A work management system for managing work to be performed on a work subject. The work management system includes multiple work robots, a storage unit, a generator, and an instruction unit. The work robots each include movement means capable of moving to any location. The storage unit stores target information on a target state of the work subject and current state information on a current state of the work subject. The generator generates work procedure information indicating a work procedure to be performed by the work robots so that the work subject is brought close to the target state, on the basis of the target information and the current state information. The work procedure information includes work instruction information for instructing the work robots to perform one or more types of work to be performed on the work subject.

There is provided a server and a system capable of enabling an operator of a remote operating device to recognize which remote operating device remotely operates a work machine displayed on an output interface constituting the remote operating device. For example, a first work environment image indicating a situation of a work site acquired through an image pickup device 412 loaded on a work machine 40 is outputted on an output interface 220 constituting each of a plurality of remote operating devices 20.

To provide an autonomous traveling control method for a crawler vehicle capable of accurately computing a predicted slide-down amount of a crawler vehicle when the crawler vehicle travels on a slope, and enabling an autonomous traveling control based on the predicted slide-down amount. An autonomous traveling control method for a crawler vehicle includes the steps of setting a target trajectory of a crawler vehicle; and computing a predicted slide-down amount of the crawler vehicle when the crawler vehicle travels on a slope on the basis of a target trajectory, using a center of gravity position of the crawler vehicle, an angle of the slope and a traveling direction of the crawler vehicle in the slope.

A robotic vehicle (10) may include a chassis supporting a storage tank in which an aqueous solution is contained, a mobility assembly operably coupled to the chassis to provide mobility for the robotic vehicle about a service area (20), a positioning module (60) configured to provide guidance for the robotic vehicle (10) during transit of the robotic vehicle (10) over the service area (20), a spray assembly (90) and control circuitry (12).

An unmanned vehicle control method for setting a permitted area where traveling is permitted for each unmanned vehicle, the unmanned vehicle control method includes: acquiring unmanned vehicle data including position data of the unmanned vehicle; acquiring road surface condition data of a travel path on which the unmanned vehicle travels; and generating data including a permitted area in the travel path of the unmanned vehicle, a stop point in the permitted area, and a target traveling speed for the unmanned vehicle to stop at the stop point on a basis of the unmanned vehicle data that has been acquired, wherein the permitted area is set on a basis of the road surface condition data of a predetermined area including the stop point.

A vehicle coordination system is provided for coordinating the trajectories of vehicles on a road network. The vehicle coordination system comprises a plurality of vehicles each having respective vehicle position tracking assemblies that are in communication with respective vehicle communication systems for transmitting vehicle state messages including positions of the vehicles. A task assignment allocator is provided that is arranged to generate task assignments for each of the plurality of vehicles, including destinations in the road network for the vehicles. A vehicle coordination assembly is in communication with the vehicle communication systems via a data network for receiving the vehicle state messages. The vehicle coordination assembly is configured to determine respective paths for each vehicle to arrive at their respective destinations and determine trajectory control commands for each vehicle to traverse their respective paths whilst optimizing a predetermined objective and avoiding active interactions of two or more of the vehicles occurring in any shared areas of the paths. The vehicle coordination assembly is configured to transmit the trajectory control commands to each vehicle. The predetermined objective may be an aggregate traversal time for the vehicles.

A device may receive machine data indicating a pose of one or more components of a machine. The machine data may be generated based on first data from one or more first sensor devices associated with the machine. The device may generate validation data to validate the machine data. The validation data may be generated based on second data from a second set of sensor devices. The device may perform a comparison of the machine data and the validation data and determine, based the comparison, whether the machine data is validated or not validated. The device may selectively control an operation of the machine based on the machine data. Based on determining that the machine data is validated, the operation may be controlled based on the machine data. The operation may be controlled without the machine data based on determining that the machine data is not validated.

The present disclosure relates generally to techniques for the kinematic estimation and dynamic behavior estimation of autonomous heavy equipment or vehicles to improve navigation, digging and material carrying tasks at various industrial work sites. Particularly, aspects of the present disclosure are directed to obtaining a set of sensor data providing a representation of operation of an autonomous vehicle in a worksite environment, estimating, by a trained model comprising a Gaussian process, a set of output data based on the set of sensor data, controlling an operation of the autonomous vehicle in the worksite environment using input data derived from the set of sensor data and the set of output data, obtaining actual output data from the operation of the autonomous vehicle in the worksite environment, and updating the trained model with the input data and the actual output data.

The present application provides a method for autonomously controlling a vehicle performed by a control system of the vehicle on the basis of a mission received from a mission controller, the method comprising: receiving a mission comprising a set of instructions from the mission controller; validating the mission by checking whether the mission meets a first set of requirements; executing the mission if the mission meets the first set of requirements and rejecting the mission if the mission does not meet the first set of requirements.

An autonomous earth moving system can determine a desired state for a portion of the EMV including at least one control surface. Then the EMV selects a set of control signals for moving the portion of the EMV from the current state to the desired state using a machine learning model trained to generate control signals for moving the portion of the EMV to the desired state based on the current state. After the EMV executes the selected set of control signals, the system measures an updated state of the portion of the EMV. In some cases, this updated state of the EMV is used to iteratively update the machine learning model using an online learning process.