Autonomous machine having an adaptive controller

JP2025522648A5Pending Publication Date: 2026-06-08KELLY ROBIN LTD
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Patent Information

Authority / Receiving Office
JP · JP
Patent Type
Applications
Current Assignee / Owner
KELLY ROBIN LTD
Filing Date
2023-05-30
Publication Date
2026-06-08

AI Technical Summary

Technical Problem

Existing autonomous machines struggle to efficiently manage their energy state by distinguishing between internal and external power dissipation, leading to suboptimal energy usage and inefficient recovery of offline energy storage.

Method used

The autonomous machine incorporates a control system with predictive models based on a common energy basis, utilizing mutual and non-mutual information channels to distinguish between near-field and far-field interactions, and employs a common mode regulator to maintain a positive energy state through adaptive learning and feedback mechanisms.

Benefits of technology

This approach enables the machine to accurately identify and manage its energy stress, optimizing energy efficiency and recovery by distinguishing between internal and external power dissipation, thereby enhancing its operational autonomy.

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Abstract

The self-regulating machine is configured to provide an observable measurement of the mechanical energy consumed by the machine relative to the mechanical energy stored by the machine, such that the decision-making of the control system of the machine can be based on the actual energy stress of the machine. The self-regulating machine has a control system with predictive models of the internal and external environments. Both predictive models are based on the same set of information representing the common energy basis of the machine. The set of information includes (i) a plurality of interaction signals indicating the direct interactions of the machine, and (ii) a plurality of non-interaction signals indicating information available to the machine without consuming energy. The plurality of non-interaction signals includes emulated signals necessary to ensure that the predictive models of the internal and external environments are based on equivalent parameter sets.
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