Distributed optimization system and method
First Claim
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1. A method for controlling a plurality of agents to search an area for an objective, wherein each agent comprises a sensor, a communicator, and a cooperative controller, the method comprising on each agent:
- a) determining a reading from the sensor;
b) sharing the reading with other agents in the plurality of agents;
c) concurrently approximating the search area, according to a plurality of readings from the plurality of agents;
d) solving for a cooperative approximation, corresponding to the concurrently approximated search area;
e) updating an agent control strategy, according to the cooperative approximation and the reading; and
f) repeating steps a) through e) until an optimization condition for the objective is met.
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Abstract
A search system and method for controlling multiple agents to optimize an objective using distributed sensing and cooperative control. The search agent can be one or more physical agents, such as a robot, and can be software agents for searching cyberspace. The objective can be: chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, explosive sources, time dependent sources, time independent sources, function surfaces, maximization points, minimization points, and optimal control of a system such as a communication system, an economy, a crane, and a multi-processor computer.
49 Citations
11 Claims
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1. A method for controlling a plurality of agents to search an area for an objective, wherein each agent comprises a sensor, a communicator, and a cooperative controller, the method comprising on each agent:
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a) determining a reading from the sensor;
b) sharing the reading with other agents in the plurality of agents;
c) concurrently approximating the search area, according to a plurality of readings from the plurality of agents;
d) solving for a cooperative approximation, corresponding to the concurrently approximated search area;
e) updating an agent control strategy, according to the cooperative approximation and the reading; and
f) repeating steps a) through e) until an optimization condition for the objective is met. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
a) a location for the agent; and
b) a measurement of the search area at the location.
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3. The method of claim 1, wherein the cooperative controller comprises step memory.
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4. The method of claim 1, wherein sharing the reading comprises communicating the reading to each agent in the plurality of agents.
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5. The method of claim 1, wherein each agent further comprises an agent communication range, wherein sharing the reading comprises communicating the reading to a plurality of nearest neighbor agents in the plurality of agents within the communication range of the agent.
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6. The method of claim 1, wherein the cooperative approximation corresponds to a quadratic form according to:
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where xi and xj denote points in an N dimensional search space, and aq for every q=0, 1, . . . , ½
N(N+3) denotes unknown coefficients defining the quadratic form at time t.
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7. The method of claim 6, wherein solving for a cooperative approximation comprises solving for the unknown coefficients using a least squares approximation.
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8. The method of claim 1, wherein the objective is selected from the group consisting of:
- chemical sources, temperature sources, radiation sources, light sources, evaders, trespassers, intruders, explosive sources, and combinations thereof.
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9. The method of claim 1, wherein the objective is selected from the group consisting of:
- function surfaces, maximization points, minimization points, optimal control of a system, and combinations therof.
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10. A method for controlling a plurality of processors in a computer to cooperatively optimize an objective for a surface function, the method comprising on each processor:
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a) evaluating the surface function at a location;
b) sharing the evaluation and the location with other processors in the plurality of processors;
c) concurrently approximating the surface function, according to evaluations and locations from the plurality of processors;
d) solving for a cooperative approximation, corresponding to the concurrently approximated surface function;
e) updating a processor control strategy, according to the cooperative approximation and the location; and
f) repeating steps a) through e) until an optimization condition for the objective is met. - View Dependent Claims (11)
where xi and xj denote points in an N dimensional search space, and aq for every q=0, 1, . . . , ½
N(N+3) denotes unknown coefficients defining the quadratic form at time t.
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Specification
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Current AssigneeNational Technology & Engineering Solutions Of Sandia LLC (Honeywell International Inc.)
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Original AssigneeSandia Corporation (Martin Marietta Corporation)
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InventorsHurtado, John E., Dohrmann, Clark R., Robinett, Rush D. III
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Primary Examiner(s)Follansbee, John
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Assistant Examiner(s)Hartman, Jr., Ronald D
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Application NumberUS09/635,019Time in Patent Office1,040 DaysField of Search700/2, 700/4, 700/19-20, 700/28, 700/245, 700/248, 700/252, 701/23, 901/1, 901/46US Class Current700/2CPC Class CodesG05B 19/41865 characterised by job schedu...G05B 2219/33055 Holon, agent executes task ...G05B 2219/33065 Ontogenetic learning, agent...G05B 2219/33066 Phylogenetic learning, grou...G05B 2219/33068 CCP coordination cooperatio...Y02P 90/02 Total factory control, e.g....
