steering mechanisms of the agricultural vehicle. The guidance control module includes a sensor input configured to receive kinematics of the agricultural vehicle, a vehicle kinematics comparator configured to determine one or more error values using the received vehicle kinematics, a crop curvature generator configured to determine crop row curvature using the one or more error values, and a steering interface configured to provide instructions to a vehicle steering controller to guide the agricultural vehicle using the crop row curvature.

A sensor arrangement for an agricultural vehicle includes a first electro-optical sensor including a first field of view having an optical axis, and a second electro-optical sensor including a second field of view having an optical axis. The first and second sensors are spaced apart from one another and oriented such that the optical axes of the two sensors intersect at a distance from the two sensors.

Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.

Aspects of the present disclosure are presented for a multi-purpose robot. In certain implementations, the robot of the present disclosure can initiate performance of one or more tasks. Aspect(s) of the power consumption of the robot can be monitored. Input(s) originating from sensor(s) of the robot can be received. Based on the aspect(s) of the power consumption of the robot and input(s) originating from the sensor(s), aspect(s) of the performance of the one or more tasks can be adjusted.

An agricultural machine includes a vehicle body, a travel switch operable to issue a command to start autonomous travel of the vehicle body, and an autonomous travel controller to perform autonomous travel of the vehicle body based on a planned travel line when the command is issued. When the command is issued by operating the travel switch, if at least one of a positional deviation between the planned travel line that is selected and the vehicle body and an orientational deviation between the planned travel line and an orientation of the vehicle body is greater than or equal to a corresponding one of respective first thresholds, the autonomous travel controller is configured or programmed to perform line alignment to make the positional deviation and the orientational deviation less than the respective first thresholds.

An agricultural machine includes a vehicle body, an obstacle detector to detect obstacles, an autonomous travel controller to perform autonomous travel of the vehicle body, the autonomous travel controller being configured or programmed to, when performing the autonomous travel, stop the vehicle body based on detection information about an obstacle detected by the obstacle detector, and a mode switch to switch a mode during the autonomous travel in an agricultural field between an effective mode in which the stopping of the vehicle body based on the detection information is allowed and an ineffective mode in which the stopping of the vehicle body based on the detection information is not allowed.

An automatic travel system for a work vehicle has an automatic travel control unit that causes a work vehicle, which is provided with an obstacle detection unit that detects obstacles, to travel automatically. The obstacle detection unit includes imaging units and active sensors. When the obstacle detection unit detects an obstacle based on information from the imaging units, and the separation distance from the obstacle is acquired based on information from the active sensors, the automatic travel control unit executes a first collision avoidance control for avoiding a collision between the work vehicle and the obstacle, on the basis of the separation distance from the obstacle acquired based on the information from the active sensors.

The invention relates to a method for cultivating row crops (200), comprising the following steps: sensor and satellite based recording of locations and/or courses of rows of plants (202a-202l ) while a first cultivation measure is carried out on a row crop (200) by means of an agricultural plant cultivation device (10) and controlling the movement and/or operation of an agricultural plant cultivation device (10) while a second cultivation measure is carried out on the row crop (200) based on the recorded locations and/or courses of the rows of plants (202a-2021).

Providing an object detection engine, training the object detection engine to identify a weed, training the object detection engine to identify a crop, providing an image from a sensor to the object detection engine, discerning with the object detection engine the weed from the crop, and plotting a path from the weed to a spot spray assembly upon identification of the weed by the object detection engine.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.