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US 12,704,836 B2Granted
Learning-based techniques for autonomous agent task allocation
AssigneeIntel Products IP LLC
InventorsVenkataraman Natarajan, Amala Sonny
Life of this patent
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Abstract
Techniques are disclosed to perform task allocation for autonomous systems by implementing machine-learning to perform task allocation to Autonomous Mobile Robots (AMRs) in an environment. The disclosed techniques also provide for enhanced path planning and the identification of AMR health and failure prediction to further improve upon task allocation and system efficiency.
Claims24
A computing device, comprising:
- a memory configured to store computer-readable instructions;
- and
- processing circuitry configured to execute the computer-readable instructions to cause the computing device to:
- receive autonomous agent task data including (i) a set of tasks to be performed by a plurality of autonomous agents operating within an environment, and (ii) a set of task parameters identified with each respective one of the set of tasks,
- train a neural network using a training dataset that includes a set of target vectors representing an allocation of an autonomous agent to each respective one of the set of tasks based upon a set of reward metrics,
- wherein the training dataset is generated by:
- calculating, for each one of the set of tasks, a respective set of reward metrics that assigns a reward metric to each one of the plurality of autonomous agents for potentially performing the respective task by computationally determining a result of allocating each autonomous agent to the respective task based on (i) the task parameters, and (ii) cost factors associated with respective ones of the plurality of autonomous agents;
- and
- determining a respective target vector for each one of the set of tasks by allocating an autonomous agent from among the plurality of autonomous agents having the highest respective calculated reward metric for each one of the set of tasks, and
- wherein each reward metric from among the set of reward metrics corresponds to a result of allocating one of the plurality of autonomous agents to perform each respective one of the set of tasks;
- generate, by providing (i) the autonomous agent task data and the set of target vectors as inputs to an input layer of the trained neural network, and (ii) autonomous agent task allocation data as an output of an output layer of the trained neural network, the autonomous agent task allocation data representing an allocation of each one of the plurality of autonomous agents to a respective one of the set of tasks;
- and
- calculate a route for the plurality of autonomous agents to follow to perform their respectively allocated one of the set of tasks, and transmit route data to the plurality of autonomous agents to cause the plurality of autonomous agents to traverse respective routes,
- wherein the calculation of the route is performed based upon a model of the environment that is shared among the plurality of autonomous agents and comprises a plurality of cells having a predetermined size, the model of the environment including (i) a cell location of the plurality of cells that is occupied by the plurality of autonomous agents within the environment, and (ii) a mapping of one or more wireless communication metric values to respective cells of the plurality of cells based upon measurements performed by the plurality of autonomous agents in the environment as commanded by the computing device,
- wherein the model of the environment used for the calculation of the route is updated over time as the plurality of autonomous agents move among different cells within the environment, and
- wherein the route traversed by the plurality of autonomous agents comprises respective cells in the environment having one or more wireless communication metrics in excess of one or more respective predetermined thresholds.
13 more elements
An edge network computing device, comprising:
- processing circuitry configured to execute computer-readable instructions to cause the edge network computing device to:
- receive autonomous agent task data including (i) a set of tasks to be performed by a plurality of autonomous agents operating within an environment, and (ii) a set of task parameters identified with each respective one of the set of tasks,
- train a neural network using a training dataset that includes a set of target vectors representing an allocation of an autonomous agent to each respective one of the set of tasks based upon a set of reward metrics, wherein the training dataset is generated by:
- calculating, for each one of the set of tasks, a respective set of reward metrics that assigns a reward metric to each one of the plurality of autonomous agents for potentially performing the respective task by computationally determining a result of allocating each autonomous agent to the respective task based on (i) the task parameters, and (ii) cost factors associated with respective ones of the plurality of autonomous agents;
- and
- determining a respective target vector for each one of the set of tasks by allocating an autonomous agent from among the plurality of autonomous agents having the highest respective calculated reward metric for each one of the set of tasks, and
- wherein each reward metric from among the set of reward metrics corresponds to a result of allocating one of the plurality of autonomous agents to perform each respective one of the set of tasks;
- and
- generate, by providing the (i) autonomous agent task data and the set of target vectors as inputs to an input layer of the trained neural network, and (ii) autonomous agent task allocation data as an output of an output layer of the trained neural network, the autonomous agent task allocation data representing an allocation of each one of the plurality of autonomous agents to a respective one of the set of tasks;
- and
- calculate a route for the plurality of autonomous agents to follow to perform their respectively allocated one of the set of tasks;
- and
- a communication interface configured to transmit commands to each one of the plurality of autonomous agents to cause the plurality of autonomous agents to traverse their respectively calculated routes and to perform their respectively allocated tasks along their respective routes,
- wherein the calculation of the route is performed based upon a model of the environment that is shared among the plurality of autonomous agents and comprises a plurality of cells having a predetermined size, the model of the environment including (i) a cell location of the plurality of cells that is occupied by the plurality of autonomous agents within the environment, and (ii) a mapping of one or more wireless communication metric values to respective cells of the plurality of cells based upon measurements performed by the plurality of autonomous agents in the environment as commanded by the computing device,
- wherein the model of the environment used for the calculation of the route is updated over time as the plurality of autonomous agents move among different cells within the environment, and
- wherein the route traversed by the plurality of autonomous agents comprises respective cells in the environment having one or more wireless communication metrics in excess of one or more respective predetermined thresholds.
13 more elements
Dependencies are inferred from the claim text.