Composite AI Data Streams for Multi-UAV Object Detection

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Solution Overview

Problem

Robotic systems equipped with multiple recording devices face challenges in efficiently processing diverse data types (e.g., video, infrared, audio, heat) due to the need for separate machine learning models, which can overwhelm computing resources and hinder real-time data processing from multiple unmanned vehicles.

Innovation Solution

A data stream processing system at a central location receives and processes multiple data streams from unmanned vehicles, determines the type of data, applies appropriate machine learning models, and generates composite frames with object detections across different data types, synchronizing and overlaying indicators for integrated display.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If separate machine learning models are used for each data type (video, infrared, audio, heat), then object detection accuracy is improved, but computing resource consumption increases and real-time processing capability deteriorates

Engineering Contradiction:

Improveobject detection accuracyVSAvoidcomputing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent implements a unified machine learning model that can process multiple data types (video, infrared, audio, heat) simultaneously. The model is designed with multi-functional capabilities to handle different sensor inputs through a single processing architecture, eliminating the need for separate dedicated models for each data type while maintaining detection accuracy across all modalities.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent combines multiple data streams from different sensors into a unified processing pipeline. By merging the input data from various sources (visual, thermal, acoustic) into a single model framework, the system achieves efficient resource utilization while preserving the ability to detect objects across all data types through integrated feature extraction and analysis.

Inventive Principle:

Principle #5Merging (Combining)

2Adaptability or versatility

If separate machine learning models are used for each data type, then specialized processing capability is improved, but system complexity increases

Engineering Contradiction:

Improvespecialized processing capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The unified machine learning model incorporates specialized processing capabilities for different data types within a single architecture. The model is designed to adaptively process video, infrared, audio, and heat data through shared layers and specialized modules, maintaining high versatility while avoiding the complexity of multiple independent systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent segments the processing architecture into modular components that can handle different data types independently within the unified model. This segmentation allows specialized feature extraction for each sensor type while maintaining a cohesive overall structure, reducing system complexity compared to fully separate models while preserving specialized processing capabilities.

Inventive Principle:

Principle #1Segmentation

3Loss of information

If multiple unmanned vehicles transmit diverse data types to a central location, then comprehensive situational awareness is improved, but data processing time increases

Engineering Contradiction:

Improvesituational awareness completenessVSAvoiddata processing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent merges data streams from multiple unmanned vehicles into a unified processing framework at the central location. By combining visual, thermal, and acoustic data from various sources into a single model execution, the system achieves comprehensive situational awareness while reducing processing time compared to sequential analysis of each data type.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The unified machine learning model enables continuous processing of diverse data types from multiple vehicles simultaneously. The system maintains uninterrupted analysis of all incoming data streams through parallel feature extraction and integrated decision-making, ensuring real-time situational awareness without the time delays associated with sequential processing.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS20250391171A1Architecture for distributed artificial intelligence augmentation with objects detected using artificial intelligence models from data received from multiple sources

Publication Date: 2025.12.25 TOMAHAWK ROBOTICS INC

AI summary

Methods and systems are described herein for generating composite data streams. A data stream processing system may receive multiple data streams from, for example, multiple unmanned vehicles and determine, based on the type of data within each data stream, a machine learning model for each data stream for processing the type of data. Each machine learning model may receive the frames of a corresponding data stream and output indications and locations of objects within those data streams. The data stream processing system may then generate a composite data stream with indications of the detected objects.