This automatic travel system for a work vehicle is provided with: a position information obtaining unit; and an automatic travel control unit that causes a work vehicle to automatically travel along a target path. The automatic travel control unit sets a control target position on the target path including a plurality of work paths arranged in parallel with each other and a plurality of turning paths that connect the work paths in an order of travel of the work vehicle, to enable automatic travel of the work vehicle along the target path. The automatic travel control unit, when the work vehicle is positioned on a work path in the vicinity of a boundary with a turning path, sets the control target position on an extension of the work path. The automatic travel control unit, when the work vehicle is positioned on a turning path in the vicinity of a boundary with a work path, sets the control target position on the work path.

This autonomous traveling control system is capable of generating, in a headland region which is in a farm field surrounded by peripheries formed from a plurality of edges and which is formed between the peripheries and a work region in which a work vehicle works while traveling autonomously, a headland traveling auxiliary route along which the work vehicle travels autonomously. The autonomous traveling control system selects, from among the plurality of edges, a selective edge for generating the headland traveling auxiliary route, on the basis of the distance between the edges and the vehicle position of the work vehicle and of the angles formed between the edges and the vehicle orientation of the work vehicle.

This invention relates to a system of lawn mowing and caring services. It comprises a lawn profile data information management system having lawn profile data stored therein; at least one mobile device for registered users, which communicates wirelessly with the lawn profile data information management system for requesting services; and at least one robotic lawn mower or lawn robot which works with the mobile device to acquire requisite lawn profile data from the lawn profile data information management system for the services before the robotic lawn mower or lawn robot can perform the requisite mowing or caring services on the specific piece of lawn.

An approach is provided for biasing machine learning models towards potential risks for controlling vehicles/robots. The approach involves, for example, determining an occluded space that is occluded in sensor data collected from one or more sensors of a vehicle or a robot. The approach also involves generating a sensor space completion that represents the occluded space based on biasing a generation of one or more potential risks to the vehicle or the robot originating from the occluded space. The approach further involves providing the sensor space completion to a system of the vehicle or the robot for generating a control decision, a warning, or a combination thereof.

A system dynamically generates a cleaning coverage pattern of an area using waypoints and sensor data from one or more sensor modalities. To do this, a robotic cleaning device moves through an area to be cleaned, identifies consecutive waypoints through the area, and stores the waypoints in a memory. At least one sensor of the robotic cleaning device collects sensor data about a portion of the area as the device moves between the consecutive waypoints. A temporary map of the portion of the area between the consecutive waypoints is generated based on the collected sensor data, and a cleaning coverage pattern is generated using the temporary map. The area is then cleaned by moving the robotic cleaning device according to the cleaning coverage pattern. In certain embodiments, upon completing the cleaning, the consecutive waypoints are retained in the memory, while the temporary map may not be retained.

Provided herein is a system of a vehicle that comprises one or more sensors, one or more processors, and memory storing instructions that, when executed by the one or more processors, causes the system to perform: selecting a trajectory along a route of the vehicle; predicting a trajectory of another object along the route; adjusting the selected trajectory based on a predicted change, in response to adjusting the selected trajectory, to the predicted trajectory of the another object, the predicted change to the predicted trajectory of the another object being stored in a model; determining an actual change, in response to adjusting the selected trajectory, to a trajectory of the another object, in response to an interaction between the vehicle and the another object; updating the model based on the determined actual change to the trajectory of the another object; and selecting a future trajectory based on the updated model.

In a method of making work plans for construction machinery, information of the construction machinery at a work site is obtained. The information of the construction machinery is received via a wireless communication. The information of the construction machinery is displayed on a display portion of a server. A work plan of the construction machinery is created on the display portion of the server by using the information of the construction machinery and information stored in the server. The work plan is transmitted to the construction machinery.

A closed-loop detecting method using an inverted index-based key frame selection strategy, storage media and apparatus are provided. The method includes following steps: step I: acquiring image information at a current position, processing the image information to extract corresponding image features therefrom and solve a camera pose; step II: capturing image features successively during movement of a robot, as consecutive image frames, performing, on the consecutive image frames, an indexing, in which the inverted index-based key frame selection strategy is introduced into a key frame selection strategy to supplement key frames which are prone to be missed in a conventional forward indexing during a curvilinear movement of the robot; and step III: performing closed-loop detection and correction of accumulative errors based on image features carried by key frames.

Systems, computer program products, and methods for constructing models and simulations of real-world environments are described. A robot employs various sensors to collect data from its environment and provides this data to a tele-operation system. Any number of tele-artists may access the tele-operation system and use the robot sensor data to collaboratively construct a simulated scene representative of the robot's environment. The tele-artists may continue to update the simulation in real-time as the robot explores its environment and provides more sensor data. The robot may use the simulation in support of fundamental operations through its cognitive architecture, such as action planning and hypothesis generation.

An artificial intelligence controller of the robot may monitor the adaptations made to the simulation by the tele-artists in response to the sensor data in order to learn (e.g., via reinforcement learning) how to autonomously generate and update its own simulation based on its own sensor data.

An agricultural support system includes a first communication device located in or on a tractor to transmit information, and a second communication device located in or on a transport vehicle to acquire the information transmitted from the first communication device. The transport vehicle is capable of transporting the tractor. The agricultural support system further includes a position sensor in or on the working machine to detect a position of the working machine. The first communication device is operable to transmit the position detected by the position sensor to the second communication device as the information. The second communication device is operable to receive the position transmitted from the first communication device.