A transportation system controls a fleet of autonomous vehicles to implement passenger transportation and coordinate delivery of baggage or other associated items using separate vehicles. The transportation system receives passenger data and associated item data via a user interface, and determines the number and type of autonomous vehicles to transport the passengers and items from selected pick-up locations to a destination. In various implementations, the transportation system may support different pick-up locations, pick-up times and/or delivery times for the passengers and associated items. The transportation system also may determine delayed item delivery options for different delivery times and modes of transportation. Based on the passenger and item data, along with input received via the user interface, the transportation system determines the vehicles to deploy and the delivery routes, and transmits instructions to the autonomous vehicles to provide the passenger transportation and perform the item delivery.

A method includes: obtaining a batch of item group definitions, each item group definition having one or more item identifiers and corresponding quantities; for each item identifier, retrieving (i) a location of a corresponding item in a facility, and (ii) a dimensional attribute of the corresponding item; based on the locations and dimensional attributes, assigning sets of the item group definitions to respective receptacle configurations, each receptacle configuration including, for each item group definition in the set, a receptacle type with a predefined capacity; monitoring availability of a plurality of transporters in the facility, each transporter having a chassis configured to support a selectable set of receptacles; and responsive to detecting that a transporter is available, selecting one of the receptacle configurations, and presenting the selected receptacle configuration via an output device, to initiate placement of receptacles onto the chassis of the transporter according to the selected receptacle configuration.

A robot management system executes, for a plurality of transport robots, an estimation process for estimating load applied to the transport robots based on a current value during traveling and a traveling distance or a traveling time of the transport robots. The robot management system determines a transport robot to be used from among the transport robots based on an estimation result in the estimation process.

A route for a trip to a destination is generated using map information. A set of no-go roadway segments, where the vehicle is not able to drive in an autonomous mode, relevant to the route from the plurality of no-go roadway segments is identified from the map information. A local region around a current location of the vehicle is determined. A local map region including roadway segments of the map information that correspond to locations within the local region is determined. The set of the plurality of no-go roadway segments is filtered from the roadway segments of the local map region. A cost value is assigned to each roadway segment of the filtered roadway segments of the local map region. Any assigned cost values are used to determining a plan for maneuvering the vehicle for a predetermined period into the future. The vehicle is maneuvered according to the plan.

A system including a processor and memory may provide for automatically activating or deactivating a feature of a fleet vehicle. For example, one or more fleet vehicles may include one or more of a global-positioning system, a speed governor, electronically-controlled brakes, an electronically-controlled accelerator, a speed limiter, or an on-board computer with a processor and memory. One or more features may be activated by a local or remote computing device or system. For example, a system may determine one or more recommended routes between two or more locations. The system may track a fleet vehicle's progress along a route, and activate a feature of the fleet vehicle based on the fleet vehicle following or not following the recommended route. For example, the system may cause activation of a speed limiter on the fleet vehicle, disable the fleet vehicle, and/or activate or deactivate autonomous features of the fleet vehicle.

The present disclosure relates to a control device, and a control method, a program, and a mobile body that enable efficient search for surrounding information when it is in an own position indefinite state. When it is in an own position indefinite state, on the basis of an own position, obstacle position information around oneself, and information of a surface sensing possible range of a surface sensing unit including a stereo camera for determining the own position, information of a surface-sensed area of an obstacle is recorded, and a search route is planned on the basis of the information of the surface-sensed area of the obstacle. The present technology can be applied to a multi-legged robot, a flying body, and an in-vehicle system that autonomously move according to a mounted computer.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing robot plan online adjustment. A method includes receiving an initial plan for performing a particular task with a robot having a sensor. The initial plan defines an initial path having a plurality of waypoints. Each waypoint is associated with a target position and a target velocity. The method includes generating an alternative path from the initial path. Generating an alternative path includes generating a plurality of alternative paths including performing respective modifications to one or more waypoints in the initial plan, evaluating each alternative path according to a simulated total time duration required for the robot to traverse the alternative path, and selecting an alternative path having a total time duration that is less than a total time duration of the initial plan.

The present disclosure discloses a method for route optimization based on dynamic window and redundant node filtering, comprising using an existing raster map data set to determine the coordinate information of a starting position and a destination position of movement, and to mark a destination node and an obstacle node in the raster map; using A* algorithm to plan a global route; globally optimizing the global route planned by A* algorithm, and filtering redundant nodes out; combining a dynamic window algorithm to perform the local optimization section by section on the optimized global route so as to obtain a final global route. According to the present disclosure, the combination of algorithms reduces a single movement duration of a mobile robot and improves the smoothness of the movement route curve. At the same time, the problems of the robot occurring on the route during the static driving are alleviated.

An automatic truck unloading apparatus and method are provided. The automatic truck unloading apparatus generates sensing information regarding the presence or absence of a pallet on the truck by implementing sensors installed in a region of a truck and a region of a storage area, and sets optimal transport paths for multiple unmanned forklift vehicles based on the sensing information, and unloads a pallet from the truck and moves and stores the pallet in the storage area by implementing an unmanned forklift vehicle.

According to various aspects, a method of generating a collision free path of travel may include defining a global search area encompassing at least a global start position and a global target position; and determining a set of collision free trajectories by iteration, the set of collision free trajectories connecting the global start position to the global target position via one or more local target positions, each iteration including: determining a local search area within the global search area; determining, from the global obstacle map, a local obstacle map associated with the local search area; defining a local start position and one or more local target positions within the local search area; and calculating, in the local search area, a collision free trajectory from the local start position to the one or more local target positions considering the local obstacle map.