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US 2026/0310191 A1Published application
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.
Claims20
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 machine-learning model comprising a neural network using training data that includes a set of agent-task pairings 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 data 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 agent-task pairing for each one of the set of tasks by selecting an autonomous agent from among the plurality of autonomous agents based on the respective calculated reward metrics for each one of the set of tasks,
- wherein each reward metric from among the set of reward metrics corresponds to a result of selecting one of the plurality of autonomous agents to perform each respective one of the set of tasks;
- generate, by applying the trained neural network to the autonomous agent task data and the set of agent-task pairings, autonomous agent task allocation data for 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.
10 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 machine-learning model comprising a neural network using training data that includes a set of agent-task pairings 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 data 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 agent-task pairing for each one of the set of tasks by selecting an autonomous agent from among the plurality of autonomous agents based on the respective calculated reward metrics for each one of the set of tasks,
- wherein each reward metric from among the set of reward metrics corresponds to a result of selecting one of the plurality of autonomous agents to perform each respective one of the set of tasks;
- generate, by applying the trained neural network to the autonomous agent task data and the set of agent-task pairings, autonomous agent task allocation data for 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 route data to the plurality of autonomous agents to cause the plurality of autonomous agents to traverse respective routes.
9 more elements
Dependencies are inferred from the claim text.