Abstract: An object induction system is disclosed for assigning handling parameters to an object. The system includes an analysis system, an association system, and an assignment system. The analysis system includes at least one characteristic perception system for providing perception data regarding an object to be processed. The association system includes an object information database and assigns association data to the object responsive to commonality with of any of the characteristic perception data with any of the characteristic recorded data. The assignment system is for assigning programmable motion device handling parameters to the indicia perception data based on the association data, and includes a workflow management system as well as a separate operational controller.

Abstract: Described is a Distributed Resource Allocation System (DRAS) for sensor control and planning. The DRAS comprises an information framework module that is configured to specify performance goals, assess current performance state, and includes sensor models to achieve the performance goals. The DRAS is configured to further allocate the sensors to achieve the performance goals. Once allocated, the DRAS then reassesses the current performance state and continues reallocating the sensors until the current performance state is most similar to the performance goals.

Abstract: According to one embodiment, a computing system includes a computing node coupled to a number of sensors. The sensors are operable to generate records from received information and transmit these records to the computing node. The computing node is operable to bind the plurality of records in a plurality of classifications using a multiple level classifier such that each classification has a differing level of specificity.

Abstract: A method and system of a sparse sampling planner uses a finite number of measurements to determine a track's expected intermediate kinematic and classification state for a specific sensor action. It uses the expected track state to compute a reward function. The expected states are further propagated for actions at the next time step to determine the next states and so on. The sampling becomes sparse and the reward function is discounted as one propagates further in time. This produces a state-action tree that is more top-heavy while providing greater accuracy at times closer to the decision point. By doing so, the planner creates a plan comprising a sequence of actions that result in the highest reward. By employing various heuristics to further prune the tree gives highly accurate results with significant savings in computational processor time.

Abstract: A method and system of a sparse sampling planner uses a finite number of measurements to determine a track's expected intermediate kinematic and classification state for a specific sensor action. It uses the expected track state to compute a reward function. The expected states are further propagated for actions at the next time step to determine the next states and so on. The sampling becomes sparse and the reward function is discounted as one propagates further in time. This produces a state-action tree that is more top-heavy while providing greater accuracy at times closer to the decision point. By doing so, the planner creates a plan comprising a sequence of actions that result in the highest reward. By employing various heuristics to further prune the tree gives highly accurate results with significant savings in computational processor time.

Abstract: According to one embodiment, a computing system includes a computing node coupled to a number of sensors. The sensors are operable to generate records from received information and transmit these records to the computing node. The computing node is operable to bind the plurality of records in a plurality of classifications using a multiple level classifier such that each classification has a differing level of specificity.