Abstract: Vehicle dispatch system includes upper stage unit, lower stage unit and interface communication unit. The upper stage unit, configured to generate vehicle schedules, is communicatively connected to the interface communication unit. The lower stage unit, communicatively connected to the upper stage unit and the interface communication unit, has two storage units and a control unit. The first storage unit stores in a state representation multiple possible states having multiple possible actions. The control unit, which receives the schedule as a state representation, is configured to simulate states during an episode by selecting a state action and determining a reward value. The second storage unit stores the reward value and has a policy linked to one possible action for each state. The interface communication unit, operable to receive and transmit vehicle communications, is configured to access the policy and its associated action and communicate the action to a vehicle.

Abstract: Vehicle dispatch system includes upper stage unit, lower stage unit and interface communication unit. The upper stage unit, configured to generate vehicle schedules, is communicatively connected to the interface communication unit. The lower stage unit, communicatively connected to the upper stage unit and the interface communication unit, has two storage units and a control unit. The first storage unit stores in a state representation multiple possible states having multiple possible actions. The control unit, which receives the schedule as a state representation, is configured to simulate states during an episode by selecting a state action and determining a reward value. The second storage unit stores the reward value and has a policy linked to one possible action for each state. The interface communication unit, operable to receive and transmit vehicle communications, is configured to access the policy and its associated action and communicate the action to a vehicle.

Abstract: A method for dispatching a plurality of vehicles operating in a work area among a plurality of destination locations and a plurality of source locations includes implementing linear programming that takes in an optimization function and constraints to generate an optimum schedule for optimum production, utilizing a reinforcement learning algorithm that takes in the schedule as input and cycles through possible environmental states that could occur within the schedule by choosing one possible action for each possible environmental state and by observing the reward obtained by taking the action at each possible environmental state, developing a policy for each possible environmental state, and providing instructions to follow an action associated with the policy.

Abstract: A vehicle including a chassis, a drive system carrying the chassis, and a vision system carried by the chassis. The vision system having a stereo visible light camera producing a colorized 3D point cloud and a stereo long wave infrared camera producing 3D data. The vision system being configured to fuse the 3D data with the 3D point cloud thereby producing an enhanced 3D point cloud.

Abstract: Vehicle dispatch system includes upper stage unit, lower stage unit and interface communication unit. The upper stage unit, configured to generate vehicle schedules, is communicatively connected to the interface communication unit. The lower stage unit, communicatively connected to the upper stage unit and the interface communication unit, has two storage units and a control unit. The first storage unit stores in a state representation multiple possible states having multiple possible actions. The control unit, which receives the schedule as a state representation, is configured to simulate states during an episode by selecting a state action and determining a reward value. The second storage unit stores the reward value and has a policy linked to one possible action for each state. The interface communication unit, operable to receive and transmit vehicle communications, is configured to access the policy and its associated action and communicate the action to a vehicle.

Abstract: A method for dispatching a plurality of vehicles operating in a work area among a plurality of destination locations and a plurality of source locations includes implementing linear programming that takes in an optimization function and constraints to generate an optimum schedule for optimum production, utilizing a reinforcement learning algorithm that takes in the schedule as input and cycles through possible environmental states that could occur within the schedule by choosing one possible action for each possible environmental state and by observing the reward obtained by taking the action at each possible environmental state, developing a policy for each possible environmental state, and providing instructions to follow an action associated with the policy.

Abstract: A vehicle including a chassis, a drive system carrying the chassis, and a vision system carried by the chassis. The vision system having a stereo visible light camera producing a colorized 3D point cloud and a stereo long wave infrared camera producing 3D data. The vision system being configured to fuse the 3D data with the 3D point cloud thereby producing an enhanced 3D point cloud.

Abstract: A robotic control system for a vehicle having a chassis and a drive system carrying the chassis. The robotic control system including a controller configured to control the drive system. The controller being further configured to at least one of auto-load the vehicle onto a trailer, preclude tipping of the vehicle, stabilize yaw of the vehicle, simulate Ackerman steering, balance the vehicle on two wheels, retrieve an other vehicle, transfer a payload from the vehicle to the other vehicle, coupling of at least one other vehicle to the vehicle, retrieval or movement of a container using either relative sensing or absolute position referencing, profile cutting of plants, and 3D print cement.

Abstract: A method for dispatching a plurality of vehicles operating in a work area among a plurality of destination locations and a plurality of source locations includes implementing linear programming that takes in an optimization function and constraints to generate an optimum schedule for optimum production, utilizing a reinforcement learning algorithm that takes in the schedule as input and cycles through possible environmental states that could occur within the schedule by choosing one possible action for each possible environmental state and by observing the reward obtained by taking the action at each possible environmental state, developing a policy for each possible environmental state, and providing instructions to follow an action associated with the policy.

Abstract: Vehicle dispatch system includes upper stage unit, lower stage unit and interface communication unit. The upper stage unit, configured to generate vehicle schedules, is communicatively connected to the interface communication unit. The lower stage unit, communicatively connected to the upper stage unit and the interface communication unit, has two storage units and a control unit. The first storage unit stores in a state representation multiple possible states having multiple possible actions. The control unit, which receives the schedule as a state representation, is configured to simulate states during an episode by selecting a state action and determining a reward value. The second storage unit stores the reward value and has a policy linked to one possible action for each state. The interface communication unit, operable to receive and transmit vehicle communications, is configured to access the policy and its associated action and communicate the action to a vehicle.

Abstract: A system and method for dispatching a plurality of vehicles operating in a work area among a plurality of destination locations and a plurality of source locations includes implementing linear programming that takes in an optimization function and constraints to generate an optimum schedule for optimum production, utilizing a reinforcement learning algorithm that takes in the schedule as input and cycles through possible environmental states that could occur within the schedule by choosing one possible action for each possible environmental state and by observing the reward obtained by taking the action at each possible environmental state, developing a policy for each possible environmental state, and providing instructions to follow an action associated with the policy.

Abstract: A system and method for dispatching a plurality of vehicles operating in a work area among a plurality of destination locations and a plurality of source locations includes implementing linear programming that takes in an optimization function and constraints to generate an optimum schedule for optimum production, utilizing a reinforcement learning algorithm that takes in the schedule as input and cycles through possible environmental states that could occur within the schedule by choosing one possible action for each possible environmental state and by observing the reward obtained by taking the action at each possible environmental state, developing a policy for each possible environmental state, and providing instructions to follow an action associated with the policy.