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 vehicle control system for causing a follower vehicle to follow a leader may have a tether system mounted to the follower vehicle. The tether system may include a tether having an end adapted to be attached to the leader, a length sensor, and an angle sensor. A path tracking system operatively associated with the tether system determines a path traveled by the leader. A path control system operatively associated with the path tracking system and the follower vehicle causes the follower vehicle to follow the path traveled by the leader. A spacing control system operatively associated with the path tracking system and the follower vehicle causes the follower vehicle to maintain a predetermined spacing between the follower vehicle and the leader.

Abstract: A vehicle control system for causing a follower vehicle to follow a leader may have a tether system mounted to the follower vehicle. The tether system may include a tether having an end adapted to be attached to the leader, a length sensor, and an angle sensor. A path tracking system operatively associated with the tether system determines a path traveled by the leader. A path control system operatively associated with the path tracking system and the follower vehicle causes the follower vehicle to follow the path traveled by the leader. A spacing control system operatively associated with the path tracking system and the follower vehicle causes the follower vehicle to maintain a predetermined spacing between the follower vehicle and the leader.

Abstract: Apparatus for controlling a track on a tracked vehicle may comprise a drive system, a clutch, a brake, and a track control system to operate the clutch and the brake. A control signal generator operatively associated with the track control system produces control signals that vary at a first rate to cause the track control system to transition the clutch from an engaged state to a disengaged state and produces control signals that vary at a second rate to cause the track control system to transition the brake from an initial application state to a full application state, the second rate being less than the first rate.

Abstract: A vehicle control system for causing a follower vehicle to follow a leader may have a tether system mounted to the follower vehicle. The tether system may include a tether having an end adopted to be attached to the leader, a length sensor, and an angle sensor. A path tracking system operatively associated with the tether system determines a path traveled by the leader. A path control system operatively associated with the path tracking system and the follower vehicle causes the follower vehicle to follow the path traveled by the leader. A spacing control system operatively associated with the path tracking system and the follower vehicle causes the follower vehicle to maintain a predetermined spacing between the follower vehicle and the leader.

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.