A system for autonomously measuring workpieces, the system comprising one or more mobile robots, configured to move autonomously in a production environment with a plurality of production facilities that produce a plurality of different workpieces, each of the mobile robots comprising a spatial localization system for deriving a location of the mobile robot in the production environment, an autonomous navigation and propulsion unit configured for providing mobility of the mobile robot in the production environment, a wireless communication interface providing a data link to at least one other mobile robot and/or to computation and storage system, wherein a first mobile robot comprises a sensor setup comprising one or more sensors and is configured to use one or more of the sensors for identifying a workpiece to be measured and for determining an at least rough position of the workpiece that allows collecting or measuring the workpiece.

A management system includes a course data generation unit that generates course data for each of a plurality of unmanned vehicles such that loading work for the plurality of unmanned vehicles by a loader is sequentially performed on a work site where a plurality of the loaders operates; and a priority determination unit that determine a passage order at an intersection on the work site of the plurality of unmanned vehicles traveling according to the course data so as to reduce a total loading loss indicating a total of loss amounts in operation of each of the plurality of the loaders.

Systems and methods of the present disclosure leverage distributed ledger technology (DLT) to provide decentralized control of cooperative tasks performed by a plurality of robots. Characteristics of the plurality of robots may be stored in a distribute ledger, which may be provided by a blockchain or a distributed database system. When a service request is received, a set of tasks may be identified for providing the requested service and the robot characteristics recorded to the distributed ledger may be used to identify a list of candidate robots possessing characteristics corresponding to the set of tasks may be identified. A smart contract may be utilized to select one or more candidate robots for performing the task and to verify the selected robot(s) successfully completed the task. State information associated with operation of the selected robot(s) may be monitored to verify task completion.

An online shopping concierge system identifies a set of delivery orders and a set of delivery agents associated with a location. The system allocates the orders among the agents, each agent being allocated at least one order. The system obtains agent progress data describing travel progress of the agents to the location, and order preparation progress data describing progress of preparing the orders for delivery. The system periodically updates the allocation of the orders among the agents based on the agent progress data and the order preparation progress data. This involves re-allocating at least one order to a different delivery agent. When a first agent arrives at the location, the system assigns to the first agent the orders allocated to the first agent. The system then removes the first agent from the set of available delivery agents, and removes the assigned delivery orders from the set of delivery orders.

Systems and methods for flexible conveyance in an assembly-line or manufacturing process are disclosed. A fleet of self-driving vehicles and a fleet-management system can be used to convey workpieces through a sequence of workstations at which operations are performed in order to produce a finished assembly. An assembly can be transported to a first workstation using a self-driving vehicle, where an operation is performed on the assembly. Subsequently, the assembly can be transported to a second workstation using the self-driving vehicle. The operation can be performed on the assembly while it is being conveyed by the self-driving vehicle.

A method for perception-sharing between similarly-situated vehicles that are traveling on a portion of a roadway that is equipped with an intelligent vehicle highway system is described, and includes executing, in a multi-access edge computing cluster in communication with a roadside unit disposed to monitor a roadway, an application-layer routine. The application-layer routine includes collecting real-time data associated with a plurality of objects from each of the similarly-situated vehicles, predicting motion of each of the plurality of objects based upon the real-time data, object-matching the motion of each of the plurality of objects, and executing fusion of the plurality of objects based upon the object-matching of the motion of each of the plurality of objects. Locations of the similarly-situated vehicles traveling on the roadway are identified based upon the fusion of the plurality of objects. The locations are communicated to one of the similarly-situated vehicles.

An information processing apparatus includes a controller, the controller being configured to select a first mobile body to deliver baggage to a delivery destination existing in a predetermined facility from among a plurality of types of delivery mobile bodies, wherein the controller selects the first mobile body, based on required time necessary to deliver the baggage to the delivery destination in a site of the predetermined facility, for each delivery mobile body.

According to various embodiments, a vehicle may be provided. The vehicle may include: a sensor configured to sense a marking, wherein the marking is at least substantially not visible to the human eye under a natural lighting condition; and a localization circuit configured to determine a location of the vehicle based on the sensing of the sensor.

A system for digital twinning a refuse vehicle includes a refuse vehicle, and a controller. The controller is configured to receive multiple datasets from the refuse vehicle, and generate a virtual refuse vehicle based on the multiple datasets. The virtual refuse vehicle includes a visual representation of the refuse vehicle and the multiple datasets. The controller is further configured to operate a display of a user device to provide the visual representation of the refuse vehicle and one or more of the multiple datasets to a user.

Embodiments are provided for controlling a fleet of vehicles and drones. The vehicles are directed to respectively drop off passenger groups at multiple locations. Routes are calculated routes for the vehicles to respectively pick up the passenger groups from the multiple locations based on predicted pick-up times, passenger group sizes and available vehicle capacities. One or more assign drones are assigned to each passenger group at each location. Each drone is configured to broadcast a current location of the passenger group in the location and a corresponding one of the predicted pick-up times and delay the corresponding passenger group in the location based on one of the vehicles assigned as a pick-up vehicle for the passenger group being delayed.