Technologies are generally described for delivery vehicles and containers for en route food product preparation. Modular food product preparation systems that may receive food items and supplies and prepare food product(s) en route such that the food product(s) is prepared by the time the system reaches a delivery destination may include trucks, railway cars, watercraft, and similar vehicles. Food preparation process steps and timing may be determined based on travel information (e.g., delivery destination, routes, etc.), as well as, food item and food product information. An on-board controller may determine the process steps and timing(s) and control operations of robotic devices arranged modularly in a container or vehicle to execute steps of the food preparation process. Alternatively or additionally, the on-board controller may receive instructions from a remote controller. Travel parameters of the vehicle may also be adjusted based on the food preparation process and/or travel information.

In described embodiments of method for executing autonomous bin picking, a physical environment comprising a bin containing a plurality of objects is perceived by one or more sensors. Multiple artificial intelligence (AI) modules feed from the sensors to compute grasping alternatives, and in some embodiments, detected objects of interest. Grasping alternatives and their attributes are computed based on the outputs of the AI modules in a high-level sensor fusion (HLSF) module. A multi-criteria decision making (MCDM) module is used to rank the grasping alternatives and select the one that maximizes the application utility while satisfying specified constraints.

Robotic arms or manipulators can be utilized to grasp inventory items within an inventory system. Information can be obtained about constraints relative to relevant elements of a process of transferring the item from place to place. Examples of such elements may include a grasping location from which an item is to be grasped, a receiving location in which a grasped item is to be placed, or a space between the grasping location and the receiving location. The information about the constraints can be used to select from multiple possible grasping options, such as by eliminating options that conflict with the constraints or preferring options that outperform others given the constraints.

A robot for delivering items within a building or within a prescribed radius of a building are provided. A method comprises receiving a task indicating a non-stationary origin and a destination; identifying a current location of the non-stationary origin by interrogating a remote computer associated with the non-stationary origin for the current location of the non-stationary origin; moving towards the current location of the non-stationary origin; determining that the non-stationary origin has changed location by interrogating the remote computer associated with the non-stationary origin for an updated current location of the non-stationary origin; predicting a next location of the non-stationary origin using an artificial intelligence prediction algorithm; determining that the robot has arrived at the origin; detecting an interaction with the robot that is associated with introducing an item to or removing an item from a storage compartment in the robot; moving towards the destination inside of the building.

Technologies are generally described for delivery vehicles and containers for en route food product preparation. Modular food product preparation systems that may receive food items and supplies and prepare food product(s) en route such that the food product(s) is prepared by the time the system reaches a delivery destination may include trucks, railway cars, watercraft, and similar vehicles. Food preparation process steps and timing may be determined based on travel information (e.g., delivery destination, routes, etc.), as well as, food item and food product information. An on-board controller may determine the process steps and timing(s) and control operations of robotic devices arranged modularly in a container or vehicle to execute steps of the food preparation process. Alternatively or additionally, the on-board controller may receive instructions from a remote controller. Travel parameters of the vehicle may also be adjusted based on the food preparation process and/or travel information.

A workpiece picking system including: a robot; a hand, attached to a hand tip portion of the robot, for picking workpieces; a three-dimensional sensor, attached to the hand tip portion, for acquiring positional information of a three-dimensional point group in a partial region in a container; a workpiece state calculation unit which calculates a position and posture of a workpiece based on positional information of a three-dimensional point group in an acquired first partial region; a data acquisition position calculation unit which calculates a robot corresponding to a second partial region where positional information is to be acquired next, based on the positional information of the three-dimensional point group in the acquired first partial region; and a control unit which controls the robot and the hand based on the calculated position and posture of the workpiece and based on the calculated robot position corresponding to the second partial region.

A robot for delivering items within a building or within a prescribed radius of a building are provided. A method comprises receiving a task indicating a non-stationary origin and a destination; identifying a current location of the non-stationary origin by interrogating a remote computer associated with the non-stationary origin for the current location of the non-stationary origin; moving towards the current location of the non-stationary origin; determining that the non-stationary origin has changed location by interrogating the remote computer associated with the non-stationary origin for an updated current location of the non-stationary origin; predicting a next location of the non-stationary origin using an artificial intelligence prediction algorithm; determining that the robot has arrived at the origin; detecting an interaction with the robot that is associated with introducing an item to or removing an item from a storage compartment in the robot; moving towards the destination inside of the building.

Robotic arms or manipulators can be utilized to grasp inventory items within an inventory system. Information can be obtained about constraints relative to relevant elements of a process of transferring the item from place to place. Examples of such elements may include a grasping location from which an item is to be grasped, a receiving location in which a grasped item is to be placed, or a space between the grasping location and the receiving location. The information about the constraints can be used to select from multiple possible grasping options, such as by eliminating options that conflict with the constraints or preferring options that outperform others given the constraints.

Robotic arms or manipulators can be utilized to grasp inventory items within an inventory system. Information can be obtained about constraints relative to relevant elements of a process of transferring the item from place to place. Examples of such elements may include a grasping location from which an item is to be grasped, a receiving location in which a grasped item is to be placed, or a space between the grasping location and the receiving location. The information about the constraints can be used to select from multiple possible grasping options, such as by eliminating options that conflict with the constraints or preferring options that outperform others given the constraints.

Robotic arms may be utilized to grasp inventory items within an inventory system. Information about an inventory item to be grasped can be detected and used to determine a grasping strategy in conjunction with information from a database. Instructions for grasping an inventory item can be generated based on the detected information and the database.