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.

A retrofit kit for a power machine can include a detection module configured to be removably secured to the power machine to detect objects around the power machine. A control module can be configured to receive detected object data from the detection module and control the display module based on the detected object data to provide one or more indicators of an object detected by the detection module.

Methods and systems for autonomous and semi-autonomous vehicle control, routing, and automatic feature adjustment are disclosed. Sensors associated with autonomous operation features may be utilized to search an area for missing persons, stolen vehicles, or similar persons or items of interest. Sensor data associated with the features may be automatically collected and analyzed to passively search for missing persons or vehicles without vehicle operator involvement. Search criteria may be determined by a remote server and communicated to a plurality of vehicles within a search area. In response to which, sensor data may be collected and analyzed by the vehicles. When sensor data generated by a vehicle matches the search criteria, the vehicle may communicate the information to the remote server.

Methods, apparatus, systems and articles of manufacture to detect changes in a physical environment are disclosed. An example apparatus includes a descriptor generator to generate a first descriptor, the descriptor generator including: a chirp producer to emit a chirp into the environment, a chirp recorder to record a response to the chirp from the environment, and a chirp response encoder to generate an encoding of the response to the chirp; a descriptor similarity generator to generate a similarity value, the similarity value to compare the first descriptor to a second descriptor; and a physical change indicator to, in response to the similarity value exceeding a similarity threshold, indicate that a physical change has occurred in the environment.

The present disclosure provides a cleaning method for the water surface of swimming pools and cleaning robot, the cleaning robot comprising a cleaning robot body, a sonar arranged around the cleaning robot body, two rear thrusters located at the tail of cleaning robot body. According to the present disclosure, as long as the cleaning robot body is placed on the water surface of the pool, cleaning robot body floating on the surface of the pool may automatically move and steer, so as to ensure that it can turn in advance before encountering the pool wall and won't knock against the pool wall, reduce the probability of malfunction and damaging of cleaning robot body, and the cleaning robot body can cover the entire water surface of pool and the pool wall, there is no omitting of cleaned water surface, and there is no need for excessive human involvement, it makes it easy for cleaning staffs to clean, when the cleaning robot body cleans the water surface of the pool, the cleaning staffs can carry out other cleaning work, which improved the cleaning efficiency of the cleaning staffs.

An autonomous moving apparatus includes a moving unit moving the apparatus, a detector detecting distances from surrounding objects and shapes of the objects, and a controller. When a route of the apparatus is adjusted to a route of a follow target under control of the moving unit, the controller controls the moving unit so that the apparatus continues to move without changing the route of the apparatus if the follow target has changed the route but an obstacle having a possibility of causing a movement abnormality when the apparatus moves over the obstacle is present between the route of the follow target and the route of the apparatus. When the obstacle is no longer present between the route of the follow target and the route of the apparatus, the controller controls the moving unit so that the route of the apparatus is adjusted to the route of the follow target.

A method of localizing a mobile body (MB) in a known environment, includes the following steps: a) defining an occupancy grid (G) modeling the environment; b) defining a set of position grids (Π) each position grid being associated to a heading of the mobile body; c) receiving a time series of measurements (z.sub.1, z.sub.2, . . . ) from a distance sensor carried by the mobile body; and d) upon receiving a measurement of the time series, updating the pose probabilities of the position grids as a function of present values of the occupancy probabilities and of the received measurement; wherein step d) is carried out by applying an inverse sensor model to the received measurement, while considering the distance sensor co-located with a detected obstacle and by applying Bayesian fusion to update the pose probabilities of the position grids.

In various examples, one or more output channels of a deep neural network (DNN) may be used to determine assignments of obstacles to paths. To increase the accuracy of the DNN, the input to the DNN may include an input image, one or more representations of path locations, and/or one or more representations of obstacle locations. The system may thus repurpose previously computed information—e.g., obstacle locations, path locations, etc.—from other operations of the system, and use them to generate more detailed inputs for the DNN to increase accuracy of the obstacle to path assignments. Once the output channels are computed using the DNN, computed bounding shapes for the objects may be compared to the outputs to determine the path assignments for each object.

Aspects of the disclosure relate generally to generating and providing route options for an autonomous vehicle. For example, a user may identify a destination, and in response the vehicle's computer may provide routing options to the user. The routing options may be based on typical navigating considerations such as the total travel time, travel distance, fuel economy, etc. Each routing option may include not only an estimated total time, but also information regarding whether and which portions of the route may be maneuvered under the control of the vehicle alone (fully autonomous), a combination of the vehicle and the driver (semiautonomous), or the driver alone. The time of the longest stretch of driving associated with the autonomous mode as well as map information indicating portions of the routes associated with the type of maneuvering control may also be provided.

One aspect of the invention relates to a method for signaling information to the operator of the remote control for a parking assistance system which can be controlled by remote control from outside of a motor vehicle for automatically parking the motor vehicle into a parking space. The method includes detecting surroundings information with respect to the vehicle surroundings on the motor vehicle side using a surrounding sensor system of the motor vehicle. The parking assistance system in the motor vehicle detects a parking space on the basis of the surroundings information, and information on the detection of a parking space is transmitted from the motor vehicle to the remote control via a wireless communication connection. On the basis of the received information, the remote control signals the presence of the detected parking space to the operator.