Abstract: A machine learning model, such as a Gradient Boosting Machine model, is trained using historical data associated with different types of operations. The machine learning model extracts features from the historical data and learns which features result in a predefined outcome, such as approval by a predefined entity. After the machine learning model is trained and validated as being accurate, it is used to predict the likelihood of a prospective operation achieving the predefined outcome when submitted right away versus a future date. If the likelihood of achieving the predefined outcome is low if submitted right away, the submission of the prospective operation is temporarily suspended. The machine learning model calculates a future time during which to submit the prospective operation for approval, where the future time has a greater likelihood of achieving the predefined outcome. This reduces waste of electronic resources including computer processing power and network communication bandwidth.

Abstract: A identification and planning system includes a scanner assembly, a robotic manipulator, and control server. The control server detects a first plurality of inventory items in a first inventory storage system and determines a first set of position parameters for a first inventory item from the detected first plurality of inventory items, with respect to a position of one or more image sensors in the scanner assembly. The control server generates a plurality of pick-path plans for the first inventory item, where each pick-path plan corresponds to a transformation of the determined first set of position parameters to a second set of position parameters with respect to the robotic manipulator. The control server further selects a first pick-path plan from the plurality of pick-path plans and controls the robotic manipulator to pick the first inventory item from the first inventory storage system, based on the first pick-path plan.

Abstract: A identification and planning system includes a scanner assembly, a robotic manipulator, and control server. The control server detects a first plurality of inventory items in a first inventory storage system and determines a first set of position parameters for a first inventory item from the detected first plurality of inventory items, with respect to a position of one or more image sensors in the scanner assembly. The control server generates a plurality of pick-path plans for the first inventory item, where each pick-path plan corresponds to a transformation of the determined first set of position parameters to a second set of position parameters with respect to the robotic manipulator. The control server further selects a first pick-path plan from the plurality of pick-path plans and controls the robotic manipulator to pick the first inventory item from the first inventory storage system, based on the first pick-path plan.

Abstract: A identification and planning system includes a scanner assembly, a robotic manipulator, and control server. The control server detects a first plurality of inventory items in a first inventory storage system and determines a first set of position parameters for a first inventory item from the detected first plurality of inventory items, with respect to a position of one or more image sensors in the scanner assembly. The control server generates a plurality of pick-path plans for the first inventory item, where each pick-path plan corresponds to a transformation of the determined first set of position parameters to a second set of position parameters with respect to the robotic manipulator. The control server further selects a first pick-path plan from the plurality of pick-path plans and controls the robotic manipulator to pick the first inventory item from the first inventory storage system, based on the first pick-path plan.