Resilient multi-robot system with social learning for smart factories
AssigneeUniversity of South Florida
InventorsZixiang Nie, Kwang-Cheng Chen
Life of this patent
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Abstract
A system and methods for operating a multi-robot system (MRS) are disclosed. In some aspects, each robot of the MRS can: determine a local system regret state belief based on local evidence obtained by the robot itself and social evidence provided by other robots in a social community, determine a local system drift state belief based on the local system regret state belief, determine a next action based on the local system regret state belief and the local system drift state belief, and execute the next action. Local system regret state belief is generally an estimation of a system regret state for the MRS. Local system drift state belief is generally an estimate of a system drift state for the MRS.
Claims18
A method of operating a first robot in a multi-robot system, wherein the first robot and at least one additional robot of the multi-robot system form a social community in a network, the method comprising:
- determining, by the first robot, a local system regret state belief based on local evidence obtained by the first robot and social evidence provided by the at least one additional robot in the social community, wherein the local system regret state belief is an estimation of a system regret state for the multi-robot system;
- determining, by the first robot, a local system drift state belief based on the local system regret state belief, wherein the local system drift state belief is an estimate of a system drift state for the multi-robot system;
- generating, by the first robot, an opinion vector representing a level of trust to the at least one additional robot in the social community, wherein the opinion vector is calculated from the local system drift state belief;
- receiving, by a remote computing device, the opinion vector;
- generating, by the remote computing device, a trust matrix based at least in part on the opinion vector and at least another received opinion vector;
- responsive to detecting, by the remote computing device and based at least in part on the trust matrix, a point failure resulting in product defects, initiating a corrective action within the multi-robot system, wherein the corrective action comprises at least one of forcing a robot calibration or scheduling maintenance;
- determining, by the first robot, a next action relating to a manufacturing or fabrication task based on the local system regret state belief and the local system drift state belief;
- and
- executing, by the first robot, the next action.
A control system for a first robot in a multi-robot system, wherein the first robot and at least one additional robot of the multi-robot system form a social community in a network, the control system comprising:
- a processor;
- and
- memory having instructions stored thereon that, when executed by the processor, cause the control system to:
- determine a local system regret state belief based on local evidence obtained by the first robot and social evidence provided by the at least one additional robot in the social community, wherein the local system regret state belief is an estimation of a system regret state for the multi-robot system;
- determine a local system drift state belief based on the local system regret state belief, wherein the local system drift state belief is an estimate of a system drift state for the multi-robot system;
- generate an opinion vector representing a level of trust to the at least one additional robot in the social community, wherein the opinion vector is calculated from the local system drift state belief, and wherein the opinion vector is transmitted to a remote computing device configured to:
- (i) generate a trust matrix based at least in part on the opinion vector and at least another received opinion vector, and (ii) responsive to detecting, based at least in part on the trust matrix, a point failure resulting in product defects, initiate a corrective action within the multi-robot system, wherein the corrective action comprises at least one of forcing a robot calibration or scheduling maintenance;
- determine a next action for the first robot relating to a manufacturing or fabrication task based on the local system regret state belief and the local system drift state belief;
- and
- control the first robot to execute the next action.
A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause a device to:
- determine, for a first robot in a multi-robot system, a local system regret state belief, wherein the first robot and at least one additional robot of the multi-robot system form a social community in a network, wherein the local system regret state belief is determined based on local evidence obtained by the first robot and social evidence provided by the at least one additional robot in the social community, and wherein the local system regret state belief is an estimation of a system regret state for the multi-robot system;
- determine, for the first robot, a local system drift state belief based on the local system regret state belief, wherein the local system drift state belief is an estimate of a system drift state for the multi-robot system;
- generate, for the first robot, an opinion vector representing a level of trust to the at least one additional robot in the social community, wherein the opinion vector is calculated from the local system drift state belief, and wherein the opinion vector is transmitted to a remote computing device configured to:
- (i) generate a trust matrix based at least in part on the opinion vector and at least another received opinion vector, and (ii) responsive to detecting, based at least in part on the trust matrix, a point failure resulting in product defects, initiate a corrective action within the multi-robot system, wherein the corrective action comprises at least one of forcing a robot calibration or scheduling maintenance;
- determine a next action for the first robot relating to a manufacturing or fabrication task based on the local system regret state belief and the local system drift state belief;
- and
- control the first robot to execute the next action.
Dependencies are inferred from the claim text.