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US 10,228,680 B2Granted
Autonomous performance optimization in robotic assembly process
AssigneeTexas State University
InventorsHeping Chen, Hongtai Cheng
Life of this patent
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Abstract
A method for process parameter optimization in a robotic manufacturing process includes identifying, in two or more successive iterations, a system model for the robotic manufacturing process. Manufacturing process parameters are optimized based on the model identified. The process may be a robotic assembly process.
Claims15
A method for process parameter optimization in a robotic manufacturing process, comprising:
- identifying, by a computational device, in two or more successive iterations during the robotic manufacturing process, a system model for the robotic manufacturing process;
- and
- optimizing one or more manufacturing process parameters based on the model identified wherein, for at least one of the iterations, the model is identified through a Gaussian process regression—surrogated Bayesian optimization algorithm, and wherein, for at least one other of the iterations, optimization is performed and applied to the manufacturing process on-line based on the model identified through the Gaussian process regression—surrogated Bayesian optimization algorithm while the manufacturing process is being performed, wherein online parameter optimization through the Gaussian process regression—surrogated Bayesian optimization algorithm comprises adding a random variation factor to a lower confidence bound acquisition function.
A system, comprising:
- a processor;
- a memory coupled to the processor, wherein the memory comprises program instructions executable by the processor to implement:
- identifying, by a computer system, in two or more successive iterations during a robotic manufacturing process, a system model for the robotic manufacturing process;
- and
- optimizing one or more manufacturing process parameters based on the model identified, wherein, for at least one of the iterations, the model is identified through a Gaussian process regression—surrogated Bayesian optimization algorithm, wherein online parameter optimization through the Gaussian process regression—surrogated Bayesian optimization algorithm comprises adding a random variation factor to a lower confidence bound acquisition function;
- and
- wherein, for at least one other of the iterations, optimization is performed and applied to the manufacturing process on-line based on the model identified through the Gaussian process regression—surrogated Bayesian optimization algorithm while the manufacturing process is being performed.
4 more elements
A non-transitory, computer-readable storage medium comprising program instructions stored thereon, wherein the program instructions are configured to implement:
- identifying, by a computer system, in two or more successive iterations during a robotic manufacturing process, a system model for the robotic manufacturing process;
- and
- optimizing one or more manufacturing process parameters based on the model identified in the iterations;
- wherein, for at least one of the iterations, the model is identified through a Gaussian process regression—surrogated Bayesian optimization algorithm, wherein online parameter optimization through the Gaussian process regression—surrogated Bayesian optimization algorithm comprises adding a random variation factor to a lower confidence bound acquisition function;
- and
- wherein, for at least one other of the iterations, optimization is performed and applied to the manufacturing process on-line based on the model identified through the Gaussian process regression—surrogated Bayesian optimization algorithm while the manufacturing process is being performed.
3 more elements
Dependencies are inferred from the claim text.